Complexity & Organization

Emergence: Complexity & Organization An International Transdisciplinary Journal of Complex Social Systems

VOLUME 18, Number 2, 2016

Published and distributed by:

3810 N 188th Ave Litchfield Park, AZ 85340, USA

i

Emergence: Complexity & Organization An International Transdisciplinary Journal of Complex Social Systems

Editors-in-Chief PETER ALLEN, Complex Systems Research Centre, Cranfield University, Bedford, UK JEFFREY GOLDSTEIN, Adelphi University, Garden City, NY, US DAVID SNOWDEN, Cognitive Edge, UK Managing Editor and Production Editor KURT RICHARDSON, Emergent Publications, Litchfield Park, AZ, US Founding Editor of Emergence MICHAEL LISSACK, ISCE, Boston, MA, USA Graphic Design MARSHALL CLEMENS, Idiagram, Boston, MA, USA Subject Editors

Innovation & Networks: PIERPAOLO ANDRIANI, Durham University, UK Organizational Knowledge & Learning: ELENA ANTONACOPOULOU, University of Liverpool, UK Strategy, Leadership & Change: DOUGLAS GRIFFIN, University of Hertfordshire, UK Economics & Markets: STAN METCALFE, University of Manchester, UK Philosophy: RIKA PREISER & JANNIE HOFMEYR, Department of Philosophy, Stellenbosch University, ZAF Methodology: PEDRO SOTOLONGO, Instituto de Filosofia de La Habana, CUB MIKA AALTONEN Finland Futures Research Centre, FIN ROBERT ARTIGIANI U.S. Naval Academy, USA PAUL ATKINS Australian National University in Canberra, AUS MIN BASADUR Applied Creativity, CAN KEN BASKIN ISCE Research, USA TERRY BOSSOMAIER Charles Sturt University, AUS DANIEL R. BROOKS University of Toronto, CAN DAVID BYRNE University of Durham, UK RAY COOKSEY University of New England, AUS YSANNE CARLISLE Open University Business School, UK JERRY L. R. CHANDLER Washington Evolutionary Systems Society, USA SHAUN COFFEY Commonwealth Scientific and Industrial Research Organization, AUS LYNN CRAWFORD University of Technology, Sydney, AUS ELEODORO VENTOCILLA CUADROS DKV Group, VEN ADRIAN DALE Creatifica Associates Limited, UK ERIC B. DENT University of North Carolina, USA KEVIN DOOLEY Arizona State University, USA LEIF EDVINSSON Universal Networking Intellectual Capital, SWE GLENDA EOYANG Chaos Limited, USA MARY MARGARET EVANS Office of the Secretary of Defense, USA JOHN FERRIE Smiths Aerospace, UK WILLIAM FREDERICK University of Pittsburgh, USA HUGH GUNZ University of Toronto, CAN JIM HAZY Adelphi University, USA HEATHER WOOD ION Independent Consultant, USA

ROBERT JERVIS University of Columbia, USA ALICIA JUARRERO Prince George’s Community College, USA ROBERT KAY University of Technology, Sydney, AUS RICHARD KNOWLES The SOLiance Group, USA LEV LEVITIN Boston University, USA STEVE MAGUIRE McGill University, CAN IGOR MATUTINOVIC GfK: Center for Market Research, HRV DAN MCGRATH IBM Corporation, USA ELIZABETH MCMILLAN Open University, UK GERALD MIDGLEY University of Hull, UK EVE MITLETON-KELLY London School of Economics, UK GARETH MORGAN Imaginization Inc., CAN DT OGILVIE Rutgers Business School, USA PASCAL PEREZ Australian National University, AUS NICHOLAS C. PEROFF University of Missouri-Kansas City, USA MARIO ANTONIO RIVERA University of New Mexico, USA ENZO RULLANI Ca’ Foscari University, ITA ANDREW TAIT Idea Sciences, USA ROBERT E. ULANOWICZ University of Maryland, USA WILLARD UNCAPHER Network Emergence, USA MARIUS UNGERER USB, ZAF LIZ VARGA Cranfield School of Management, UK CAROL WEBB University of Sheffield, UK MAURICE YOLLES Liverpool John Moores University, UK RODRIGO ZEIDAN University of Nottingham, CHN

Subscription prices (2016, volume 18, 4 issues) not including postage and handling: US$1295.00 Corporate/US$795.00 Institutional/ US$99.95 Individual (complementary print on request and electronic access to all previous issues). Members of the ISSS can subscribe to this journal at a concessionary rate. They must declare that the subscription is for their own private use, it will not replace any institutional subscription, and it will not be put at the disposal of any library. Subscriptions should be sent directly to the Emergent Publications, 3810 N 188th Ave, Litchfield Pk, AZ 85340, USA, or to any subscription agent.

ii

iii

iv

VOLUME 18, Number 2, 2016

CONTENTS

Contents

Editorial: Rationality vs. irrationality........................................ vii Peter Allen

Research Papers

Paradox in organizations seen as social complex systems............................................................................... 1 Petter Braathen

Viewing WIL in business schools through a new lens: Moving to the edge of chaos with complexity theory.....28 Laura Rook & Lisa McManus

Social systems: Complex adaptive loci of cognition.........55 Marta Lenartowicz, David (Weaver) Weinbaum & Petter Braathen

A cladistics and Linnaean exploration into the Darwinian selection of favorable varieties of the ideal / textbook manufacturing species..................................................................90 Christen Rose-Anderssen, James Baldwin & Keith Ridgway

Complexity, conceptual models, and teacher decision-making research......................................................... 119 Marla Robertson & Leslie Patterson

v

CONTENTS (continued)

Classic paper

Four domains of complexity.................................................... 137 Gerald Midgley

Forum

Adjacent opportunities: Enlightened economics............ 177 Ron Schultz

Calling notices and announcements............................................................181 Featured image.....................................................................................................188

vi

Editorial: Rationality vs. irrationality


Editorial

Allen Peter Cranfield University, ENG

M

y previous editorial was about the differences in views between Plato and Aristotle, and how this difference is still of importance today. Plato thought that the objects of true knowledge inhabit another world—an abstract realm independent of time and space, accessible only to the intellect. He thought that the world we experience was a shallow, misleading façade, behind which lay a more important, more fundamental world of perfect forms and souls. But Aristotle thought that there was only one world that we could philosophize about—the world we inhabit and on which we can perform experiments. He was dismissive of Plato’s Ideal Forms. He did not believe they exist. Where Plato believed that truth could be reached only through reason, Aristotle believed that by combining experiments with rational analysis and mathematics, we could reveal universal truths. Aristotle is clearly a founding thinker of the scientific approach. As a scientist, clearly, all my sympathies were with Aristotle and his approach. I hope to gain knowledge and use it successfully. In real life then, the ‘sensible thing’ is to work out what the probable consequences of a possible action might be, based on previous experiments, and then decide on the one you think is ‘best’. Over many years I have been involved in building models on the basis that they might help the people involved to understand what factors are important for their situation, and what are the dimensions of potential interest resulting from a possible action or decision. This did help us (the modellers) greatly in surviving financially long enough to develop complex systems modelling and to learn about various chunks of ‘real’—urban development and transportation, fisheries, economic development and planning, organizational decisions, innovations and many others. Eventually it has led to ‘agent based modelling’ where the different agents and types of agent involved in an issue have individual characteristics, motivations and beliefs, and the models can explore how such a system may change over time, and potentially how the constituent individuals may change as well. This clearly links the rational external effects of the agents in interaction to their own inner layers of values and needs, which may be hidden and might cause surprises. The first success of the early complex systems models showed how the variables might, over the medium term, self-organize into different patterns either spatially or E:CO 2016 18(2): vii-xi | vii

structurally, which could radically alter the performance of the system as a whole. This was due to the non-linearities in the interactions between individual elements. The models could explore the permanent dialogue between the existing structure and the local fluctuations and deviations from average, and thus can permanently ‘test’ the stability of the existing structures. This was captured in early ‘complex systems’ models, which could generate different possible trajectories into the future, with different emergent structures and organizations, giving rise to new regimes of operation. So such models allowed the exploration of potential emergent structures, for different historical deviations from average. In this way, a range of possible futures could be imagined for cities as different possible hierarchies might emerge. Similarly, within cities, neighborhoods could take on different characteristics and activities. Fishing fleets could adopt different spatial strategies of ‘learning’ how to locate new fish stocks, and economic organizations could evolve creatively their production systems and organizational structures. In some ways the models revealed something of the possible medium term reorganizations that might occur. Such changes would not have been predicted by ‘traditional’ systems models, whose spatial or organizational structure remained constant. All this was potentially very useful in clarifying what might happen over the medium term. Looking back now I can see that, for the longer term, there remained a problem. All of these systems evolved qualitatively (new variables and mechanisms) over time, and for many of them the arrival of new technologies, new issues and problems, new possible solutions gradually overtook the models. The early urban models talked of white collar and blue collar workers, for example, and obviously the actual nature of the work involved in particular sectors changed radically. Such models therefore started off close to reality, but over time were increasingly describing changes in terms of the wrong variables and mechanisms. Evolution both in human systems and in ecosystems more generally, will evolve and change both the inputs, the variables and the outputs that are relevant. Models should be seen therefore as exploring the effects of current rationalities, allowing for deviations from average and local fluctuations that can lead to different possible collective structures and patterns emerging. Notably, though, they do not anticipate changes in the rationalities and internal beliefs of the individuals represented in the models, and the identities, values, ethics and morals of the participants. We find that our models represent a kind of scientific ‘Aristotelian process’ sitting on a ‘Platonic’ set of impenetrable inner beliefs and views which may change in unpredictable ways over time. In the end we are forced to use models to test whether the variables and mechanisms within them are still true. The real ‘use’ of such models is to monitor the system and detect when something is no longer chang-

viii | Allen


ing as forecast—and to force us to seek out what the reason might be. We can then ‘re-normalize’ our models and ‘bring them up to date’ with new, unexpected realities. It is important to realize that we can never have models that ‘predict’ correctly the future over the long term. This is because, if the agents within a model ever believed in its predictions, then some of them at least would change their behavior in order to ‘profit’ from the predictions - thus invalidating the model and its predictions! This is the reality of ‘reflexivity’ which tells us that the model, if believed, becomes part of reality itself, and affects what will actually happen. In 1987 George Soros wrote a remarkable book entitled “The Alchemy of Finance”, which made this point. But perhaps being written by a financier and not by an academic it never got the academic recognition it deserved. However, it turns out that these ideas are not just true for finance, but really are true for social systems in general. Models of economic markets, urban development and of flows of goods, services and information will all display this alchemy, whereby the ‘predicted outcome’ may affect the behavior of some individuals and thereby invalidate the assumptions of the model and its predictions. These assumptions are couched in terms of behavior that reflects the ‘preferences’ of the individuals or organizations concerned. These are most often reflected in economic terms so that prices or cost/benefits are used to drive the model. With this, and the wellknown ‘It’s the economy stupid’, we had moved to the idea that it is really only economics that matters. And that ‘learning’ was all about rational economic arguments. And then came the BREXIT (the UK referendum on staying or leaving the EU) vote. In that economics is seen as the most important basis of politics and government, it was assumed that by presenting the economic calculations about what would happen with or without BREXIT, then people would vote to remain in the EU. Despite the overwhelming evidence given by every professional organization concerned with the economics of the UK there was nevertheless a majority for leaving. This shows that people are not necessarily swayed by reason and reasonable arguments, even about economics. David Hume in the early 1700s said “Reason is, and ought only to be the slave of the passions”. In other words, it is not ‘reason’ that dictates what we love and what we hate, but rather that we use ‘reason’ to better pursue our loves and hates. The feeling of some people was to simply to give the government a good kicking, and they calculated correctly that a vote to leave the EU would be a great protest. Analysis of the voting showed that less well educated, older people tended to vote ‘leave’, and younger, educated people tended to vote to ‘stay’. The point being that the older, less educated people used the referendum to vent their feelings about successive UK governments that had allowed the gap between rich and poor to grow,

E:CO 2016 18(2): vii-xi | ix

had allowed in lots of immigrants, from both inside and outside the EU, who competed for jobs, and who also had seen the impact of ‘free trade’ international competition on manufacturing and many ordinary jobs, leading to increased pressure on their wages and their jobs. In addition, the UK has always had a rather strong nationalist streak that is massaged by the constant focus of a savage press on celebrities, and on the wonders of the UK and its amazing history. This has meant that English people on the whole do not speak foreign languages, are not very concerned by events outside the UK and simply revel in being British. All this has meant that instead of participating in European Democracy and its Parliament, the few people that bothered to vote in European elections, sent candidates like Nigel Farage there, and watched in amusement as he tried to cause mayhem and to disrupt proceedings. This reveals how the rational/irrational decision to leave the EU was based on internal feelings and emotions about identity that were not based on rational economic assessments. Of course, the reality of the ‘leave’ vote will only be felt over time - several years at least and will depend on the kind of deal that we can manage with the EU. It is totally premature to suggest that things are okay because the stock markets have not crashed. Right now, through the currency devaluation of 10% that followed the vote, the people of the UK ‘lost’ - £183 Billion which dwarfs the (false) figures quoted by the ‘leave’ campaign for what we ‘paid’ into the EU. All this goes to show how the hidden, internal thoughts and identities are crucially important in what they do. These are shaped by history and how it is depicted, by individual histories, local events and how these are portrayed to them. Clearly, although young people were in favor of staying in the EU, older people still were not. The overhanging memories and heroic part played by the UK in successive World Wars clearly still made many older people see Europe as more a source of problems than of solutions. The young had already shifted their perspective to a more positive view of Europe. Anyway, the point of this Editorial is not to argue for or against BREXIT, but to show that ‘reason’ is not as important as we think. Science can show many things through repeatable experiments, but in social systems it is not clear which experiments will be repeatable, and how to ‘capture’ the inner beast. The idea of representing human behavior by that of Homo Economicus or Rational Man is clearly erroneous when one thinks how people’s decisions will reflect their desired ‘image’, their hormones and their particular self-delusions. In a way the whole operation has highlighted the dangers of a government by plebiscite, as different individuals can use the occasion for a multitude of different purposes. Particularly when the actual consequences are not clear as nobody has detailed the different trajectories that might occur. It demonstrates the importance of a parliamentary ‘shield’ through which ideas and their posx | Allen


sible consequences are debated thoroughly. It does seem that a yes/no referendum for such a complex question was really not a sensible idea. Complexity shows us that in reality, evolved systems have multiple levels, which co-evolve, and hence which combine internal structures and beliefs with external effects and issues. In essence Emergence: Complexity and Organization is all about this amazing, creative evolutionary system which is the universe. This means that over time the actual elements and structures of a system will change—the current dictionary will have to evolve in pursuit of reality. As the first chapter of the Dao de Jing says “That of which you can speak is not the reality—The Way that can be walked is not the eternal Way”. This shows us that what constitutes ‘reason’ itself will evolve and change, and any model will need to be revised over time. In Chapter 4 of the Dao de Jing we find—“It is the underdetermined nature of the world that makes it, like a bottomless goblet, inexhaustibly capacious. The Way is empty, yet inexhaustible, like an abyss!” This phrase is absolutely marvelous and uses (it seems) some Chinese equivalent of ‘underdetermined’!!! This means that it understands that although there is some ‘causality’ in the world, it is not all-powerful, and so some things will happen which were not explicit in what went before. The world, although often running along in an unsurprising way, is also free to do something unexpected and creative. Within a temporary framework of structure and organization, new elements, characteristics and functionalities can emerge! In other words, an inexhaustible evolution will occur. In many ways complexity allows us to bring science to this ancient wisdom, and instead of proving it false, to see now we may recognize and enhance its brilliant thoughts. No model that we can build will be a fixed ‘reality’, we shall need to change the dictionary (of variables and mechanisms) over time. And for this we need to make sure that we look for changes occurring inside its structure and elements as well as within its complex evolved organized networks that constitutes its current structure. The example of BREXIT merely goes to show us that inner dimensions may well throw surprises in the way of seemingly clear rational conclusions. To misquote Hume “Rationality is not only the slave of the passions, but the passions are to an extent the slave of history”.

E:CO 2016 18(2): vii-xi | xi

Paradox in organizations seen as social complex systems


Theoretical

Petter Braathen Vrije Universiteit, BEL Mr. Braathen holds a Master of Science in Control & Electrical Engineering from Norwegian University for Science and Technology, and from University of Washington. He further holds a Master of Science in Strategy and International Business from the Norwegian School of Management. Mr. Braathen is currently completing a Ph.D. at the University of Brussels, in the interdisciplinary program, focusing on complexity theory applied to strategic management and organizational theory. Mr. Braathen facilitates strategic visioning, organizational change, and leadership development for global companies in various industries.

Paradox may be the ground zero for disciplined speculation that forces individuals, organizations and societies to challenge normality and existing mental frames. Paradox can be a threat, and paradox can be a source for new insight. This paper examines how a paradox can emerge and develop in organizations. I will argue that the organization can be seen as a complex social system, and that the paradox rises as the system faces increased complexity in its environment, while equipped with an information processing architecture that reduces the complexity in an inadequate way. Following a review of classes of paradoxes: rhetorical, logical and social, the paper describes an organization as a complex social system with cognitive operations. The cognitive operations include drawing of distinctions, forming of categories, individuation of the system and the boundaries to the environment, and adaptability as a second order reorganization. The paper then discusses the dynamics and micro-foundation of how a paradox is formed based on this model. Three categories of social paradoxes: paradox of belonging, paradox of learning, and paradox of organizing, are analyzed and described as dynamic behavior in a system. The paper intends to inform a trans-disciplinary approach to describe phenomenon in organizations seen as complex social systems, and to contribute with conceptual understanding to be applied in empirical studies of paradoxical situations in organizations.

Introduction

T

he cognitive discomfort evoked by the phenomenon of a paradox provides a motivation to go back and resume business as usual, as if nothing happened. Only a few, usually philosophers at heart, like to revisit that edge, and wonder.

E:CO 2016 18(2): 1-27 | 1

These are the thinkers of which Kierkegaard wrote that the paradox is the source of their passion. We may think of the paradoxes as the atoms of philosophy, the basic point of departure for disciplined speculation. They challenge our compulsory universal beliefs; force us to see the symptoms of frailties in our reason, and to ask fundamental questions. Paradox increasingly appears in organizational studies with a growing recognition that management and organization theory need to address the phenomenon that is inherent in human beings and their social systems1,2. We need to confront the paradoxes. To seek what really happens in organizations as opposed to ‘what is supposed to happen'3. Organizational theories attempt to capture a highly complex reality with finite internally consistent statements, which essentially are incomplete4. Multiple scholars have claimed that exploration of paradoxes, with dynamic tension and balances, might move beyond oversimplified and polarized notions and stimulate development of more encompassing theories1,5,6. The approach would need to embrace complexity, diversity and ambiguity of organizational life as well as the complexity and ambiguity of the challenges in its environment. Paradoxes come in all shapes and colors, and there are multiple meanings of the concept. On one hand, we have the logical paradoxes that are clearly defined, and have been subject to thorough analysis7. Further, throughout history, we find paradoxes in the science of physics addressing real world observations. The paradoxes often lead to a crisis in the sciences, but later helped accelerate the main developments in physics, mathematics and philosophy8. We find paradox in arts and aesthetics as thought provoking rhetoric to challenge existing frames of normality. Finally, we find the social paradoxes, where people in social systems experience counter intuitive and inconsistent feedback that appears to be interrelated, and simultaneously contradictory. The paradoxical experience may lead individuals and organizations to confusion and paralysis, or it may be a source for driving change and development. The first part of the paper will discuss the three overall categories of paradoxes: rhetorical paradoxes, logical paradoxes, and social paradoxes. Even though the primary interest lies in the social paradoxes, their basis in philosophy and logic will be used as a touchstone in our analysis. There are two kinds of systems that display enough self-awareness to relate to paradox in a way described above: individual human minds, and human social systems. In this paper I will concentrate on social systems, as I am interested in understanding how organizations relate to paradoxical situations. We argue that every paradox, regardless of what disciplinary label it would normally wear, can be explained as a

2 | Braathen


‘glitch’ in cognitive operations of a complex adaptive system. The glitch happens when the system confronts areas of an increased environmental complexity, while constituted by a structure that reduces that complexity in an inadequate way. The paradoxical glitch results from the system's own cognitive architecture unable to adapt to complexity in the environment. The second part of the paper presents an integrated theory of how organizations may be seen as social complex adaptive systems with cognitive operations of observation and coding of distinctions and categories. The third part of this paper combines, our understanding of paradox, with the description of an organization as a complex adaptive system, to discuss how a paradox develops from the system's exposure to increased complexity in its environment. I will develop a micro-foundation description of how different types of social paradoxes emerge and are resolved in complex social systems. I will study three categories of social paradoxes developed by Marianne Lewis in her extensive review of organization studies of paradox6. The categories are (i) paradoxes of belonging, (ii) paradoxes of learning, and (iii) paradoxes of organizing.

What is a paradox?

T

he term ‘paradox’ stems from Greek paradoxos, the adjective of paradoxon. It means ‘contrary to expectation', a combined word of para, meaning ‘contrary', and doxa meaning ‘opinion'. The Merriam-Websters dictionary notes three meanings of the word - a paradox can be a statement that is seemingly contradictory or opposed to common sense and yet is perhaps true; a self-contradictory statement that at first seems true; or an argument that apparently derives self-contradictory conclusions by valid deduction from acceptable premises.

Rhetorical paradox The rhetorical tradition used paradoxical statements actively to awaken the interest of the reader or listener, to challenge rigid truths, to enlarge the frames of understanding, or to prepare the grounds for innovation and creativity. Zeno (490-430b.c) was a pre-Socratic philosopher, most known for his paradoxes. Even though Zeno's paradoxes were later subject to the study of both metaphysics, mathematics and logic, they were originally articulated as rhetorical paradox put forward to protect Parmenides's theory on monoism where change is impossible. His rhetorical strategy was to utilize the paradox to reduce the question of change to something absurd and rationally impossible.

E:CO 2016 18(2): 1-27 | 3

Rhetorical paradox was often intended to be amusing. In 1593 Anthony Munday writes a book ‘Defence of the Contraries’ with a collection of rhetorical paradoxes to the king where he excuses himself by saying that the book is intended ‘only as an exercise of wit, in difficult matters'9. One might imagine that the real intention was to challenge and reframe the frames for accepted ways of thinking. Even though Munday's paradoxes hardly demonstrated strict logical contradictions, they indirectly addressed the frames existing in common sense, and hence made it possible to focus and reframe accepted patterns of thinking. Writers as William Shakespeare frequently used paradox not only to play with contradictions, but also to expand, challenge, or even disintegrate the individual and social belief systems that constituted his plays10. As an example, in his play the Twelfth Night, duke Orsino exclaims, upon seeing the twins Viola and Sebastian, “one face, one voice, one habit, and two persons, / a natural perspective that is and is not!”. John Keats called Shakespeare's ability to create an ambivalence by incorporating two opposing values that are still somehow valid, a negative capability “that is when man is capable of being in uncertainties, mysteries, doubts, without any irritable reaching after fact and reason”10. During the nineteen and especially the twentieth century rhetorical paradox lost its role with the increasing development of formalism in mathematics and logic. Paradox was treated as something to be avoided and something that needed to be solved.

Logical paradox The logical tradition tries to suppress paradox to avoid ontological distinctions of being and non-being to exist at the same time. This principle of contradiction is articulated in Aristotle's Metaphysics and still stands as fundamental: a statement cannot be true and not true at the same time. It is known as the second of the so-called three classic laws of thought. Quine, a leading logician, has later classified paradoxes in three overall categories: a veridical paradox, a falsidical paradox, and an antinomy7. A veridical paradox is something that is strange but yet, surprisingly, true. Quine7 exemplifies this type with the paradox of Frederic, a protagonist of the ‘The Pirates of Penzance’ comic opera by W.S. Gilbert and A. Sullivan. Frederic is bound to be a pirate until his 21st birthday. However, being born on the 29th of February, at the age of 21 Frederic has only passed five birthdays. It turns out that he will have to be a pirate until he is in his eighties. Veridical means ‘truth-telling': even though the statement at first appears to be absurd, it defends its logic when investigated more closely. As rare

4 | Braathen


as it is, it is possible that ‘a man can be 4n years old on his nth birthday'7. A paradox of this kind may lead to a discovery of a ‘buried premise of some preconception previously reckoned as central'7, such as the premise that number of birthdays passed is a reliable indicator of someone's age. The falsidical paradox is ‘one whose proposition not only seems at first absurd, but also is false, there being a fallacy in purported proof'7. There are many famous paradoxes in this category that have challenged science, philosophy, and mathematics throughout centuries. Quine's example is the Barber's Paradox, described by Bertrand Russell in 1918: in a village there is a man, a barber, who shaves all and only those men in the village that do not shave themselves. The question that brings the paradox alive is: does that barber shave himself? We cannot say so, because he shaves all and only those men that do not shave themselves. But we get in trouble also if we say that he does not, because he shaves all and only those men that do not shave themselves. To solve this paradox we must disprove the barber or the village. There is no such barber, or there is no such village. We need to conclude that an assumption, on which the paradox rests, is false. Finally, there are antinomies. The oldest example of this type is the ancient paradox of Epimenides from Crete who said, ‘all Cretans are liars'. If he spoke the truth, he was lying, and if he was lying, he spoke the truth. Similar to the Barbers Paradox, there are loopholes to solve Epimenides’ paradox. If we reject that Epimenides were not always lying (maybe he occasionally told the truth), the paradox might be reduced to a falsidical paradox instead of an antinomy. However, in a refined and more abstract version, such as ‘this sentence is false', a falsification through reduction ad absurdum is no longer possible: this sentence is true if and only if it is false. If subjected to accepted ways of reasoning, an antinomy produces a self-contradiction. In Quine's characteristics it ‘packs a surprise that can be accommodated by nothing less than a repudiation of part of our conceptual heritage'7.

Social paradox Social paradoxes tend to be looser than the logical paradoxes. The opposing terms are not logical contradictions, but rather in the form of tensions and oppositions between incompatible positions. Further, whereas logical paradoxes exist independent of time and in abstract thought, social paradoxes are subject to temporal and spatial constraints in the physical world4. Examples can be found on a cultural level in the western society where we currently find post-ideological paradoxes as (the drive for individualization): (the need to be part of something larger than ourselves); or a paradox of E:CO 2016 18(2): 1-27 | 5

progress, i.e. (the more technology we develop to do our work): (the more technology actually controls our work and us). On an organizational level paradoxical situations emerge where individuals and groups with inherent dynamics are involved in tensions and reinforcing cycles at their very core. We try to build teams out of (individualistic) experts; trying to explore and innovate while exploiting resources to optimize; thinking globally while acting locally; fostering creativity while we increase efficiency; or trying to be in control when letting go of control seems to be working better. In one way, paradox has become a modern cliché. The term is used whenever we have dilemmas or contradictory input from our environment. However, in organization and management research, there are a number of studies that analyze paradox in organizations to extrapolate the essence of different social paradoxes. M. Lewis reviewed the research of exemplars and derived three categories of paradoxes6. First, the paradoxes of belonging, that arises from the complex relationship between the individual and the others. In particular, the tension between the evolution and expression of individuality vs. the function and individuation of the group and its boundaries. Groups become strong and sustainable only if the individuality of their members is expressed, however, individual expression potentially initiate and fuels group conflicts, which provoke a feeling of inclusion and exclusion simultaneously11. Second, the paradoxes of learning, where the organization fail to recognize dramatic changes or increased complexity in its environment, and end up in contradictory and counter-intuitive situations. The current understanding, routines and structures are self-referential, relying and building upon themselves. They are a consequence of the organization interacting with the environment over time. The learning paradox rises in the struggle between the comfort of the past and the uncertainty of the future. Third, we have the paradoxes of organizing. ‘Paradox of organizing stress conflicting yet simultaneous demands for control and flexibility…(in) organizational performance, empowerment, and formalization'6. At the very core of this paradox is the inherent tendency of selforganization in human systems vs. the underlying principle of planning and control in modern organizations. The paradox rise in the tension between planning, designed structures and control aimed at increased efficiency and precision, vs. the need for flexibility, adaptation and change on the other hand. The symptoms may appear as lack of empowerment, resistance, and confusion in the organization6.

Complex cognizing systems

I

ndividuals experience paradox due to their cognitive abilities and self-awareness. The question then becomes, do social systems equally ‘experience’ a paradox? To understand how paradox appear in social systems, and what role ‘cognitive’ pro-

6 | Braathen


cesses may play in it, we need an integrated theory of how organizations may be seen as a complex social system with a set of cognitive operations. Hence, we also need to address the question of what are the parts, interactions, and laws of interactions, which together form possible cognitive operations. In the following, I will develop such description based on existing theory of complex adaptive systems applied to social systems. I will not equal cognitive operations in a social system with the cognitive system of the human mind. However, I will argue that there are collective cognitive processes performed by human organizations that exceed the mere aggregate of the cognitive activities performed by their individuals. Further, that social systems are themselves cognitive agents in their environment with a set of cognitive operations that shows resemblance of qualities in human cognition12. I will describe the cognitive operations of a social system in three parts. First, I will start at a broader understanding of cognition that we find in both complex human and social systems—the ongoing process of sense making. In cognitive science, the enactive cognition approach regards sense making as the primary cognitive operation13. Sense making is a primary observation of phenomenon in our environment, transformed into distinctions and the relationship between them. The distinctions and relations between them will gradually evolve into richer and more complex categories. Second, the ongoing sense-making process, coding phenomenon of the environment into categories held by the system, has been argued to initiate a general individuation process of the system14,15. Over time, the ongoing individuation process will evolve into boundaries between the system and its environment. Hence, both systems may be seen as closed and self-maintained relative to their environment (Luhmann, 1996). Finally, both human and social systems are adaptive and autopoetic, seeking to maintain a function and show a ‘willingness’ to survive16. Hence, both seek to preserve invariance and coherence in their operations related to their environment. Their cognitive processes are distributed in the system, and changes in their environment will initiate changes and reorganization of the interactions.

Organizations as social complex systems The word complex stems from the latin word complectre, meaning to be entwined, twisted together, or weaved together. The word system has its origins from the Greek word systema and means a whole compounded by parts. From these two etymological definitions we understand that a complex system consists of parts that are connected so that it is difficult to separate them. The connection between the parts points to the interrelatedness of the parts. In an organization, the interrelationship implies E:CO 2016 18(2): 1-27 | 7

interaction between individuals or between an individual and some other resource17,18. An organization can be seen as a social complex system of interactions; i.e. a set of connections in the form of a stable pattern of interactions between a variety of individuals19,17,20. What connects us to other people is action of communication, and what connects us to the physical resources is the action upon them and the communicating feedback we get. When we see the organization as an emergent stable pattern of joint activity, we base our grounding ontology on (inter) action21, and we stand in the tradition of process philosophy22,23. Our model is based on change rather than on qualities of substance in the physical world. From such perspective, the organization is present in the becoming, rather than in the being. This is parallel to the ontological discussion from the ancient Greek philosophers debating whether the world is based on matter as described by the Atomists, or on change and action as promoted by Heraclites. The latter lead to process philosophy, influencing both theories of evolution25 and how complex systems of change can form stable structures24. From a complex systems perspective, the organization exists on multiple levels simultaneously. Each level integrates both constant change and stable structures. This is all dependent on the scale of perception. We can traverse through the multiple levels, from the perspective of the entire organization, to divisions, departments and teams. From close observations on any level we find constant change and patterns of (inter) actions. From a scale perspective above, the units at levels below appear as static and stable structures. From a corporate level, a department is referred to as a ‘unit’ with an input and output, however, a situated study from inside the department will report a myriad of activity and interaction between people. This is a natural property of structural hierarchies found in all complex systems26.

Boundaries of a social system On the scale below the social group, we find the individual human being as the constituent part. The human individual is in itself a stable pattern of activity on a biological level of bodily systems, and an embodied mind that we may call a psychic system. The psychic system is on a qualitatively different level of scale; i.e. of biology rather than on a social level on which we aim to describe the organization. In the same manner, interactions of Carbon and Oxygen in molecules are on a level of physical systems, and therefore left out when aiming to describe biological systems. We may therefore see the psychic system to be external to the social system itself. The psychic system becomes a micro-environment for the social system, and following the theo8 | Braathen


ries of Nicholas Luhmann, we may say that there is a loose coupling between the psychic system and the social system20. There are certainly mutual influence between the two, however, the interaction between individuals are part of the social system itself, whereas the activity in the human embodied mind is on a biological (psychological) level of scale in the structural hierarchy of reality. On the other end of the scale of a multileveled hierarchy of an organization, we find the boundaries between the macro-environment and the organization. Implicit from our definition of a social complex system, we understand that this it not a physical boundary, and does not follow the topology of formal organizational structures. In line with the work of Nicholas Luhmann, we may say that the boundary is in its essence the shared distinctions and categories held by the system. The categories are created through a process where input from the environment is interpreted by a shared set of codes into patterns of interaction inside the system. The patterns are complex sets of (inter) actions that may take on stable forms upheld by continuous interaction, or as physical or digital structures. Hence, the boundary of the system becomes a ‘cognitive membrane'27 where challenges presented by the macro or micro environment evoke patterns of internal communication that are mapped into categories held by the social system.

Distinctions and categories The challenge from the macro environment will be any type of sign28 or reactant that the system is able to (re)act with21. The (re) action constitutes the most basic type of operation whereby the system observes a difference. The difference observed through the (re) action will introduce a new state in the system. This operation leads the way for the system to draw a distinction29. The ability to draw a distinction is the fundamental cognitive operation of the social system. The distinction is the basis for all further development of categories. Distinctions are also the constituent part of the system's boundary and ‘coding function’ between phenomenon observed in its environment into internal patterns of (inter) actions. Following the theories of the mathematician Spencer Brown, we may think of distinctions as an abstract entity, a form, that separates a marked space from an imaginary unmarked space. We may say that the form has an ‘inside’ and an ‘outside'. Inside the form is the being; outside the form is the non-being. If we draw a simple form as for example a circle, we may refer to what is inside the circle (the being), and what is not inside the circle (the non-being). Similarly we may draw the distinction at the being of an object; e.g., a hammer/not a hammer. E:CO 2016 18(2): 1-27 | 9

By crossing the border between the inside and the outside, we enter the unmarked space to the outside of the form that becomes the non-being. Thereby the distinction being/non-being becomes specifiable. The inside of the form with its positive value of being, opens up for the possibility of attaching further observations and distinctions into thicker and more complex units of meaning. We may find that there are distinct different hammers (e.g., brick hammer, chasing hammer, sledgehammer), used in different contexts (e.g., chiseling bricks, shaping metal jewelry, destruction work). The inside of any distinction will open up for a possible new distinction to be made, and thereby introducing a new variety, a new state in the system. Further, the positive value of the distinction makes it possible for new connections to be made between new distinctions and actual existing distinctions. When the system ‘reenters’ the operation of observation, now holding a particular distinction, it is called recursive. A recursive process uses the results of its own operations as the basis for further operations. Hence, what is undertaken is determined in part by what has occurred in earlier operations. The recursive process will act as a confirmation of consistency, where the states of the system that has been produced, serves as criteria for the acceptance and rejection of further operations29. If the produced output of the operation is confirmed, the distinction is confirmed. The product of a (re) action may be reactant in a new (re) action, and sets of reactions interacting with each other may form stable patterns. The patterns ‘condensate’ distinctions into a higher order unit of meaning, whose verification can no longer be obtained by a single operation30. The recursive nature of the process means that the system reenters the operation under different situations, different times, and under different aspects. This leads to further enrichment and a condensed meaning. The stable structure is what in mathematics is called an eigen-value and corresponds with an attractor in the system29,31). In our example of the distinction ‘hammer', the system may recursively draw new distinctions of different hammers with poly-contextual connective value. As a complex system of distinctions and connections, they form distinctions on a different level, indicating the general identity of a hammer (e.g., tool, often with a metal head, attached to a lever). This ‘super-distinction’ is what we may call a category. A category is a stable complex system of distinctions. The specific stable connections between distinctions in a category represent a set of constraints compared to all the possible connections that are not present. We may see a category, in fact any stable system, as constraints on variety (of the distinctions)33. The variety is given by the set of (inside) distinctions, and the constraints are given by the connections between distinctions

10 | Braathen


made by the system. In our example, sledgehammer is connected to destruction, and not to jewelry, hence, there is a constraint on the actual connections versus all possible connections between the distinctions. The connection is strengthened by confirmation of the distinctions and connections in the recursive interaction between the system and environment. Hence, there is a natural complexification process with the two basic properties of evolution: variety and connection (selection). The category as an emergent super-system will in itself draw a new type of distinction on the level below. The new distinction is the set of constraints on variety indicated by the category, and is drawn in the imaginary space of all possible constraints on variety. The outside of this form still represents possibilities in an unmarked space, e.g., connection between sledgehammer and chiseling bricks. The category is drawn upon the space spanned by the connections and variety of distinctions, which are internal states created by the system itself. Hence, they do not relate to the ontological distinction of being/non-being in the physical world. The system's categories belong to a closed system ‘separated’ from the environment. When the system draws a distinction on its own imaginary space of possibilities, it creates a boundary between the system and the environment. The categories belong to the closed system of (inter) actions that we may call a social system, distinguished from both the macro and micro environment. The distinction drawn by a category in the space of constraints, is a basic operation of self-observation; i.e. the system draws a distinction on itself. Forming of a category is therefore a first order self-observation. Structural hierarchies form in complex systems26. New levels of categories as emergent super-systems will form and constrain the variety of the interconnected categories on the level below. For our simplistic example of the hammer, the initial distinction was drawn for a particular hammer in a particular context. With a recursive operation of the distinction at different times and contexts, the general category of a hammer is developed. The interrelatedness to other distinctions of tools and concrete activities may emerge into a category on the level above, for example of a trade; e.g., carpenter or blacksmith. Distinctions of identical activities and skills across trades may form an even more abstract category: competence. The categories are higher aggregated units of meaning held inside the system itself. So far, I have discussed (inter) actions evoked in the system as a consequence of challenges presented by the macro-environment. However, challenges may also be introduced from the micro-environment, as a result of activity in an individual's psychic system. From our argument, it follows that the distinction held by the individual

E:CO 2016 18(2): 1-27 | 11

is not the same as the distinction held by the social system. The output of the psychic system may result in an action (challenge), introduced into the social system, with a potential following (re) action. Just as for the challenges from the macro-environment this may initiate a process of drawing new distinctions, and confirming or rejecting actual distinctions.

Dynamic categories Dynamic categories are meta-categories of a second order with the ability to alter the meaning of the categories on the level below. The dynamic categories are a result of a complexification process where challenges in the environment introduce new variety. With additional variety, possible new connections increase exponentially. A category as a stable set of constraints on variety gives meaning for a particular environment. As the system is exposed to a more complex set of environments, different stable configurations of constraints may be possible. It may even be required in order to serve and maintain the function of the system. The same category may have slightly different configurations, and thereby meaning, dependent on the environment. The coevolution of categories will seek out new variations of constraints on variety; i.e. changing the internal structure of constraints. The variations on the defining constraints are in itself a new type of variety. A second order distinction and variety is introduced in the system. We may call this a dynamic category, defined as a constrained variation on the categories. This may for consistency be defined as a constrained variation on (the constraints on variety). From this follows that the dynamic category is a category with the ability to alter it's meaning by applying variation to the connections on the level below. The dynamic category still emerges from its sub-systems, but can by such process influence and alter its structure and, thus, meaning. This phenomenon found in complex systems is called a meta-system transition, proposed by Valentin Turchin, and further developed by Francis Heylighen,32,33. The dynamic category becomes a second order observation of the system on it self. The dynamic category can ‘observe’ the system's own categories by distinguishing between them, and by altering its own internal structure, change its meaning and identity. A new identity of a category may further have new connective value to other categories. The complexification process leads to (co) evolution of the structures of categories and to the systems ability to adapt to its environment.

12 | Braathen


Adaptability Social systems are widely accepted to be of a class called Complex Adaptive Systems (CAS)34. In essence, complex adaptive systems adapt to changes in its environment to maintain fitness. Fitness of the system may be viewed both as functional fitness and structural fitness33. Functional fitness is the ability of the system to perform a function in its environment including the ability to interact, utilize resources presented, and co-evolve with other systems. Structural fitness is the internal fitness relating to the robustness and strength of the connections between parts. The foundation behind a social system's drive to seek fitness in its environment, and to uphold its function, is debated on the basis of Maturana and Varela's concept of autopoiesis16. Even though the original concept was introduced with reference to the phenomenon of biological life from an operational and temporal perspective, sociologists like Luhmann incorporated it later in his theories of social systems. The concept has proven to be useful to studies of social systems, and in particular developing its interdisciplinary character through systems theory35. Adaptation to environment may be seen from two perspectives. First, changes in the environment may introduce new variety of (re) actions and connections inside the system. With the introduction of variety and connections the complexity evolves incrementally to counteract and compensate for perturbations from the environment. This follows Ashby's law of requisite variety, where the system needs at least the same variety of possible actions as the variety of challenges from the environment36. Second, we may see changes in the environment that are too radical, too disruptive and of a dynamic nature where the system no longer can uphold its function with its present structure. The system itself needs to change its structure of interconnected categories, to reorganize, in order to support the continuity and coherence of the whole. A real life paradox certainly may present a challenge of this magnitude. The system cannot only adapt incrementally to the situation. It must change its internal structure and architecture. New variety of actions must form new stable patterns of categories held by the system. There is always a risk that the system is unable to find a new stable state and that it might become unstable and disintegrate.

E:CO 2016 18(2): 1-27 | 13

Micoro foundations of paradox experienced in social systems

W

ith a description of an organization in terms of a social complex system, I am now ready to describe the foundations for a paradox experienced by a social system. I will utilize the three categories of social paradoxes as described above.

Paradox of belonging All distinctions inherently contain a possible paradox. As we recall, every observation draws a distinction separating the being from the non-being. As long as we relate only to the positive value of being, new operations and connections are made. Once we try to observe both sides of the distinction at once, a paradox appears. This is an entity without any connective value. Non-being and being exist at the same time. This implies that all distinctions and knowledge is founded on a paradox where the world is split into two parts - the marked and unmarked. The unity of the two parts become a paradox, but unobservable to the system. The epistemic tradition from Aristotle is based the ontology of being and nonbeing, and therefore the logical duality that follows of conjunction and disjunction. Objects in the physical world are distinguished apart from anything else. We can observe the hammer without having to refer to a screwdriver or a pair of pliers. This is the logical principle of ‘tertium non datur’—something is, or is not, and it follows that the logical domain for this distinction is two-valued37. From our description of a social system, we described how the system's distinctions and categories are a particular configuration of the system itself, and hence, a closed set of interactions to the environment. Hence, a two-value distinction of the same phenomenon drawn by two different systems, may be the same but not identical. Here I utilize the established logical distinction between sameness and identity. For example the ‘hammer’ is the same category for two observing systems, however the true identity of the hammer is contingent on the contexturality of the observing system. ‘The ultimate identity is confined by the contexturality in which it originates'37. Categories held by the system are more complex structures of sub-categories (distinctions). A more complex category is often made with reference to another category: up/down, left/right, positive/negative, hot/cold. Hence, the more complex categories include forms that do not comply with the two-value logical domain of being/ non-being. As discussed, the categories are internal forms constraining all possible

14 | Braathen


states of the system, and are distributed and inherent in the system. For distributed systems many values are possible in the operations between categories. Gotthard Günther calls these operations transjunctions. Transjunctions are neither conjunctions nor disjunctions of being, but rather relative positive/negative distinctions made on a higher level of complexity. Transjunctional operations may accept or reject a connection between categories, and hence search for other connections or other distinctions to alter it38. This may lead to social paradoxes, and in particular a paradox of belonging. The categories and transjunctional operations have a specific identity and meaning in the social system. The polycontexural nature of the individual vs the social system means that the individual develops the same category, however not with the same identity. A specific situation revealing new complexity in the environment, can waken the paradox and make visible what was before invisible because of ‘sameness’ of the distinctions drawn by the systems. We may use the example given above of Frederic in the Pirates of Penzance. The '21st birthday’ acts as a category of the social system of the pirate band; it has been formulated as an indenture of Frederic's apprenticeship. For such a social system Frederic's rare date of birth presented an opportunity - and the opportunity has been taken to attain a valuable human resource. The paradoxical situation occurs when the category '21st birthday’ held by the indenture in the pirate band ‘collides’ with the category ‘21st birthday’ held by Frederic in his psychic system. The categories are the same, however as they are confined by the contexturality in which they originate, they are not identical. The particular communication of the pirate band, the indenture, mapping a particular aspect of the band's environment, a person's age, is attempted unified with the expectation produced in another system, Frederic's mind. The expectation had its merits, being based on experience of the mind witnessing operations of other social systems using the same system of language: where '21st birthday’ normally responds to '21 years old'. From Frederic's point of view the transjunction between the two identities of the category ‘21st birthday’ must be rejected, even though the ontological question of their being would be accepted and confirmed by both systems respectively. The veridical paradoxes between individual psychic systems and collective social systems happen when the meaning-processing components of social systems remain faithful to their ‘rules of production’ of categories; however surprising to the individual psychic systems, the identity of the categories are revealed as contradictory to the ‘same’ categories held by the individual. It does not feel right to people, but it is, nev-

E:CO 2016 18(2): 1-27 | 15

ertheless, consistent with the internal logic of the systems they construct. We can only imagine that there had been interactions between Frederic and other members of the pirate band about the indenture. The pattern of communication upheld the ‘sameness’ of the categories and only continued to make the paradox invisible. The new situation required the unity of the two identities of the category ‘21st birthday', and so the paradox became visible.

Paradox of learning A different class of organizational paradox, the learning paradox, emerges when beliefs or assumptions fail to keep up with the external changes39. Beliefs and assumptions are formed through the recursive evolutionary process forming a complex system of categories, and the learning paradox emerges as a belief system is exposed to increased complexity in the environment. Learning paradoxes emerge when organizations ignore dramatic changes in their environment, and lack ‘the ability to frame new knowledge within understandings, routines, and structures that enable actors to comprehend and adjust to variations'6. The paradoxical tension reveals the need for learning, for reframing existing beliefs, and for the evolution of new sets of categories. Feedback from the environment might appear contradictory to the intended result of the action. There are a multitude of organizational studies of learning paradox6. As an example, Lenoard-Barton40, found that the more one focused on ‘core capabilities', the more it would invoke their flip side, ‘core rigidness'. The inertia of the organization's patterns of actions and routines were stronger than the ‘cognitive ability’ to adapt to a new category demanded by the environment. In another study, Philip Streatfield analyzed post merger integrations, and how existing understanding of the category ‘managing', hence ‘being in control', lead to states of ‘not being in control', whereas deliberate new strategies of ‘releasing control’ could lead to a state of ‘being in control'3. The learning paradoxes fueled selfreferential cycles with potential organizational paralysis and decline6. The first example of a learning paradox can be found 500 years b.c. when Hippasos, the follower of Phytagoras, studied geometry. The Phytagorean's belief system was based on ratios of natural numbers. They would find the ratios governing structure in nature, in possible constructions, and in musical harmonies. The confirmation of the categories was so strong that the Phytagoreans thought of ratios and natural numbers as an expression of God and was linked to a divine experience. Hippasos’ discovery showed that the sides and diagonal of a square are incommensurable; i.e. it is impossible to measure the length of the diagonal in units of the sides of the square. 16 | Braathen


By this he brought the paradox to life—stating that the ratios of natural numbers (and a true divine proof of God's existence), is impossible (and hence false) for any geometric figure with a square. Almost 2000 years passed before the paradox of Hippasos was resolved with irrational numbers, more specifically the discovery that the root of the number 2 is irrational. The paradox of Hippasos was both a falsidical paradox and a learning paradox. It was falsidical since it was based on a false assumption that all numbers must be rational. What seemed to be an antinomy paradox turned out to be falsidical by eliminating the constraints on the category of numbers and of ratios. As Quine points out—‘one man's antinomy is another man's falsidical paradox, give or take a couple of thousand years'7. Further, it was a learning paradox as the beliefs, and hence, epistemic categories had evolved inside a social system of the Phytagorean, and failed to keep up with new external discoveries. 2000 years of development makes it easy for us to see how the Phytagoreans had developed categories that were internal and ‘closed’ from reality, and that were ‘self maintained’ by their confirmation of nature through mathematics. Hippasos's findings awakened the paradox by introducing a distinction based on increased complexity observed in geometry. From our description of an organization as complex social systems, we remember that the social system was defined as a stable set of (inter) actions. The system would (re) act with challenges presented by the environment, further resulting in distinctions being drawn. Categories would emerge as stable super-systems of connected distinctions (constrained varieties of distinctions). The distinction (and category) is confirmed and strengthened through the system's recursive (inter) action with its environment, and hence, the system interprets and codes the complexity of the environment into an internal system of categories with reduced complexity. The categories (as systems) are closed and self maintained which means that they are sustained as invariant structure in spite of a constant dynamic process of (re) actions. In formal reaction theory, it is possible to show that the category as a stable set of closed and self-maintained (re) actions, correlate with eigenvalues and attractors found in complex systems41. The attractor is a stationary regime of activity where the distinctions (and their strength) and connections between them have stabilized. The attractor is a sub set of the state space (set of all possible states of a complex system) that the system will evolve towards, and an attractor is often referred to as an equilibrium point for the system. In summary, a category acts as an attractor in the social system, and as part of a dynamic equilibrium of the system.

E:CO 2016 18(2): 1-27 | 17

A complex system with an attractor can in principle be expressed as a set of formal statements (differential equations), where specific solutions are represented by functions describing the evolution of the system. As discussed above, the continuous process (complex set of reactions) upholding a category is of such nature. For illustrative purposes, I will describe the process as an interpretation function. The interpretation function takes challenges from the environment, that we will call signs (s), combined with existing categories (c) in the system, and returns a ‘state’ of recognition for the category. When the system recognizes the signs, and codes them into an existing category, the interpretation function I(s, c)=0 meaning that the category is stable and in an equilibrium. When signs from the environment changes, a new state is introduced in the system (category), the interpretation function returns a state where I(s,c)/=0. The category is not immediately recognized. When the system is out of equilibrium, we may see two alternative developments. First, new variety of distinctions is introduced as new states in the system. Some connective value of the distinctions may be selected, thus constraining the variety. This may be the start of a new category evolving as described above. Second, we may see that the system tries to stay in homeostasis where it is ‘pulled back’ into the attractor and equilibrium of the category. The new signs/challenges from the environment is not forming a new category, but rather ‘enriches’ the existing category with more complexity. This will require the interpretation function (and hence indirectly the category) to change as the system recursively faces the set of altered challenges. We may illustrate this in very simplistic terms, utilizing a phase plane method were we map the category as an attractor with the imaginary interpretation function I(s, c). The phase plane is the set of all possible states of a dynamical system and is spanned by the state variables, a set of time, and a rule for evolution. The phase plane portrait is a graphical illustration where the possible trajectories of the system are mapped. For our simplistic example with an illustrative purpose, the two dimensional state space is spanned by the interpretation function I(s, c) and the change in the interpretation function dI/dt(s, c). In our first example, the system stays in homeostasis after the system (re) act with new challenges from the environment. Initially the interpretation function will return I(s,c)/=0 as the system encounter new challenges in the interaction with its environment, represented by the point I0 (Figure 1). The system's (re) actions will start to change as a consequence and hence also the change in I(s, c). We see this as an arc moving towards the center representing the equilibrium where the challenges are interpreted to ‘match’ the category. The new challenges are ‘integrated’ into the inter-

18 | Braathen


Figure 1 Homeostasis, enriching the distinction pretation and mapping between challenge and category. The trajectory shows one potential evolution of the system towards a stable equilibrium. The equilibrium is reinstated when the interpretative coding function comes back with I(s, c)=0 and the change in categories and the interpretation function will come to a halt. If we follow the same line of reasoning, we may see how a paradox emerges (figure 2 a, b). Let's assume again that the system is exposed to new challenges from the environment that leads to a new state I0. Again the system (and the interpretation function) changes following a trajectory in the state space. Following the trajectory from I0 we see that I(s, c) changes and tries to adapt, however, unable to reach the equilibrium in I1. Rather, at point I1, we see that the interpretation of the challenges map the category in the system, however, the momentum of change is at its maximum. The system must follow a trajectory in state space and in our example below approaches I2, the other side and the ‘antithesis', of the state at I0. Since the state of I(s, c) is now at its maximum distance from the equilibrium, however in the opposite direction, the process starts again. The coding function and categories are changing gradually to the other side again.

E:CO 2016 18(2): 1-27 | 19

a b Figure 2 Paradox and spiraling dialectics We may see two potential patterns that both are well documented behavior of complex systems; i.e. a spiral trajectory back to the equilibrium, or a limit cycle that would constitute the paradox. For the spiraling trajectory, the system will gradually dampen the paradox. We find this behavior of systems in Hegelitarian dialectics where the thesis, antithesis and synthesis evolves back to an equilibrium where balance is found and where new challenges in the environment has brought new complexity and quality to the category. The equilibrium and attractor of the system has also evolved, however, not depicted in our simplistic illustration below. The other possibility is that the system remains to oscillate in a perpetual movement in a limit cycle around the attractor. The system has a paradoxical experience as the state of the ‘thesis’ consequently brings it into the state of the ‘antithesis'. Hence, the organization's own belief system, based on its own categories, appears to be inconsistent and directly contradictory. As we see, this is a time dependent evolution in the system's own state space that is closed and self maintained. The system is not experiencing any ontological paradox in the environment, but rather, is exposed to increased complexity from the environment leading to limit cycles in its own system of categories. We see how the learning paradoxes of Lenoard-Barton and Phillip Streatfield (introduced above) can be described in this model. In both examples the organization was exposed to increased complexity in the environment. The complexity of a merger between two corporations challenged the category of ‘management', ‘being in control'; in the other example, the strive for competitive advantage required a redefinition of the category ‘competence’ into a more subtle distinction of ‘core competence'. By

20 | Braathen


applying ‘management being in control’ (the existing category of control held by the organization) there was a feeling of ‘not being in control', and ‘not being in control’ (letting go of ‘control') would lead back to a notion of ‘being in control'. Similarly, the focus on ‘core competence’ would lead to ‘rigidness', contradictory to the intention to gain competitive advantage through increased flexibility of combined specialized competence. In both situations the application of existing categories to the environment of increased complexity, resulted in new states of the systems that appeared. By observing the difference, however, upholding existing patterns of (inter) actions, routines and structures, the organization arrived a contradictory state. The recursive observation of the ‘difference’ with the continuous inertia of existing patterns of (inter) action lead to a limit cycle in the system's own state space. Since, learning paradoxes are based on non-valid categories, they are by reason falsidical paradoxes in logics. Hence, the paradoxes are resolved, either by an evolution of categories to integrate increased complexity, or to reject the category. In the example of Hippasos, the category of ‘numbers’ was eventually expanded to include irrational numbers, a more complex category that could map the challenge of commensurability between the sides and the diagonal. Simultaneously, the category of ‘God’ as ‘ratios of natural numbers’ has been rejected, hence, solving the paradox. In the literature of exemplars, multiple methods of resolving learning paradox have been proposed that resonates with the understanding that might be derived from the description above.

Paradox of organizing In the last two sections we could see how the paradox of belonging correlates with the class of veridical paradoxes in logic, and that the learning paradox correlates with the falsidical paradoxes. What the paradoxes of belonging and paradoxes of learning have in common, is the foundation in the system's own internal structure of categories. It is the system's ‘epistemic’ interpretation of the environment to its own closed and self maintained system of categories that creates the paradoxical experience. For the last class of social paradoxes, the paradox of organizing, this is slightly different. The paradox stems from the very nature of two types of organizing; i.e. the self-organization, and the designed structured organization. The self-organization is based purely on (inter) actions in the social system. The individuals choose to interact to achieve their own and common interests, and the evolving system is a product of selection and fitness between them. The social ordering, and hence the organization, emerges with structural properties that channel individual activity. The individuals are E:CO 2016 18(2): 1-27 | 21

free, purposeful agents, yet the organizational social structure constrains their actions. Actions will facilitate structure; structure will facilitate actions. In a formally designed organization, the structure is constituted by power of authority, hierarchy, shared rules, and physical infrastructure as buildings and networks. Traditionally, the ontological assumptions about organizational structures, are that they are concrete objects4. However, based on fundamental action ontology they might just as well be stable structures (i.e. systems) of actions. It would for example be just as legitimate to argue that a database held in a computer system is based on (inter) actions of electrical current, referred to as bits and bytes, as it is a matter of substance. In self-organizing systems, control upholding its function and coherence is inherent and distributed in the system itself31. In a designed organizational structure, the management acts as the controller. The management will, based on the owner's intention, design and seek to enforce structures in the organization. The management's communicative action would either introduce distinctions, or enforce connections between agents, hence constraining the total potential variety;—‘the very action(s) of organizing involve the drawing of distinctions; organization itself is a source of tension'42. We end up with two types of categories (distinctions) in the system. On one hand, the categories imposed by the management, conceived in their smaller system of the management team or in the psychic system of an individual, and expressed as communication in the organization. On the other hand, categories held by the organization as a result of interaction with the environment where observations lead to patterns of communicative (inter) actions. Under stable conditions, formal organizing categories and categories of self-organization are complimentary, in equilibrium, and part of a complex structural hierarchy. For example in traditional industrial production, the core management principle was to reduce cost per unit by division and specialization of work. Complex tasks were broken down into sub-tasks by a classic Newtonian method. The self-organization would appear as ‘niches’ in the formal structure, where individuals came together to interact. Routines as stable patterns of interaction would emerge, and both formal capabilities and informal social structures would evolve. The industrial model was challenged by more complex demands from the environment. The established frame that customers want the mass-produced and cheapest product changed. Instead there was a demand for individualized products, bespoke to specific requirements, accompanied by a set of services; while still requiring

22 | Braathen


that the price remained low. We know that new organizing management principles were needed and introduced. Examples may be: modularization; flexibility and speed in reconfiguration of production lines; self organizing teams; lean production; total quality management; just in time delivery models; service as a product, etc. The development would accentuate opposing forces coexisting within the existing formal structure of the organization. The existing frame would promote efficiency and cost reduction through exploitation of resources, while the new paradigm would require additional flexibility and innovation through exploration43. The principles of lean production, TQM, and just in time delivery models, would encourage the employee's discretion and problem solving, while at the same time require new and more extensive systems of monitoring and statistical control44,6. Hence, the paradox of organizing has elements of both paradox of belonging and paradox of learning as described above. First, there is a ‘collision’ between categories from different systems similar to a paradox of belonging. The categories of management originate inside the system of management, and while communicated and agreed, the organization does not necessarily hold the same meaning in the (inter) action based self-organized system. The categories are the same, however, not identical. New challenges in the environment might awaken the differences and hence the paradox. Recently, I witnessed such paradox in a larger organization in the oil and gas industry. In the present crisis in the industry (2016), new delivery models are needed and existing methods are challenged. The category in question was ‘the management system', and whether it is a representation, and an aggregated description of how the organization work; or if it is a prescriptive process of how the organization should work. Fundamental, questions regarding the category of ‘a management system’ resulted in tension and paradoxical ‘loops’ in the argument. The previous decade of stable growth and condition in the industry had created equilibrium, where the category held by the systems was the ‘same', however not ‘identical'. Second, we also find elements of a learning paradox. The environment appears with new challenges and complexity that are observed by the system. The system is unable to process new challenges into existing categories, and enters into a limit cycle in its own internal system of categories. In our example above the category of ‘mass-produced’ correlated with an organizing principle of ‘cost efficiency, scale and repetition', whereas ‘customized’ would require ‘bespoke solutions and dedication to the individual customer'. With more complex demands from the market, the category ‘mass-produced’ evolved into ‘mass produced and customized'. The organizational actions, routines and structures would need to change to incorporate new organizational principles that could integrate ‘cost efficiency with flexibility and change'. New E:CO 2016 18(2): 1-27 | 23

and more complex systems of categories are needed that can ‘absorb’ the contradictions, and create the necessary new distinction with new connective value, enabling the system to proceed. In our example of the mass vs. customized production, such category may for example have been ‘modularity'. Modularity has enabled standardization of smaller building blocks while emphasizing the possible number of combinations that promotes the uniqueness of the whole. Hence, the paradox of organizing is the social equivalence of the antimony paradox of the logical paradoxes. It requires the system, not only to adapt its categories, however also to change its internal architecture of cognitive processing. New dimensions and degrees of freedom in the imaginary space of categories are needed.

Summary

T

he present paper has proposed to describe organizations as complex social systems with cognitive operations to understand how social paradoxes emerge. The cognitive processes are limited compared to the psychic system of the individual human being. By defining the organizations based on action ontology, we were able to utilize theory of complex systems to describe cognitive operations of the social system. We discussed how the system interacts with challenges presented by the environment, and how change in the system's state results in distinctions being drawn. Categories would emerge as stable super-systems of connected distinctions. The categories are confirmed and strengthened through the system's recursive (inter) action with its environment, so that the system interprets and codes the complexity of the environment into an internal system of categories with reduced complexity. Finally we discussed how, the categories are closed and self-maintained, sustained as invariant structure in spite of a constant dynamic process of (re) actions, and act as attractors in the system. With the model of an organization as an action-based complex social system at hand, we could study the three categories of social paradoxes: paradox of belonging, paradox of learning and paradox of organizing. Each type of social paradox was described through the integrated model of a complex social system and through reallife examples. Comparisons and parallels were drawn to the logical and philosophical paradox. We were able to see how the social paradoxes arise as the organizations encounter increased environmental complexity, while equipped with an information processing architecture, which reduces that complexity in an inadequate way. We concluded that paradox is a result of a social system's cognitive adaptation process.

24 | Braathen


The paradox will not resolve from a repetitive, more focused, or a more comprehensive processing done by the same system architecture. The system needs to alter and reorganize its set of categories to process aspects of the environment that are not being related to appropriately. This may include enrichment and altering of ‘false’ categories, or it may involve reorganization of the internal structure of the cognitive operations of the system. This will include cognitive operations by the system, on the system itself. Our hyper competitive and dynamic environment will continue to evoke paradoxical situations in organizations with equally increasing intricacy, ambiguity and diversity. Much of contemporary organizational theory is still struggling to live with, and moreover understand, paradoxes. I have argued that a trans-disciplinary approach of complex systems theory, theory of evolution, and cognitive science, may contribute to understand the dynamics of paradox in organizations. Further, I hope that practitioners may find support in theoretical distinct concepts in helping them translate principles of management and organizing into real-life organizations.

References 1. Cameron, K.S., and Quinn, R.E. (1988). “Organizational paradox and transformation,” in R.E. Quinn and K.S. Cameron (eds.), Paradox and Transformation: Toward a Theory of Change in Organization and Management, ISBN 9780887301568, pp. 1-18. 2. Smith, W. and Lewis, M. (2011). “Toward a theory of paradox: A dynamic equilibrium model of organizing,” Academy of Management Review, ISSN 1930-3807, 36(2): 381-403. 3. Streatfield, P. J. (2001). The Paradox of Control in Organizations, ISBN 9780415250313. 4. Poole, M.S., and Van de Ven, A.H. (1989). “Using paradox to build management and organization theories,” Academy of Management Review, ISSN 1930-3807, 14(4): 562-78. 5. Handy, C. (1995). The Age of Paradox, ISBN 9780875846439. 6. Lewis, M.W. (2000). “Exploring paradox: Toward a more comprehensive guide,” Academy of Management Review, ISSN 1930-3807, 25(4): 760-76. 7. Quine, W.V. (1961). “The ways of paradox,” Scientific American, ISSN 0036-8733, 206. 8. Al-Khalili, J. (2013). Paradox: The Nine Greatest Enigmas in Physics, ISBN 9780307986795. 9. Vickers, B. (1968). “King Lear and renaissance paradoxes,” The Modern Language Review, ISSN 0026-7937, 63(2): 305-14. 10. Platt, P.G. (2013). Shakespeare and the Culture of Paradox, ISBN 9781409475156. 11. Smith, K.K., and David N.B. (1987). “A paradoxical conception of group dynamics,” Human Relations, ISSN 1741-282X, 40(10): 633-57. 12. Lenartowicz, M., Weinbaum, D.R., and Braathen, P. (2016). “Social systems: Complex adaptive loci of cognition,” http://pespmc1.vub.ac.be/papers/ECCO-WorkingPapers.html. E:CO 2016 18(2): 1-27 | 25

13. Di Paolo E., Rohde M., and De Jaegher, H. (2010). Horizons For The Enactive Mind: Values, Social Interaction And Play, ISBN 9780262014601. 14. Weinbaum, D., and Viktoras, V. (2015). “Open ended intelligence: the individuation of intelligent agents,” http://pespmc1.vub.ac.be/ECCO/ECCO-papers/LenartowiczLociofCognition.pdf. 15. Simondon, G. (1992). “The genesis of the individual,” in J. Crary and S. Kwinter (eds.), Incorporations, ISBN 9780942299298, pp. 296-319. 16. Maturana, H.R., and Varela, F.J. (1991). Autopoiesis and Cognition: The Realization of the Living, ISBN 9789400989474. 17. Winter, S.G. (2013). “Habit, deliberation, and action: strengthening the microfoundations of routines and capabilities,” The Academy of Management Perspectives, ISSN 1943-4529, 27(2): 120-37. 18. Braathen, P. (2015). “Capabilities as evolution of complexity in social systems,” http:// pespmc1.vub.ac.be/papers/ECCO-WorkingPapers.html. 19. Blau, P.M. (1964). Exchange and Power in Social Life, ISBN 9780471080305. 20. Luhmann, N. (1995). Social Systems, ISBN 9780804726252. 21. Heylighen, F. and Beigi, S. (2016). “Mind outside brain: a radically non-dualist foundation for distributed cognition,” http://pespmc1.vub.ac.be/papers/Non-dualism.pdf. 22. Whitehead, A.N. (2010). Process and Reality, ISBN 9781439118368. 23. Rescher, N. (2000). Process Philosophy: A Survey of Basic Issues, ISBN 9780822961284. 24. Prigogine, I. (1984). Order Out of Chaos: Man’s New Dialogue with Nature, ISBN 9780006541158. 25. Darwin, C. (2003). The Origins of Species: 150Th Anniversary Edition, ISBN 9781592242863. 26. Simon, H.A. (1962). “The architecture of complexity,” Proceedings of the American Philosophical Society, ISSN 0003-049X, 106: 467-482. 27. Lenartowicz, M. (2015). “Mere impediments? A second thought on the role of social boundaries in self-organization of the global collective intelligence on the Earth,” Proceedings of the ISIS Summit Vienna 2015. 28. Bateson, G. (1972). Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology, ISBN 9780226039053. 29. Foerster, H.V. (1984). Observing Systems, ISBN 9780914105190. 30. Luhmann, N., and William R. (2002). Theories of Distinction: Redescribing the Descriptions of Modernity, ISBN 9780804741231. 31. Ashby, W.R. (1956). An Introduction to Cybernetics, ISBN 9781614277651. 32. Turchin, V.F. (1995). “A dialogue on metasystem transition,” World Futures: The Journal of General Evolution, ISSN 0260-4027, 45(1-4): 5-57.

26 | Braathen


33. Heylighen, F. (1995). “(Meta) systems as constraints on variation: A classification and natural history of metasystem transitions,” World Futures: Journal of General Evolution, ISSN 0260-4027, 45(1-4): 59-85. 34. McQuade, T.J., and Butos, W.N. (2009). “The Adaptive Systems Theory of Social Orders,” Studies in Emergent Order, 2: 76-108. 35. Cadenas, H., and Arnold, M. (2015). “The autopoiesis of social systems and its criticisms,” Constructivists Foundation, ISSN 1782-348X, 10(2): 169-176. 36. Ashby, W.R. (1958). “Requisite variety and its implications for the control of complex systems,” Cybernetica, ISSN 0011-4227, 1: 83-99. 37. Günther, G. (2004). “Life as polycontexturality,” Vordenker, ISSN 1619-9324, Feb. 38. Günther, G. (1967). “Time, timeless logic and self-referential systems,” Annals of the New York Academy of Sciences, ISSN 1749-6632, 138(2): 396-406. 39. Cannon, Tom. (1996). Welcome to the Revolution, ISBN 9780273620495. 40. Leonard-Barton, D. (1992). “Core capabilities and core rigidities: a paradox in managing new product development,” Strategic Management Journal, ISSN 1097-0266, 13(S1): 11125. 41. Dittrich, P., and Speroni, di F. (2007). “Chemical organization theory,” Bulletin of Mathematical Biology, ISSN 1522-9602, 69(4): 1199-1231. 42. Ford, J.D., and Robert W.B. (1988). “Organizational Change in and out of Dualities and Paradox,” in R.E. Quinn and K.S. Cameron (eds.), Paradox and Transformation: Toward a Theory of Change in Organization and Management, ISBN 9780887301568, pp. 8-121. 43. March, J.G. (1991). “Exploration and exploitation in organizational learning,” Organization Science, ISSN 1526-5455, 2(1): 71-87. 44. Eisenhardt, K., and Westcott, B.J. (1988). “Paradoxical demands and the creation of excellence: The case of just-in-time manufacturing,” in R.E. Quinn and K.S. Cameron (eds.), Paradox and Transformation: Toward a Theory of Change in Organization and Management, ISBN 9780887301568, pp. 169-194. 45. Dittrich, P., and Lars W. (2008). “Chemical organizations in a toy model of the political system,” Advances in Complex Systems, ISSN 0219-5259, 11(04): 609-27. 46. Dosi, G., Richard N., and Sidney W. (2001). The Nature and Dynamics of Organizational Capabilities, ISBN 9780199248544. 47. Heylighen, F. (1999). “The growth of structural and functional complexity during evolution,” The Evolution of Complexity, ISBN 9780792357650, pp. 17-44. 48. Heylighen, F., Shima B., and Tomas V. (2015). “Chemical organization theory as a universal modeling framework for interaction, self-organization, and autopoiesis,” http://pespmc1. vub.ac.be/Papers/COT-applicationsurvey.pdf. 49. Spencer-Brown, G. (1994). The Laws of Form, ISBN 9780963989901. 50. Turchin, V.F., and Brand F. (1977). The Phenomenon of Science, ISBN 9780231039833.

E:CO 2016 18(2): 1-27 | 27

Viewing WIL in business schools through a new lens: Moving to the edge of chaos with complexity theory

Applied

Laura Rook Darwin University, AUS Laura Rook is a Management Lecturer in the Business School at Charles Darwin University. Dr Rook currently lectures in the areas of change management, business research, workplace ethics and work-integrated learning. Her research interests are organizational learning and development, workintegrated learning, curriculum development, change management and the application of complexity theory to organizations.

Lisa McManus Charles Darwin University, AUS Lisa McManus is the Professor of Accounting and the Associate Dean of Research and Research Training for the Faculty of Law, Education, Business and Arts at Charles Darwin University. Lisa has published in a number of high quality international research journals in various areas but particularly focused on performance measurement and management.

Employers require well rounded work-ready graduates with the skills to adapt to a contemporary workplace. Australian universities are responding to these needs through the implementation of Work-integrated Learning (WIL) programs aimed at providing students with the necessary skills, knowledge and attributes employers seek. This paper describes a study of Work-integrated Learning programs in the Human Resource Management (HRM) discipline at a number of Australian business schools. Exploratory interviews were undertaken with a range of stakeholders and examined within a complexity theory lens. The findings suggest that WIL is viewed as a threat to the role of higher education rather than an opportunity. There is increased interdependence and vulnerability within universities and as universities struggle for resources to respond to uncertainties in their ecosystem, they are being forced into making short term changes rather than co-evolving with their environment. By looking at the connectedness and evolutionary properties of the universities involved in the study, a number of recommendations are suggested to encourage universities to move to the edge of chaos, where a university’s full potential can be realized. Complexity theory provides a new way for viewing the intricacies of higher education course development and provides an argument for 28 | Rook & McManus

Viewing WIL in business schools through a new lens

universities to create enabling conditions to co-evolve with the ever changing and complex world we live in.

Introduction

H

igher education in Australia is being re-shaped in order to provide work-ready graduates sought by employers1,2. Higher education institutions in Australia are currently being influenced by not only by the recent changes in Government funding policies, but also by employers and students. Australian Business Schools are under pressure to perform and provide graduates and research that closes the gap between education, research and practice in the ever changing and turbulent business environment3. Current WIL research in the business area (of which HRM is a subset) reports that business schools are not providing graduates that are ready to “hit the ground running” with the necessary skills to contribute to the workplace upon graduation4,5. As such, business schools, are developing WIL, in an effort to fill the gap in skills, work readiness and essential character development that graduates are currently lacking4,5. In these turbulent times of change WIL programs can provide “…universities with an opportunity to offer a best product that students will appreciate as a pay-off for their investment that will enhance their branding and will attract students by re-marketing of their traditional academic courses as vocationally oriented courses”6:7. WIL not only offers universities an opportunity to share knowledge and experience across disciplines7, it also offers universities a means for responding to the needs of employers and students for more work-ready relevant material to be embedded into the degrees offered. The link between WIL programs and increased employability for students is forcing universities to compete in their offerings of WIL activities. This is an opportunity for WIL to be a major differentiator for business schools to compete by developing innovative ways of attracting students by meeting both their learning and career needs8. The application of complexity theory in organizational research is a relatively new area. Complexity theory is useful in understanding complex behavior in human social systems and relevant to this study, presents a unique way of understanding the stakeholder relationships and motivations in the development of WIL programs. Principles of complexity are generic in that they are common to all natural complex systems9. However, the nature and context of the complex system needs to be taken into account when using complexity theory to analyze complex phenomenon. Applying E:CO 2016 18(2): 28-54 | 29

complexity theory to a human complex evolving system provides a unique way of viewing patterns of interaction and the relationships in each university within the context of developing WIL programs. This paper begins with a brief overview of the literature in regards to WIL. This is followed by a description of the research methodology and then the analysis is presented and discussed. Finally, in conclusion the paper identifies the implications of the research and some potential ways forward.

Work-integrated learning

P

revious research provides several different and sometimes competing understandings and definitions of the concept of WIL. McLennan and Keating10 argue that across the wide range of Australian universities, WIL is considered to be structured assessable activities that integrate theory and practice, while other researchers define WIL as a range of activities that bring together formal coursework with industry learning in a purposeful way7,11,12. The term ‘WIL’ has also been used as an all-encompassing term for different curriculum based approaches that provide “… meaningful opportunities relevant to the real world”11:13. The literature is replete with different understandings of WIL. It has been observed that there are two groups when it comes to understanding WIL. There are those authors that view WIL programs as purely work placements, that is, where students are placed in the workplace for some work experience13,14. It is argued that this view has evolved from the traditional view and application of WIL in disciplines such as sports and nursing10,15,16. There is however another widely accepted view in the literature, where WIL programs are viewed as a range of approaches and strategies embedded within the higher education curriculum7,10,12,17,11,18. The research presented in this paper aligns with this view and examines WIL programs in undergraduate HRM curriculum in Australian universities. In this study a WIL program is defined as an “umbrella term for a range of approaches and strategies that integrate theory with the practice of work within a purposefully designed curriculum”11:iv. WIL programs in this study are viewed as ranging from one subject to a number of subjects that have specific objectives, and deliberately merge theory and practice within a carefully designed curriculum. This definition was selected as it is contextually relevant to the parameters of this study concerned with the development of WIL programs in undergraduate HRM degrees. Furthermore, within the context of higher education, this definition is broad enough so that the research will remain open to the many understandings of the concept provided by the participants of this research. This is significant as throughout the 30 | Rook & McManus


participant’s interviews, the participants may refer to ‘alternative models’ of WIL. The participants are using the term ‘alternative WIL models’ to differentiate WIL activities not involving a work placement from WIL placements. Another contestable issue concerning WIL programs is that there are often several purposes or intended outcomes of a WIL program found in practice. Barrie19 states that it is through WIL programs that involve actual work experiences that students learn to become professionals. He states that to become a professional the student must extend their learning context to include “…some degree of learning in the context of actual work experiences rather than the context of the university classroom or laboratory”19:3. Pfeffer and Fong20 support this claim by stating that clinical experience provides students with practice fields where they can truly learn business. Another purported outcome of WIL programs cited in the literature is that they have the capacity to facilitate the development of student’s personal and professional skills, thus increasing their work readiness and employability. Smith and colleagues21 examined the relationship between Career Development Learning (CDL) and WIL in the curriculum. The authors found that there is a strong relationship between CDL, WIL, graduate attributes and graduate employability. More specifically, their report argues that CDL and WIL are “…educational vehicles for graduate attributes and graduate employability”21:13. This could be interpreted as CDL being a lifelong process with WIL as a stepping stone to a holistic process of reflecting on skills (graduate attributes): and meaningful work experiences to transform a career over time (employability). Additionally, 11 has stated that Australian universities are using WIL linked to outcomes (graduate attributes and employability) to differentiate in a national student centered market. This suggests that WIL aims to enhance the development of graduate attributes, leading to the positive outcome of enhanced graduate employability, and that universities are using this link and increasing the development of WIL programs in order to positively influence their student enrollments. This view of WIL was recently supported by a ‘National Strategy on Work-integrated Learning in University Education’ report which states that “WIL is aimed at improving the employability of the graduate” and that WIL is a coherent strategy that builds workforce capability, skills and individual prospects18:1. The national strategy aims to not only increase opportunities for WIL in university education but will “focus effort, and engage government and other stakeholders in developing the knowledge, skills and productive capacity of our workforce; build practical partnerships—between employers and universities; and lay the groundwork for deeper collaboration on research driven innovation and growth”18:3. The following section addresses WIL programs in the context of business schools. E:CO 2016 18(2): 28-54 | 31

WIL in business schools

T

raditionally, WIL programs have been available for students enrolled in the degrees of sport, engineering, nursing, midwifery, medicine, law and education10,16. Changes in government funding, industry and student needs, has motivated significant growth in WIL program development across disciplines more broadly, including the growth of WIL in business degrees. The development of WIL programs in business is becoming increasingly important as research has identified skills gaps in business graduates4,5,22. Industry opinion has deemed Australian business graduates as not being ‘job ready’ and lacking essential soft skills or employability skills since the 1980s22. So it is no surprise that more recent research continues to find dissatisfaction among employers with business graduates’ ability to effectively apply disciplinary knowledge, and generic skills in the workplace4,5,22,23,24. WIL programs are often aimed at providing real world experiences and thus present challenges for business schools that currently face new demands and stakeholder expectations in an already resource scarce sector11,18. An Australian study of 211 managers/supervisors of business graduates, and 156 academics teaching business units has identified that “although graduates are confident and proficient in certain nontechnical skills, they are deficient in vital elements of the managerial skill set”5:95. The skills identified as lacking in business graduates included: leadership, critical thinking, self-reflection, conflict management and decision making skills5. The impact of these skill deficiencies is the development of an “inadequate cohort of future managers, potentially devastating in the face of beleaguered economies still recovering from the global financial crisis and growing competition from the east”5:109. It is argued herein that by looking at universities as complex evolving systems, we can understand their current operational state and learn how to create enabling conditions to co-evolve with the external environment to support managers into the future.

Methodology

S

tudy participants were identified through a preliminary review of curricula on Australian university websites of undergraduate HRM programs. This aided in identifying four key stakeholder groups (academics, careers advisor, professionals and students): and nine Australian universities relevant to understanding the design and development of WIL programs in HRM undergraduate degrees within business school. The universities that agreed to participate in the research were chosen not only because they agreed to participate but because they each have some form of WIL program in their HRM related courses which is integral for comparative purposes.

32 | Rook & McManus


Both purposive and snowball sampling techniques were utilized in this study. Purposive sampling is a form of non- random sampling that involves the selection of a sample with a particular purpose in mind25. Purposive sampling was used to identify participants from each stakeholder group within the related WIL experience from within the nine relevant Australian universities. Using university websites, participants were targeted as a result of their significant characteristics making them part of a particular stakeholder group. Most of the already active participants were happy to refer someone else suitable for the study, therefore snowball sampling was also utilised. Snowball sampling is where participants involved in the study recommend or refer other people to become participants in the study26. The sampling was purposive in that the participants needed to belong to one of the identified stakeholder groups relevant to the study, and snowball because potential participants were also contacted through references from current participants. The table below presents the characteristics relevant to the identification of participants for each of the stakeholder groups. Group Title

Number of participants

Determined characteristics

Academic

Be coordinating, lecturing or tutoring in HRM relevant units which include the assessment of students Work-integrated Learning experiences.

12 Participants

Careers advisor

Participants in this group are involved in the program coordination and managing of the relationships between students, professionals, employers and the university.

8 Participants

Professional

Be involved in the process and management of undergraduate students undertaking Work- integrated Learning placements while studying a HRM related degree.

11Participants

Student

Be enrolled at university in a HRM related undergraduate degree and have experienced a form of Work-integrated learning.

9 Participants

Table 1 Participant Characteristics A total of 38 individuals participated in the study. The participants were comprised of 12 academics, 8 careers advisors, 10 professionals and 8 student stakeholder participants from nine Australian Universities. Individuals were given a choice to participate in the research or withdraw from the study at any time. Semi-structured interviews

E:CO 2016 18(2): 28-54 | 33

were conducted and all interviews were recorded and transcribed for accuracy (with permission from participants). The participating universities characteristics include membership to the Group of 8 and or Innovative Research Universities (IRU) and most ranked as 4 or 5 star in the 2015 Australian university rankings27. Included were both single and multi-campus universities with a focus on technology application and design, and creative approaches to education and research. Several of the participating universities emphasize a greater focus in their courses on local and international community and industry engagement, so as to ensure graduates are well prepared for the workplace.

Analysis of the data: Applying the principles

T

his study adopts the principles of complex evolving systems established by Mitleton-Kelly28 for applying complexity theory to organizations. The application of complexity theory to organizational research is a relatively new phenomenon. Theories of complexity can offer new ways of viewing and managing organizations. Mitleton-Kelly28 offers one way of viewing organizations where they are considered to be complex evolving systems (CES). Viewing organizations as CES requires appropriate tools for studying and analyzing them. As such 28:41 provides 10 principles that serve as “an explanatory framework that helps us understand the behaviour of a complex social (human) system”. These 10 principles are connectivity and interdependence, coevolution, far-from-equilibrium, historicity and time, space of possibilities, feedback, path-dependence, self-organisation, emergence and the creation of new order. By considering organizations as complex systems in their own right, Mitleton-Kelly’s28:43 principles are viewed as ‘transitional objects’ that “help the transition in our thinking when faced with new or difficult ideas or concepts”. The semi-structured interviews were coded in two ways. Firstly, transcripts were input into NVivo 10. They were reviewed and analyzed for patterns of similarity between participants’ dialogue and for connections with the reviewed literature relevant to the study. As the themes started to emerge, the transcript sections were highlighted and put into ‘nodes’ which act as containers for all information relating to that theme. The second phase of coding the interviews involved reviewing both the transcripts and already themed node containers for patterns congruent with complexity theory principles. Shaped around the principles of complexity, a separate list of nodes was developed. By reviewing both what was already coded in the first phase of nodes,

34 | Rook & McManus


and the transcripts individually a second time ensured that nothing was overlooked in terms of understanding the complex evolving system that was being studied.

Moving to the edge of chaos: The application of complexity theory

T

his paper presents the analysis applying four of the ten complexity principles of connectivity, far-from-equilibrium, co-evolution and space of possibilities. These four principles provide insights into the current state of the complex evolving systems of a university relative to understanding how business schools can move to the edge of chaos by creating enabling conditions. Future papers will present the results of the analysis of the remaining six complexity principles.

Context This study viewed WIL in undergraduate HRM as embedded within the complex evolving system (CES) of a university which is in turn part of a larger ecosystem. This means that there are several open systems interacting with each other, all having a degree of influence over the development of WIL in HRM degrees in Australian universities. Each individual university exists within an environment of other Australian universities with both similar and different characteristics. Figure 1 below, the model of the study, provides a diagrammatic depiction of the study of WIL in HRM within the nine universities whom participated in this study. The circle on the left hand side of the figure represents the larger social ecosystem of WIL in HRM degrees with an individual university located in its centre. Essentially, this circular figure within the model illustrates how each individual university in the Australian Higher education sector interacts and operates within its environment. Each participating university in this study is an individual system co-evolving within the larger social ecosystem. Although there are individualities that characterize each university, overall there is sufficient homogeneity in the evolution of the participating universities to propose that they are viewed as a subset of the whole. The model of the study is referred to throughout the following analysis.

Connectivity and interdependence Connectivity and interdependence must be understood for a CES to be successful29. Connectivity and interdependence in a human CES, such as a university, can be analyzed by studying the inter-connectivity of the individuals within the system, and the relatedness between the CES and its environment. E:CO 2016 18(2): 28-54 | 35

Figure 1 Model of the study: Complexity theory lens.

Inter-relatedness of individuals within a system

T

he inter-relatedness of individuals within the CES was explored by examining stakeholder (academics, students, careers advisors, and professionals) conceptualizations of work-integrated learning and the linked concepts of graduate attributes and employability. Graduate attributes and employability were identified in the literature as having an inherent link to WIL programs11,21. The four stakeholder groups exhibited several different levels of connectivity. A comparison across stakeholder groups revealed that the stakeholders are highly connected in terms of understanding and describing WIL, however their understandings are disconnected when describing graduate attributes and employability. The connectivity between the stakeholder groups discussed in this paper is concerned with the inter relatedness in regards to work-integrated learning programs. The other two elements of graduate attributes and employability will be addressed in more depth in a subsequent paper. The stakeholder participants conceptualized WIL in two ways: a broad approach to learning and teaching, and curriculum-based placements. This aligns with the definition of WIL offered in 11 and recently supported by the National WIL strategy18. 11 describes WIL programs as a range of approaches designed within the curriculum that integrate theory and the workplace. In this study the stakeholders shared an understanding as a program that occurs within the curriculum. This is in contrast to some previous research that suggests that WIL programs are embedded in the experience of work, which includes learning that occurs totally independent of studies13,14. Relevant to WIL development is the outcome of a pathway to employability which is inherent in the acquisition of a set of graduate attributes or capabilities that attempt to ensure the work readiness, and therefore employability of a student10,30. In this study

36 | Rook & McManus


it was found that stakeholder participants had a shared understanding of WIL, however, their understandings of graduate attributes and employability were vastly different. This suggests that the inherent link between WIL, graduate attributes and employability that has been identified in the literature is somewhat disconnected within the university system. This presents an issue. If there is no shared understanding between stakeholder groups of graduate attributes and employability, how can WIL courses be developed when no one can agree on what the graduate attributes and employability skills that are needed should be? This finding is in contrast to research which has found significant links between WIL, graduate attributes and employability.

Relatedness between human social systems Another component of assessing connectivity is the relatedness between human social systems28. This was explored through the relatedness between universities and the environment. There is a strong connection between the related human social systems of universities, the higher education sector, industry and the Australian government. The Australian higher education sector is managed by both national and state government policies. Therefore, universities have to adhere to specific standards in order to be eligible for funding (Higher Education Support Act 2003, amended to the Higher Education Support Amendment Act 2013) and are managed by threshold standards overseen by the Government agency, Tertiary Education Quality Standards Agency (TEQSA). This relationship between the government and Australian universities displays a high level of connectivity and interdependence between systems. This connectivity and interdependence is represented by the light blue circle in Figure 1. However, high connectivity and high interdependence do not always lead to positive outcomes. Mitleton-Kelly28 states that high connectivity and high interdependence between related systems leaves the entire system open to wider ripples of disturbance, as when one entity makes a move this affects all other related entities. For example, the Australian federal government legislation (Higher Education Support Amendment Act 2013) prompting student centered funding, along with the ambition to increase the educational attainment of the population, places emphasis on universities to find new ways to be competitive in the new consumer driven market31. This has affected universities by increasing student enrolments and by giving students more flexibility in university choice. Therefore, universities need to be more flexible and sensitive to the needs and wants of students when designing and developing their programs, as students now expect a payoff from their investment in higher education32.

E:CO 2016 18(2): 28-54 | 37

Australia’s higher education system is also highly connected with society by contributing to the future of the nation’s prosperity. This is achieved through a strong value for learning and promotion of the pursuit and transmission of knowledge, by enriching individuals so that they may maximize their potential “both in a personal sense and in terms of their capacity to make a productive contribution to society”33:1. This suggests that when one system (Higher Education sector or community) takes action, this will affect any other closely connected entity (Higher Education sector and community). The recent national WIL strategy also emphasizes this connection18:1 by declaring it is crucial that linkages between educators, enterprises, and the community are fostered in order to improve the quality and capacity of education systems and to succeed in meeting the challenges and opportunities presented by a rapidly changing global future. Viewing the Australian higher education system as a closely interconnected system that provides for the future of society, as suggested above, can have both positive and negative repercussions. For example, the reliance of society on the quality of education offered in universities, places an emphasis on the need for funding and training, as well as the assurance that universities are providing quality education. This interdependence flows on to the connection at the level of individual universities and government, as in order to contribute to the fulfillment of human and social potential, government funding and policies are needed for assessing academic quality standards. The sampled universities represented as CES in figure 1, which are part of, and participate, in the wider higher education sector, are highly connected with the Australian government and the community. The strong connection implies a high interdependence, thus making it easier for information and knowledge to flow between these related human social systems. This high connectivity is represented in the literature emphasizing a “synergistic” partnership with a strong emphasis on increasing WIL opportunities that can make students work-ready or employable11,34,35,36. However, this interdependence also causes the entire system to become vulnerable, as when one related system makes a change all other systems are affected. This vulnerability means that universities need to build connections with other relevant stakeholders such as private enterprises so that they are not entirely interdependently connected, influenced and affected by Government.

38 | Rook & McManus


Co-evolution Mitleton-Kelly28 refers to the work of Kauffman37 to describe and analyze organizations in terms of an ecosystem. Kauffman37 discussed organizations in terms of organisms that evolve or adapt with, or to, other organisms that are part of its environment. Mitleton-Kelly28:48 states that “an ecosystem is defined by the interdependence of all entities within it” and that “the notion of ecosystem applies both within the organization and to the broader environment, which includes the organization under study”. Co-evolution when applied within the social sciences generally refers to social coevolution: “the reciprocal evolution of two or more social systems or actors and more specifically, as reciprocal influence which changes the behavior of the interacting entities within a social ecosystem”38:44. Simply, co-evolution can be described as the way in which “each element influences and is influenced by all other related elements in an ecosystem”28:46. Co-evolution must be facilitated within an ecosystem so that processes and systems do not “…become legacy in a sense that they are what has been ‘left over’”29:4. In this study, co-evolution was examined at two levels: within the social system of the sampled universities (endogenous co-evolution): and in terms of the interactions and interdependencies between the universities and their wider operating environment (exogenous co-evolution). These two types of evolution do not necessarily occur separately, “as the endogenous and exogenous processes are necessarily interlinked, and the boundaries between the organization and its ‘environment’ may not be clear cut and stable”28:48. Exogenous co-evolution was examined by viewing the interactions between the university and the broader ecosystem. The Australian federal government has and continues to have, influence over the higher education sector. This influence has been in the form of new funding legislation (Higher Education Support Amendment Act 2013) and through the development and management of policies such as the Australian Qualifications Framework, the Employability Skills Framework, and the newly appointed government agency TEQSA. These policies can be viewed as drivers of change for university curriculum including the increased development of WIL activities in disciplines such as business and HRM. Ernst and Young31:6 have identified these drivers of change as the ‘democratization of knowledge and access’ and, ‘the contestability of markets and funding’ arguing that these key drivers will transform the higher education sector.

E:CO 2016 18(2): 28-54 | 39

The community and industry expectations of universities are evolving. With increased market and talent mobility, along with advances in technology, the labor and education market are becoming more competitive31. This is placing extra pressure on universities to conform to the needs and wants of the labor market in which students are now conceived as ‘paying customers’39. Australian businesses aspire to be innovative and sustainable organizations, thereby demanding graduates who have the necessary technical, and non-technical skills to understand the dynamics of the workplace and engage with the organization and its goals40. In addition to the demand for work-ready graduates, it was found that the purpose for attending university is changing. University is now considered an extension of high school, and generation Y is considering the views of their parents when deciding to attend university. Several participants support these arguments. Consequentially, industry and community expectations are influencing universities to adapt their learning and teaching practices. As Academic 2 states: If we want to get to the crux of the issue lifting the cap on uni [sic] places is why everyone’s rushing towards Work-Integrated Learning. It’s a strategy. Global financial crisis, young people and their parents are shaking in their boots, there’s now a trend back to more conservative degree selections...Therefore parents are evaluating this when they are guiding their children on their career choice.

As the higher education sector transforms, “universities will need to build significantly deeper relationships with industry…” so as to gain a competitive advantage in the now increasingly global mobile and volatile market31:11. This increase of industry based learning (WIL) is a result of the decisions of the Australian government, the Australian community and industry influence, and the strong connection between universities and the higher education sector. 5 describes this interdependent relationship between curriculum development in higher education and industry as largely reactive. They state that “industry actively dictates required graduate outcomes to universities through professional association accreditation criteria and, in Australia, the development of learning and teaching academic standards for undergraduate programs”5:109. As a result of the needs of the external environment including industry peak bodies in the higher education sector, universities are being forced to adapt their courses to new demands. Consequentially, some academics have stated that they have had to adapt their WIL courses and develop alternative WIL models. Academic 11 states: They [university] are not going to throw any resources at it. So what I have done then to be creative, I have said I cannot sustain this model the way it is running so let me think

40 | Rook & McManus


of more creative ways of having a community engagement type of activity built into the units where the community comes here instead of sending the kids out.

This strategy is in response to the increased size of student cohorts, the limited resources available, including the lack of businesses willing to host students in their organization and the need to remain competitive within the higher education market. However, alternative models of WIL have been suggested to be problematic. According to Academic 11 and 12, alternative models of WIL are less beneficial to the students learning experience. It’s a creative way of solving my numbers problem without resources, but to me it’s watering down the intent of Work-Integrated Learning, but I have had to do a work around because I am not getting resourced (Academic 11) .…there has suddenly been a massive interest in it and I think a very quick push to put things into place so I think it has good intentions but my worry is in the rush to implement things people might not be designing it to its fullest potential for the student experience (Academic 12).

Academics 11 and 12 are suggesting that some alternative WIL activities do not carry the same benefits that curriculum-based placements claim to provide. As universities are viewed as a co-evolving exogenous system, there will be implications for the development of less beneficial forms of WIL. It could be suggested that the alternative less beneficial forms of WIL that do not provide a placement or community engagement project, will affect enrollment numbers for that university as pointed out by participants in this research and supported by previous research which has stated that universities are using WIL linked to the outcomes of graduate attributes and employability to differentiate them in the higher education market11,31. For example, a combination of student centered funding systems that give increased university choice to students, and alternative ‘watered down’ subjects (Academic 11) being offered by a university may negatively affect that university’s competitiveness and attractiveness to potential students. Co-evolution can also depend on the level of connectivity and interdependence within the ecosystem. In this study, endogenous co-evolution refers to the co-evolution of individuals and groups within the CES. Through the evaluation of the connectivity and interdependence of the four stakeholder groups, it was found that there were varying perceptions of WIL in HRM. The stakeholder groups were found to be

E:CO 2016 18(2): 28-54 | 41

highly connected in terms of conceptualizing WIL and understanding its value in HRM degrees, however a level of disconnect was evident in terms of describing graduate attributes and employability. This suggests that stakeholders within the social system of the university are not co-evolving in the sense that their perceptions of WIL as a process with graduate attributes and employability exhibit a disconnection. This is problematic as co-evolution within an ecosystem must be facilitated so that processes and systems do not become legacy. The link between WIL programs, graduate attributes and employability needs to be shared because if there is no shared understanding of these linked concepts, WIL programs will fail to be developed in a way that ensures students have the opportunity to develop their repertoire of skills including graduate attributes and their employability in the workplace. Therefore, connectivity within the university needs to be fostered so that graduate attributes and employability do not become legacy in the sense that they are left over and not considered in WIL development. This is important for the process of developing WIL activities in the HRM curriculum, as it is a new endeavor and any processes for doing so should be valued as it was also identified by participants that the nature and complexities of HRM is currently undervalued and therefore gaining less attention in terms of developing the WIL curriculum. The benefit of this connectivity will be that the relationships between all stakeholders will become stronger, so teams will learn to operate more efficiently and this efficiency can be disseminated throughout the entire system, thus improving the overall performance of the larger ecosystem. Co-evolution can become a reactive process and change its emphasis from ‘coevolution with’ to ‘adaption to’ a changing environment29. When viewing change as ‘adaption to’, the CES and the environment are more likely to be viewed as separate entities, thus any strategy undertaken by the CES is a response to the changing environment29. In this study change is viewed as an adaption to the changing environment. This is because the environment and the complex evolving system are currently being viewed by the participants as separate, and therefore any change or strategy that is implemented within the university is viewed as an adaption to the external environment. Academic 2 describes below a sequence of events that led to the adaption of a ‘strategy’ of WIL in their university as a result of external changes in the environment: A colleague of mine explained it to us in the faculty the other day that if you are in the business of left handed basket weaving you are not going to have students anymore. They are going to come back to the core disciplines where they feel assured that they will achieve work. Therefore, parents are evaluating this when they are guiding their children on their career choice. The students themselves are looking around and

42 | Rook & McManus


thinking ok what lifestyle can I achieve? Most people are starting to say do I get a job at the end. When I went to uni [sic] I didn’t ask that.

Far-from-equilibrium When an organization is pushed far from its established ways of working by an external constraint (for example government policy or industry demand): it reaches a point where the organization can maximize its potential by being open to exploring new structures and ways of working represented in the model as the dotted line noted as the ‘edge of chaos’. ‘Far-from-equilibrium’ refers to the point in an open system where the system is pushed far from its stable state or established norms where new structures and order are created28. Throughout this process several distinctive properties of a complex system ensue. When the external constraint puts pressure on the system, the system spontaneously self-organizes into right or left handed ways of operating. From this chaos “the system has emerged as a higher level system with order and structure” and although the external constraint will instigate the change, the direction in which the system self-organizes is unpredictable and uncontrollable28:10. Therefore many possible solutions may arise. When the individual elements of the system interact and behave in a coherent manner, this is an example of ‘emergent behavior’. Emergent behavior occurs when micro level elements of the system interact in a coherent manner to create a new order. This is a distinctive feature of a complex system. As noted above, the emphasis of the co-evolving ecosystem of WIL programs in HRM in this study is of ‘adaption to’ the exogenous environment. Australian federal government legislation, community and industry expectations, and the competitiveness of the higher education sector, along with globalization and changes in technology, have had an impact on the types of courses being developed by universities. In this regard, WIL is the result of the university being forced on to the edge, the farfrom-equilibrium state where the constraint applied is the exogenous environment. The co-evolving external environment has increased the interest in the development of WIL in courses more broadly than the traditional disciplines in which WIL is an established part of the curriculum (i.e., nursing, engineering, midwifery and teaching)10. Fundamentally, universities are being forced to adapt to the changing external environment by finding new ways of operating. The increased pressure to develop and implement WIL into all education areas in universities is forcing the system to operate far-from-equilibrium. Mitleton-Kelly28:51 states that “when a social entity (individual, group, organization, industry, economy,

E:CO 2016 18(2): 28-54 | 43

country) is faced with a constraint, it finds new ways of operating, because far-fromequilibrium (established norms) systems are forced to experiment and explore their space of possibilities, and this exploration helps them discover and create new patterns of relationships and different structures”. It is on the edge far-from-equilibrium that self-organization and emergence can occur. A robust organization is argued to have a high degree of self-organization and is comfortable with the uncertainty which emerges from the self-organization within the organisation28 (represented as the desired state in figure 1 located on the right hand side of the edge of chaos). In essence, “it (a robust organization) can live with this type of uncertainty and does not find it threatening”29:3.

Chaotic edge thinking Through the application of the concept far-from-equilibrium in this study, Kuhn’s41 complexity metaphor ‘edge of chaos-chaotic edge thinking’ also applies. Traditionally, the edge of chaos in complexity literature is described as the point in an organization where complex self-organizing systems support organizational adjustment and development, thus viewing their environment full of potential41,42. However Kuhn, Woog and Hodgson43 have found it useful to discern an ‘edge of chaos’ attitude from ‘chaotic edge thinking’. Chaotic edge thinking describes a situation where people being at the edge of chaos can feel they are in a potentially dangerous and anxiety provoking situation41. This is the point that the CES has reached which is depicted as the area titled ‘chaotic edge thinking’ in figure 1. Chaotic edge thinking is holding the CES back from reaping the benefits of an edge of chaos perspective. The career advisors, professionals and student stakeholder groups interviewed view WIL as being “full of potential”. They were comfortable with the uncertainty within universities brought about by the increased development and implementation of WIL, thereby supporting the far-from-equilibrium state that universities are operating in. This potential was described by a careers advisor, a student and a professional as: The benefit of WIL is gaining practical experience so that it is an opportunity for them [students] to practice what they have learned in real life… but a bigger thing for me is actually to just go into the unknown, uncertainties… (Careers advisor 8) I think that you gain personal value out of it [WIL]… Just knowing that you understand the concept and you can integrate your learning. It’s not just trying to find a job all the time. It’s so much more (Student 3).The more you have been exposed to different elements, the wider your knowledge base is (Professional 8)

44 | Rook & McManus


It is clear from these above statements, participants view WIL as being full of potential in terms of the personal learning experiences gained, the ability to be comfortable with uncertainty and change, and the opportunity for exposure to many different elements for learning. This ‘full of potential’ perspective is also evidenced in the literature in relation to employability and work-ready graduates. Cooper, Orrell and Bowden17 also state that WIL programs can provide benefits including the development of proactive, adaptable and responsible individuals. Although WIL is viewed by careers advisors, students and professional groups as the force currently moving universities to operate in a space of full potential, the academic group showed characteristics of ‘chaotic edge thinking’ in their language which is stabilizing the system and not allowing the system to evolve and move forward. Academic respondents suggested that WIL is a threat to the role of the university in the higher education sector. This chaotic edge thinking has “organizations perceiving themselves as being under threat from almost any change or perturbation and behaving in ways designed to minimize the threat of catastrophe”41:60. Academic respondents identified the system (university) being in a state of change as a result of the increased development and implementation of WIL more broadly in education. Academics have significant power over the push-pull of the system at the edge of chaos as they play a direct role in developing WIL programs. For example, Academic 8 stated that: …students are there [at university] to learn how to work. They are not there just for a liberal education which might have been the rightful role of the university 40 years ago, but we have to get real. The same as we tell our customers to get real. The world has shifted.

The academic stakeholder group discussions about the role of WIL suggest that WIL is having a negative impact on the role of the university as an educational provider. As a result of the increased enactment of WIL within universities, the academic group suggested that the role of universities in the higher education sector is threatened. For example, Academics 4, 5 and 11 elaborated this and stated that: We’re becoming more work focused and that makes us look more like a training institute. I think some people may perceive that if we’re doing things like making students work-ready then we’re no longer a university, because universities are perceived as being thinking institutions. Most courses weren’t designed to help people to be work-ready (Academic 4).

E:CO 2016 18(2): 28-54 | 45

Of course this is not the tradition [WIL activities]. Education is a very conservative industry. These sorts of changes would have an impact on self-seeking organizations like universities that do not wish to change too rapidly. This is too radical for them but quite beneficial to students, quite beneficial to industry (Academic 5). I think universities have lost the reason that we were here for, we are here to be at the cutting edge of technology change or of innovations, well actually we are catching up if we are using the community to serve us, and I think that that’s the role of TAFE or when we used to have the college, colleges of advanced education, maybe that is where the vocational stuff is at, I don’t know that it really does sit at university (Academic 11).

These statements suggest a negative view of WIL in universities. Academics have also described the university landscape primarily as bureaucratic, rigid and traditional and the bureaucracy of the system as having restricted their choices for course design. They also articulated that the rigid university environment was having an impact on how they interact and communicate with industry. Therefore, not only is the university landscape affecting course design and collaboration with industry, the university landscape is influencing the teaching practices of academics. Two academics and career advisors suggested that it is also the expectations of the community and the Government in the landscape surrounding the university that is further influencing the decisions and new ways of operating. It was also noted that communities are expecting work-ready graduates and this is pushing undergraduate degrees to structure WIL programs into the curriculum against the traditionalism of academia. Universities really have to think what we are here for… once again it is [University courses design] driven by Government agenda, by the year 2020 they want 60% of students from high school to have a degree, so that means 40% wont and that is wrong, that’s not normal. So what is going to happen, we are dumbing down our degree. I have been here for 10 years and I have seen that, I have seen the students that used to come and what we have now and we are lowering our standards all the time. So whilst the published data may be 83 I see lots of transcripts with 60 on them (participant is referring to university admission scores).

It is argued that a robust organization embraces the potential in uncertainty and change28, thus the perception of WIL as a threat expressed by academics will have an effect on the actions taken when operating far-from-equilibrium. This is congruent with Rook’s44 mental model conceptualization. The negative shared understandings about the role of WIL will affect universities in that stakeholders may resist the new ways of operating and design ways of minimizing the perceived threat. This is because

46 | Rook & McManus


the established norms and ways of operating are significantly stronger and therefore the CES may remain stable and cease to explore its space of possibilities and operate far-from-equilibrium. The perception of WIL being viewed as a threat is reflected in Billet’s45:827 view that WIL in higher education is considered by educators to be the “antithesis of higher education”.

Space of possibilities In order for an organization to thrive and survive, complexity theory suggests exploring the space of possibilities by being open to trying many strategies28. Complexity theory also suggests that a single optimum strategy is neither possible nor desirable, as when the specific conditions from which that one strategy was thriving, changes, the strategy is no long optimal28. Therefore, for an organization to be sustainable it must continuously scan the landscape and try many different strategies, and consider having more than one strategy evolving simultaneously28. Having more than one strategy evolving at a time ensures that an organization will be prepared and flexible when faced with an unstable and rapidly changing environment. In addition, exploring the space of possibilities by being open to trying different strategies supports co-evolving with a changing ecosystem. This space is depicted on the right hand side of Figure 1. As complexity theory advocates that having one optimum strategy is not desirable, it is suggested here that more than one strategy will ensure that universities thrive and survive within an unstable environment, such as the current climate. Multiple WIL strategies can be achieved by considering the ‘adjacent possible’. The ‘adjacent possible’ principle considers alternate ways of doing somethings, reorganizing the already available resources in a new and novel way28. The benefit of considering the adjacent possible is that the possibilities are unlimited, because once the current adjacent possible has been realized, a new adjacent possible becomes feasible from the novel discoveries found in the former adjacent possible28. This is depicted by the infinity symbol in the study of the model. Academic 11, as quoted below, describes a situation whereby the ‘adjacent possible’ has been considered: For another subject what I have done is that we are actually going to take students into the city to go on a little tour of a Government agency and for the other 3 visits the community will come here and they will be not-for profit organizations, talking about issues and then students in groups have to solve the issue. So it’s a creative way of solving my numbers problem without being resourced.

E:CO 2016 18(2): 28-54 | 47

It is important to note here that by considering the adjacent possible one is looking at what resources already exist when considering new courses. This approach may ease some of the burden discussed in terms of resource challenges10,11,46. In essence, universities should consider using the already available resources to create new ways of providing WIL experiences that are sustainable and ensure that what is being provided is not less beneficial than a work placement. When considering the concept of space of possibilities, it is also clear that academics are using negative language in describing industry’s role in WIL and the lack of resources or support being made available. As Academic 4 states: I think there is a little bit of a general lack of commitment by organizations in general. I don’t think it’s just HR. To actually help and support universities to get students to be work-ready. That they just want to take someone from the university who has graduated but they don’t actually put anything back into the universities in terms of that help and support, the work placements, that type of thing.

This negative language used by academics to describe the role of industry professionals could be helping to maintain stability in the university in several ways. Firstly, stability is maintained through a restriction on the range of WIL models being developed because if it is viewed that employers are uncommitted and unwilling to participate in the development of WIL programs, then more on campus WIL models may be considered. This negative language or ‘chaotic edge thinking’ is ensuring that a university’s space of possibilities is not being explored, suggesting that the system is equalized, and therefore not operating far-from-equilibrium. Secondly, the negative language exhibited in many academics statements may also be influencing their perceptions about other elements of WIL development, including the challenges experienced when implementing WIL or vice versa. If all stakeholders view employers as uncommitted, unwilling to participate and work together to deliver WIL activities they will view placement host organizations as being unavailable, thus suggesting a lack of resources being a challenge when implementing WIL. Academic 8 provided another example of negative language stabilizing the system: Our academics here don’t want to acknowledge that [the need to change the way we educate people]. That’s why they don’t change their behaviors. That’s why they are happy in their own little ruts. I think I’ve told you, I’ll be publishing some papers this year. My colleague says lets research and I say what for. Who is going to use it? Who wants to know? That’s not the point. The point is publication. That’s what we are here for is to publish. It doesn’t matter who reads it or not. He’s a lovely person and I love

48 | Rook & McManus


working with him but we have this distant review about it. I’m the pragmatist. I’m saying what the hell are we doing this for? He’s saying because this is what we do. We publish. See the publication is an end in itself for these people. It doesn’t matter whether anyone reads it or not, except other academics.

The quote above describes how in the participant’s university, academics view that traditional ways of doing things prevail. It is evident from this quote that the traditional way of operating is held deeply within the academic’s identity. Academic 8 has quoted a colleague expressing that what they do is a result of knowing it is ‘what they are here for’. It is therefore no surprise that WIL is viewed as a threat because it is different to the traditional ways of operating.

Summary

T

he nine universities included in this study exhibit varying levels of connectivity and interdependence between stakeholders. There was a stakeholder disconnect evident in their understandings of the developmental elements of university programs (graduate attributes and employability). The CES was identified as coevolving, however with an emphasis of adapting to the external environment rather than evolving with. This means that changes happening at the level of the university are a short term adaptation to the environment. WIL was identified as an outcome of the CES moving far-from-equilibrium, however the negative chaotic edge thinking of the academics is threatening the evolution of the CES, as it has stabilizing affects that may mean the full potential of exploring the space of possibilities may never be realized. This along with the bureaucratic, rigid and inflexible state of the landscape identified by the stakeholders is having a negative effect on course design and collaboration with industry. This raises the question: with the conditions of the CES inhibiting the evolution of the university with its external environment, can new innovative ways of working and relating be developed? Can business schools use complexity theory to move forward to the edge of chaos? The answer is yes! Managers and universities are not completely helpless and at the “mercy of the system”47:13. There are many ways or opportunities for universities to affect organizational behavior within the system. The following section refers to ways for universities to move forward and affect positive organizational change.

E:CO 2016 18(2): 28-54 | 49

The way forward Working with Mitleton-Kelly’s29:3 hypothesis that “a robust organization evolves its social and organizational relationships, and is capable of guiding and supporting its co-evolution with a changing environment”, this section provides an argument for the development of the necessary enabling conditions in universities to facilitate coevolution with their environment. Mitleton-Kelly29 suggests that through creating the necessary conditions within the organization, ‘organizational fitness’ can be achieved. ‘Organizational fitness’ refers to the ability of an organization to survive through interactions with their environment37. Managing an organization as a CES requires the organization to want to experiment, spend some time in understanding the state of the landscape and its capabilities, learn how to set up the natural experiment to facilitate its success, and it “needs to create an enabling environment that will help achieve its goal, while understanding that the goal itself may change”29:4. According to Mitleton-Kelly28,29 this can be achieved by complexity researchers working with the organization to co-create the necessary conditions through helping the organization identify the conditions that are inhibiting the success of the organization, or organizations themselves can learn to create enabling characteristics for success. In both instances, it is argued that a successful CES facilitates and encourages the emergence of new ways of working and relating, new organizational forms, information and knowledge sharing, self-organization, and co-evolution29. The CES is encouraged to explore its space of possibilities, understand its own connectivity and interdependence within the system, and learn to cope in unpredictable environments through developing diversity including people, cultures, products and markets29. This suggests that for universities to move to the edge of chaos where full potential can be realized, many conditions need to be fostered. Connectivity between stakeholders needs to improve so that the system can learn to co-evolve with its environment rather than adapting to its environment. If connectivity and self-organization were to be encouraged throughout universities, it would assure that emergent properties and patterns would increase, and in turn, the university would co-evolve. As the university reaches a far-from-equilibrium point, it should be encouraged to explore the space of possibilities and adjacent possible, so as to enable the university to break through its traditional way of doing things. A supportive and positive culture between academics must be fostered so that uncertainty and change is embraced and a positive language space is cultured. As the system reaches the edge of chaos point it needs to move through the funnel to the right hand side of figure 1 and explore the space of possibilities and view the environment as being full of potential. 50 | Rook & McManus


It is recommended that business schools use WIL as a vehicle for increasing their competitiveness and relevance in the market through providing timely education that students expect in order to be work-ready or employable upon graduation. This can be achieved through exploring their space of possibilities more deeply, limiting dependence on government for funding and looking to industry, the community and student to understand their needs, wants and expectations. WIL program models in universities should be beneficial and offer students variety and flexibility without offering watered down alternatives. Connectedness must be fostered through networking and communication at all levels of the complex evolving systems, including external and internal environments. Most importantly, academics need to be comfortable with being uncomfortable and embrace uncertainty, so that their chaotic edge thinking of WIL as a threat does not continue to hinder the evolution of their business school. Through creating these conditions and connections, the complex evolving system can move to the edge of chaos and sustain the position where its full potential can be realised.

References 1. Caballero, C. and Walker, A. (2010). “Work readiness in graduate recruitment and selection: a review of current assessment methods,” Journal of Teaching and Learning for Graduate Employability, ISSN 1838-3815, 1(1). 2. Department of Industry Innovation Science Research and Tertiary Education (2009). “Transforming Australian higher education, http://www.voced.edu.au/content/ ngv%3A14895. 3. Dostaler, I., and Tomberlin, T. (2013). “The great divide between business school research and business practice,” Canadian Journal of Higher Education, ISSN 0316-1218, 43(1). 4. Jackson, D. (2013). “Business graduate employability- where are we going wrong?” Higher Education Research and Development, ISSN 0729-4360, 32(5), 776-790. 5. Jackson, D., and Chapman, E. (2012). “Non-technical skills gaps in Australian business graduates,” Education + Training, ISSN 0040-0912, 54(2). 6. Abeysekera, I. (2006). “Issues relating to designing a work-integrated learning program in an undergraduate accounting degree program and its implications for the curriculum,” Asia-Pacific Journal of Cooperative Education, ISSN 1175-2882, 7(1). 7. Brown, N. (2010). “WIL[ling] to share: An institutional conversation to guide policy and practice in work-integrated learning,” Higher Education Research and Development, ISSN 0729-4360, 29(5). 8. Orrell, J. (2004). “Work-integrated learning programs: Management and educational quality,” https://www.researchgate.net/publication/241036309_Work-integrated_ Learning_Programmes_Management_and_Educational_Quality.

E:CO 2016 18(2): 28-54 | 51

9. Mitleton-Kelly, E., and Land, F. (2012). “Complexity and information systems,” http:// citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.130.4416&rep=rep1&type=pdf. 10. McLennan, B., and Keating, S. (2008). “Work-integrated learning (WIL) in Australian universities: the challenges of mainstreaming WIL,” paper presented at the ALTC NAGCAS National Symposium June 2008, Melbourne. 11. Patrick, C.J., Peach, D., Pocknee, C., Webb, F., Fletcher, M., and Pretto, G. (2008). “The WIL (work integrated learning) report: A national scoping study,” Brisbane: Queensland University of Technology. 12. Reeders, E. (2000). “Scholarly practice for work-based learning: Fitting the glass slipper,” Higher Education Research and Development, ISSN 0729-4360, 19(2). 13. McIlveen, P., Brooks, S., Lichtenberg, A., Smith, A., Torjul, P., and Tyler, J. (2009). “Career development learning and work-integrated learning practices in Australian universities,” paper presented at the Careers Development Association of Australia National Career Conference, Melbourne, Australia. 14. Tynjala, P. (2008). “Perspectives into learning at the workplace,” Educational Research Review, ISSN 1747-938X, 3. 15. O’Shea, M., and Watson, G. (2007). “Academic learning for sport management students: learning through engaged practice,” Asia-Pacific Journal of Cooperative Education, ISSN 1175-2882, 8(1): 53-65. 16. Trigwell, K., and Reid, A. (1998). “Introduction: Work-based learning and the students perspective,” Higher Education Research And Development, ISSN 0729-4360, 17(2). 17. Cooper, L., Orrell, J., and Bowden, M. (2010). Work Integrated Learning: A Guide to Effective Practice, ISBN 9780521281942. 18. Universities Australia, and Australian Collaborative Education Network (2015). “National strategy on work integrated learning in university education,” http://cdn1.acen.edu.au/ wp-content/uploads/2015/03/National-WIL-Strategy-in-university-education-032015. pdf. 19. Barrie, S. (1999). “Assessment: Defining the worth of professional practice,” http://www. aare.edu.au/data/publications/1999/bar99509.pdf. 20. Pfeffer, J., and Fong, C. (2002). “The end of business schools? Less success than meets the eye,” Academy of Management Learning and Education, ISSN 1537-260X, 1(1). 21. Smith, M., Brooks, S., Lichtenberg, A., McIlveen, P., Torjul, P., and Tyler, J. (2009). “Career development learning: Maximising the contribution of work-integrated learning to the student experience,” https://eprints.usq.edu.au/5401/. 22. Jackson, D. (2009). “Undergraduate management education: Its place, purpose and efforts to bridge the skills gap,” Journal of Management and Organization, ISSN 18333672, 15(2). 23. Jackson, D. (2013). “Student perceptions of the importance of employability skills provision in business undergraduate programs,” Journal of Education for Business, ISSN 0883-2323, 88(5).

52 | Rook & McManus


24. Freudenberg, B., Brimble, M., and Cameron, C. (2011). “WIL and generic skill development: The development of business students’ generic skills through workintegrated learning,” Asia-Pacific Journal of Cooperative Education, ISSN 1175-2882, 12(2). 25. O’Leary, Z. (2010). The Essential Guide to Doing Your Research Project, ISBN 9781446258972. 26. Richards, L., and Morse, J. (2013). Readme First for a Users Guide to Qualitative Methods, ISBN 9781412998062. 27. Australian Education Network (2015). “Australian university rankings,” http://www. universityrankings.com.au/. 28. Mitleton-Kelly, E. (2003). “Ten principles of complexity and enabling infrastructures,” http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.98.3514&rep=rep1&type=pdf. 29. Mitleton-Kelly, E. (2003). “Complexity research - Approaches and methods: The LSE complexity group integrated methodology,” http://eprints.lse.ac.uk/29923/. 30. Oliver, B. (2010). “Teaching fellowship: Benchmarking partnerships for graduate employability,” http://espace.library.curtin.edu.au/webclient/StreamGate?folder_id=0&dv s=1475436101205~120&usePid1=true&usePid2=true. 31. Ernst and Young. (2011). “Higher education and the power of choice, higher education team,” http://www.ey.com/Publication/vwLUAssets/Higher_education_and_the_power_ of_choice_Australia/$File/Higher%20education%20and%20the%20power%20of%20 choice%20Australia.pdf. 32. Abeysekera, I. (2006). “Issues relating to designing a work-integrated learning program in an undergraduate accounting degree program and its implications for the curriculum,” Asia-Pacific Journal of Cooperative Education, ISSN 1175-2882, 7(1). 33. Nelson, B. (2002). “Higher education at the crossroads, ministerial discussion paper,” Commonwealth Department of Education Science & Training. 34. Bridgstock, R. (2009). “The graduate attributes we’ve overlooked: enhancing graduate employability through career management skills,” Higher Education Research and Development, ISSN 0729-4360, 28(1). 35. Business Council of Australia. (2011). “Lifting the quality of teaching and learning in higher education,” http://www.bca.com.au/publications/lifting-the-quality-of-teachingand-learning-in-higher-education. 36. Orrell, J. (2011). “Good practice report: Work-integrated learning,” http://www.olt.gov.au/ system/files/resources/GPR_Work_Integrated_Learning_Orrell_2011.pdf. 37. Kauffman, S. (1993). The Origins of Order: Self-Organization and Selection in Evolution, ISBN 9780195058116. 38. Mitleton-Kelly, E., and Davy, L. (2013). “The concept of ‘co-evolution’ and its application in the Social Sciences: A review of the literature,” in E. Mitleton-Kelly (eds.), Co-Evolution of Intelligent Socio-Technical Systems: Modelling and Applications in Large Scale Emergency and Transport Domains, ISBN 9783642366130, pp. 43-57.

E:CO 2016 18(2): 28-54 | 53

39. Star, C., and Hammer, S. (2008). “Teaching generic skills: eroding the higher purpose of universities, or an opportunity for renewal,” Oxford Review of Education, ISSN 0305-4985, 34(2). 40. Cleary, M., Flynn, R., Thomasson, S., Alexander, R., and McDonald, B. (2007). Graduate Employability Skills, http://aces.shu.ac.uk/employability/resources/ GraduateEmployabilitySkillsFINALREPORT1.pdf, p. 76. 41. Kuhn, L. (2009). Adventures in Complexity, ISBN 9780956263100. 42. Lewin, R. (1999). Complexity: Life at the Edge of Chaos, ISBN 9780226476551. 43. Kuhn, L., Woog, R., and Hodgson, M. (2003). “Applying complexity principles to enhance organisational knowledge management,” paper presented at the Proceedings, Global Business and Technology Association Conference, Budapest, Hungary. 44. Rook, L. (2013). “Mental models: a robust definition,” The Learning Organization, ISSN 0969-6474, 20(1): 38-47. 45. Billett, S. (2009). “Realizing the education worth of integrating work experiences in higher education,” Studies in Higher Education, ISSN 0307-5079, 34(7): 827-843. 46. Lawson, R., Fallshaw, E., Papadopoulos, T., Taylor, T., and Zanko, M. (2011). “Professional learning in the business curriculum: Engaging industry, academics and students,” Asian Social Science, ISSN 1911-2017, 7(4): 61-68. 47. Richardson, K.A. (2008). “Managing complex organizations: Complexity thinking and the science and art of management,” Emergence: Complexity & Organization, ISSN 15213250, 10(2): 13.

54 | Rook & McManus

Social systems: Complex adaptive loci of cognition


Applied

Marta Lenartowicz Free University of Brussels , BEL Marta is a social systems scientist based at the Global Brain Institute, Free University in Brussels (VUB). I study self-organization of social systems, seen as autopoietic, cognizing and intelligent “species”. I received my PhD in humanistic management (public affairs) in 2014 and my MA in philology (theoretical linguistics) in 2001 from the Jagiellonian University in Krakow.

David (Weaver) Weinbaum Free University of Brussels , BEL David R. Weinbaum (Weaver) is a PhD. researcher at the Global Brain Institute at Center Leon Apostel (CLEA), the Free University Brussels (VUB). He holds M.Sc. in electronics and computer engineering from Tel-Aviv University (1989). His research interests extend to Philosophy of mind, Cognitive Science, Foundations of thought, Metaphysics, Complex systems, Individuation and self-organization, Cybernetics, Evolution Theory and post-modernist philosophy.

Petter Braathen Vrije Universiteit, NLD Mr. Braathen holds a Master of Science in Control & Electrical Engineering from Norwegian University for Science and Technology, and from University of Washington. He further holds a Master of Science in Strategy and International Business from the Norwegian School of Management. Mr. Braathen is currently completing a Ph.D. at the University of Brussels, in the interdisciplinary program, focusing on complexity theory applied to strategic management and organizational theory. Mr. Braathen facilitates strategic visioning, organizational change, and leadership development for global companies in various industries.

We argue the case that human social systems and social organizations in particular are concrete, non-metaphorical, cognitive agents operating in their own self-constructed environments. Our point of departure is Luhmann's theory of social systems as self-organizing systems of communications. Integrating the Luhmannian theory with the enactive theory of cognition and Simondon's theory of individuation, results in a novel view of social systems as complex, individuating sequences of communicative interactions that together constitute distributed yet distinct cognitive agencies. The relations of such agencies with their respective environments (involving other agencies of the same construction) is further clarified by discussing both the Hayek-Hebb and the perturbation-compensation perspectives on systems adaptiveness as each reveals different and complementary

E:CO 2016 18(2): 55-89 | 55

facets of the operation of social systems as loci of cognitive activity. The major theoretical points of the argument are followed and demonstrated by an analysis of NASA's communications showing how a social organization undergoes a process of individuation from which it emerges as an autonomous cognitive agent with a distinct and adaptive identity. With this example we hope to invite a debate on how the presented approach could inform a transdisciplinary method of cognitive modeling applied to human social systems.

Introduction

T

he most widely accepted account of social systems today is that they are complex and adaptive—and that they are not bearers of cognition. They are complex in the sense that they are ‘made up of a large number of parts that interact in a non-simple way’ and that ‘given the properties of the parts and the laws of their interaction, it is not a trivial matter to infer the properties of the whole’8. They are considered adaptive in as far as they operate in relation to their environment in such a manner that preserves a certain set of their characteristics invariant or within a limited range of variation. They are typically not associated with the concept of cognition for two reasons. One, even though a cognitive component—of a human mind—is obviously involved, it is typically assumed that it can be treated in a black-box manner: it is enough to adequately capture the range of its possible inputs and outputs to model its role within an overall system. On the other hand, if the concept of cognition were to be attributed to the entire social system, there is a risk attached and a suspicion evoked of an eclectic and mystical use of the concept. Within the scientific discourse such attempts are rare. Nonetheless, a line of argumentation has been already established, which suggests that such application of the concept is not only due, but also rational9,10,11,12,13,14. Our aim in this paper is to support this claim. We do that through a re-conceptualization of the widely accepted notions of ‘complexity’ and ‘adaptiveness’ in a way that highlights and strengthens the view of social systems as proper, non-metaphorical, holders of cognition. To a large extent our re-conceptualization follows Niklas Luhmann's1,15,16 approach to social systems. By integrating Luhmann's thinking into the understanding of social complexity and linking it with Gilbert Simondon's theory of individuation3 we offer a view on social systems as complex, individuating sequences of occurrences of communication. We further argue that operating of such individuating sequences within their complex environments can be approached from either one of the two prevailing theoretical perspectives on adaptiveness, that is: both Hayek-Hebb4,5,6 and perturbation-compensation7 views on

56 | Lenartowicz, Weinbaum & Braathen


systems adaptation. While both perspectives are applicable, each reveals a different, complementary facet of the operating of social systems. This leads to our final argument, in which we show that the resulting integrated view on social systems, if taking both facets into account, is in itself an abstracted model of individuating, autonomous, distributed cognition.

Concept 1: Components

I

n the research paradigm of Complex Adaptive Systems it is typically taken for granted that, in the case of social systems, the basic components are human beings that interact in a ‘non-simple', context-dependent, non-deterministic manner that gives rise to complexity17,18. We would like, however, to explore here the alternative view of Niklas Luhmann. Luhmann1,15,16,19, having developed a consistent sociological framework inspired by systems theory, second-order cybernetics, and evolution, has managed to re-describe all major contemporary social systems in a way which assumes basic components that are not people, but sense-making, meaning-processing communications. These are, naturally, communications among people, but any ‘property’ of an individual human, such as the ‘contents’ of her mind, starts to play a role in a social system only when it is socially expressed. If withheld, it remains in the system's environment. On the other hand, if conveyed -expresis verbis or otherwise- it becomes a communication, i.e., the basic processual component of a social system. Luhmann's focus on communication, instead of communicating people, is part of a wider paradigm shift, which goes back to the Heraklitian view of reality as constituted of processes—instead of objects or agents. Our conventional system of thought is grounded in an ontology rooted in Greek philosophy and particularly in the metaphysics of Aristotle. It asserts a world made of entities with an identity that is a priori given as a set of stable properties and qualities. The Heraklitian shift from being to becoming enjoyed a revival during the 20th century in the writings of philosophers such as Nietzche, Bergson, Simondon among others, and was further distilled in Gilles Deleuze's ontology of difference20,21,22. It is also exemplified, to some degree by action ontology23,24,25,26 and process metaphysics27,28 and translated into the systems theoretic terms by Manuel DeLanda29. It emphasizes that even the most solid objects are in fact networks of processes, only temporarily stable (metastable). If this is so, we overlook most of the fabric of the reality when we approach it by delineating only stable entities -be it humans, systems, or any other objects- and only then look at what is happening within them and among them. We may get a fuller or even quite a different picture, if we try first to disregard the agents, typically attracting most of our attention, and instead focus solely on actions; whereas in action we mean anything that brings E:CO 2016 18(2): 55-89 | 57

forth a difference in the state of affairs. In this sense, we may leave the agent outside the boundary of the observed arena, treating her not as a component, but a mere catalyser: ‘an aspect or part of a state that is necessary for the action to occur’25. Thus, when approaching the notion of complexity on the grounds of such ontology, one needs to reframe the basic interacting components, whose interactions bring about complexity: it is actions—not agents. In Luhmann's approach, this shifts the focus from humans to processes of communication. A communication happens as a difference-making selection, or more precisely: ‘a synthesis of three different selections, namely the selection of information, the selection of the utterance [Mitteilung] of this information, and the selective understanding or misunderstanding of this utterance and its information’30. This triad corresponds with the semiotics of Peirce31,32, offering a processual version of it. Out of all possible processes, some get distinguished to carry meaning, some—to be referred to, and yet others—to be a frame of reference, i.e., providing a context for understanding. Only if all three selections take place, a process called ‘communication’ occurs. We can start the tracking and modelling of social systems with any randomly chosen single component: any occurrence of communication in the world. However, searching for an example that could be interesting for as wide international readership as possible, we have selected the utterance formulated during one of the most significant moments in the history of one of the best recognized organisations across the globe: NASA. Let us then start here with the ‘A-OK full go’ neologism, uttered by Commander Alan Shepard Jr.’ during the NASA launch, which made him the first American astronaut in space on the 5th of May 1961. Seen as a triple selection, this communication combines three processes of distinction-making: It may appear important to point out that all three communication-constituting selections are made by human minds and, as minds are subjective and changeable, to emphasize therefore that the selections cannot be considered irrespective of their source. In most cases, as in the example above, it is certainly so: all selections have been made by the operations of subjective and changeable human minds. However, it does not have to be necessarily so: the materialistic branch of the humanities already extends social agency to objects, technologies, and ‘things’33,34 and the AI branch of computer science makes it clear that this will be increasingly apparent in the future. But, again, in the process-oriented perspective what is most important is that a triple selection is being made. Whatever mental, technological, physical, or other kind of processing is prompting it, when a selection of information takes place, when it gets combined with a selection of an utterance, and when a selection of understanding


Social systems: Complex adaptive loci of cognition Information

Several processes are being selected (by the Commander) to be rendered by the following communication:the mental and physical processes of the astronaut having reached the state of optimal readiness for the blastoff;the technological processes running the operation of the spaceship having reached the state of optimal readiness for the blastoff;the current technological, social, and political processes (realized in the form of the spaceship blast-off) being as yet unprecedented in the techno-socio-political context, which is selected as relevant.

Utterance

A-OK full go

Understanding

Selection of understanding happens within the dimension of time: it happened for the first time at a certain moment during the blastoff and, after the utterance has been transformed into a text, it continues to happen each time the ‘A-OK full go’ utterance is being encountered. One of the current selections of understanding of that phrase is:‘Defined as an engineering term for ‘double OK’ or perfect, it became a U.S. idiom for ‘everything is going smoothly’.69

Example 1 The triple distinction-making selection in a single communication (NASA) follows, in addition to all the processes involved, a new process becomes apparent with this event: a process of communication. Should that utterance be multiplied in writing, print, audio recording, or any other technology, a new occurrence of communication takes place whenever it is understood. And whenever an understanding of an utterance happens, a new selection of information may follow to be uttered in response—or in relation—to that understanding, the sequence of communications continues. Even if it were solely the human mental activity what made all the selecting of information happen in the first place, the triple combination constituting an occurrence of communication bounds these selections out of that mental process and couples it with two other selections, which now constrain it and anchor it in a specific point in time. ‘Nothing is transferred’—Luhmann claims—‘Redundancy is produced in the sense that communication generates a memory to which many people can lay claim in many different ways’30. There is no better way of witnessing how that bounding out actually happens other than by ‘paying but little attention to what we ourselves say’30. If we do that, Luhmann explains, ‘we already become aware of how imprecisely we must select in order to say what one can say, how greatly the emitted word is already no longer what was thought and meant, and how greatly one's own consciousness dances about upon the words like a will-o'-the-wisp, uses and mocks them, at once means and does not mean them, has them surface and dive, does not have them ready at the right moment, genuinely wants to say them but, for no good reason, does not’30.

E:CO 2016 18(2): 55-89 | 59

Redefining the elements of social systems and reconsidering the complexity resulting from their non-simple interactions, yields profound consequences for the way in which social complexity can be studied and modeled. If the interactions constitutive to the systems of our interest, happen between instances of communication, rather than individuals, then the properties relevant for tracing and modelling of such interactions are bound to be quite different from the properties of human agents. While interconnections between humans may be explained as a result of their proximity35, similarity36, trust37, etc., the interconnections between various occurrences of communication extend from the triple selections they are constituted by. Thus, for example, (1.) the selection of understanding in one communicative occurrence will constrain and be conserved in the selection of information and utterance in the following ones; (2.) the selection of a form (the utterance) in one will be retained, refined, or refused, in another; (3.) by adhering to a shared form, several otherwise unrelated communications will prompt a selection of understanding which bundles them all together, etc. While the combinatorial possibilities are multiple, as observed in the dimension of time they result in the production of sequences of intertwined communicative occurrences which are, this way or another, adhered to some other communicative occurrences as their predecessors, frames of reference, genre models, etc. Such sequences in turn constitute various patterns, of which some are completely unique while others are more or less frequent. The most frequent and recognizable ones include: conversations, narratives, discourses, languages, organizations, groups, projects, governments, states, economies, religions, and social movements.

Concept 2: Individuation

T

he Aristotelian focus on objects and individuals also conditions the way one accounts for their genesis. To put it briefy, if individuals are the primary ontological elements of anything existing, the genesis of individuals is merely the transition of certain individuals into other individuals. Everything starts and ends therefore with individuals. The Copernican shift to a process-oriented ontology, moves away from individuals as the primary given ontological elements whereas all transformations are secondary, to individuation3,38,39—a primary formative activity whereas individuals are always intermediate, only temporarily stable entities, undergoing a continuous process of change. Individuation is a process where boundaries and distinctions that define individuals arise without assuming any individual(s) that precede(s) them. The nature of distinctions and boundaries is subtle; inasmuch as they separate subject from object, figure from background, and one individual from another, they must also connect that which they separate. A boundary, therefore, is not only known by the separation it establishes but also by the interactions and relations it facilitates. 60 | Lenartowicz, Weinbaum & Braathen


Gilbert Simondon, the father of the theory of individuation3 encourages us to understand the individual from the perspective of the process of individuation. For him, the individual is a metastable phase within a continuous process of transformation and is always impregnated with not yet actualized and not yet known potentialities of being. According to Simondon, an individual is not anymore the rigid well defined Aristotelian element endowed with ultimately given properties, but rather a plastic entity, an on-going becoming. How can an entangled network of meaning-shaping distinctions (information), meaning-carrying forms (utterances), and meaning-making selections (understanding) breed an autonomous, individuated social system? How can a particular human organization, a research project, a nation state, a social movement, a language, form a distinguishable, coherent assemblage70 of interacting components? It is easy to see the relevance and advantage of applying the concept of individuation to communication constituted social systems. Clearly, communication is a formative activity in regards to the social systems they constitute. The theory of individuation provides an important conceptual bridge between the distributed dynamism of communication and individuated entities such as teams, corporates, organizations, communities. In 39 the authors discuss in length the mechanisms of individuation and specifically how local and contingent interactions progressively achieve higher degrees of coordination among initially disparate elements and by that bring forth complex individuated entities with agential capabilities as products. Moreover, the very nature of communication as explicated above seems to be in full agreement with the concept of individuation. Happening as a triple selection, a communicative occurrence marks the fluid, processual reality with several temporary boundaries: •

the Spencer-Brownian40 boundary between the marked and unmarked sides of a distinction, delineating and linking together the selected information, which is intended to be conveyed, and the non-selected one, which thus becomes apparent as the one that could have been selected, but was not;

•

the Yuri Lotman's41semiotic boundary, delineating and linking together the signified and the particular signifier42,31, the utterance which has been selected to carry the meaning;

•

and the ‘sense-making boundary’ between the sign and the context, brought about by selecting of understanding, delineating and linking together the conveying of an information and the context in which it occurs.

E:CO 2016 18(2): 55-89 | 61

It may be that all these boundaries are brought about just temporarily, by a single communicative occurrence, and never recreated. Not all communications need to originate from preceding communications. It is always possible to communicate something novel in a way that was not determined by any pre-existing signifier (a word, a form, a medium). However, typically, communications do connect to one another, by either elaborating on the predecessor's signified, making a novel use of its signifiers, or by preserving the context of its occurrence. In the first case, some information distinguished by a communication is inherited from a preceding communication and thus preserved, confirmed, and reinforced in a novel form. In the second case, a communication uses the same forms, the same pre-existing code, to convey something that has not been communicated with these forms before. It produces new distinctions, charts new shapes, while still maintaining links with already established (stabilized, recurrent) usages and other meanings of the borrowed form. Adhering to the form of previous communications, the new one serves both as a repetition (conservation) and a difference (innovation) in relation to it. In the third case, a communication brings new information and employs new forms, but preserves the selection of understanding, made in a preceding communication: and thus responds to it. Most typically, such combinations multiply simultaneously: a communication conserves information from one communication, borrows a form from another, and reinforces the context of a third one. And it may happen that the signifier, the signified, and the context are all inherited simultaneously from a single predecessor as the same communication is repeated again and again, to convey just the same meaning, with the same form, in the same circumstances. Let us consider the interrelations of the following four communications related to NASA (See example 2). Taking into account the consecutive occurrence of these communications in time, we can plot the following interrelations among the three selections made in each communication: Interrelations

boundary between the boundary between signified and non-signified the signified and (information-making) the signifier (signmaking)

boundary between the sign and the context (sense-making)

a -> b

conserved and innovated


—

a -> c



—

a -> d

—


—


Social systems: Complex adaptive loci of cognition Communication a

It is the policy of the United States that activities in space should be devoted to peaceful purposes for the benefit of all mankind.71

Communication b

When I first started working at NASA more than twenty years ago, the motto at the time was “For the benefit of all Mankind”. It came under severe criticism of the extreme nationalists who wanted to change the word “Mankind” to “Americans”, and of the extreme feminists who questioned why “Man” and not “Woman”. In fact, it even got criticized by the animal rights groups and environmentalists for the exclusionist implication of “Mankind” towards animals and plants. And hence, NASA settled on “For the benefit of all”. (Süleyman Gokglu, Senior scientist at NASA43)

Communication c

“For the benefit of all” (NASA motto)79

Communication d

I have observed people outside NASA saying that NASA’s motto is “For the benefit of all.” I don’t recall ever seeing NASA state that as the motto, in fact, I don’t recall the word motto ever being used at the agency level. NASA does have a vision statement, and that is somewhat analogous to a motto. The official vision statement of NASA is: “To reach for new heights and reveal the unknown so that what we do and learn will benefit all humankind.” (Robert Frost, Instructor and Flight Controller in the Flight Operations Directorate43)

Example 2 Interrelations between communications (NASA) b -> c

conserved

conserved

conserved

b -> d

—

conserved

conserved

c -> b

—

conserved

conserved

Once a novel semiotic distinction appears, it also reflects on all previous communications that are somehow implicated by it as well. Whether that ‘form’ is a single word, it em5ploys its previous denotations to render a novel one. If it is a yearly report, it renders new data and events, making them replace the previously displayed ones. If it is a new motto of an organization, it introduces new distinctions and leaves out those that ceased to be relevant. Remarkably, communications often perform both conservation and innovation simultaneously: they conserve in some new way a number of distinctions made previously by other communications while employing some pre-existing communication templates to introduce distinctions which are new. These two modalities of continuity and discontinuity between communications are thus usually mingled, contributing to the complexity of their interrelations. This dynamism of communication brings forth fluid identities39 these are metastable entities in the course of individuation whose defining characteristics change over time but without losing their longer term intrinsic coherence and distinctiveness from their milieu.

E:CO 2016 18(2): 55-89 | 63

For a social system to persist as an individuating entity, however, not everything goes; while the nature of each communication is open-ended in principle (hence the potential for novelty), a certain critical mass of recurrence, and coherence grounded in the historical record of communications is necessary. Then, when observing such a metastable ‘entity’ we discover that it is not a constant pattern but rather an emergent dynamics which results in adaptability. But this concept requires the notion of the ‘environment’ to be addressed first: what it actually means when related to a bundle of intertwined communications.

Concept 3: Environment

L

uhmann's theory has made it apparent how much the meaning of the concept of the system's environment shifts when we adjust our lenses to see communications-constituted systems, instead of agents-constituted ones. Whereas an environment of interacting people would normally be understood topologically, the environment of interacting communications is much less so. It is to a large extend semiotic. Having identified the three selections which forge a single communication is a good basis for an initial tentative definition of the environment of such a single occurrence: it includes, simply, whatever the communication refers to and is being referred to. This encompasses not so much the actual surroundings of the process of communication, but the semiotic space delineated by the three meaning-creating selections: the context delineated by the selection of understanding, the signifier delineated by the selection of the utterance, and the marked side delineated by the selection of information. The environment of the simple single communication ‘For the benefit of all mankind', for example, ecompasses the ‘mankind', as constructed by the combination of the three selections. Even if the object of this denotation existed only as fluid processes in a realm where nothing has individuated yet, or did not exist at all, such communication would chart a temporary boundary rendering the ‘mankind’ as its environment—it would call it to a temporary existence. And since this communication did not happen as a first one to use that particular form, it was conserving and reinforcing the boundary that already had been brought forth many times before in entirely different contexts. Most probably, when initially used in the NASA context it was not intended nor understood as excluding women: according to the MerriamWebster dictionary the notion of ‘mankind’ used to refer to the entire human species since the 13th century. But once understood as excluding, it started to do exactly this. Once this understanding happened once, the already unsuccessful communication had to thus be replaced by a new one if the previously rendered environment was to be maintained. Then, a new selection of the utterance ('For the benefit of all') allowed



the selection of information and the selection of understanding to re-converge in the previously rendered space. Yet another shift has happened with the initial exclusion of animals and plants. This one was intended, uttered, and understood: the environment rendered by the first motto simply did not include species other than humans. Interestingly however, they were somehow present: the indication of the human species as the marked side of information was bringing forth the Spencer-Brownian unmarked side of the selection, i.e., all other species, as well. They were selected to remain just outside the environment and, thus, they could have been observed as excluded. This reminds of David Seidl's44 explanation of self-transformation of social systems as being enabled and constrained by the unchosen alternatives. The simplest computational way for charting of the environment temporarily constructed by a communication may be to identify the nouns which have been used in the utterance: Communication

Environment

To understand and protect our home planet; To explore the Universe and search for life; To inspire the next generation of explorers …as only NASA can.73

Home planet; Universe; Life; Next generation of explorers NASA

Example 3.1 The environment of a single communication (NASA) Such a landscape portrait of the environment, however, is not the only product of communication. Another one is the portrait of the communication itself. While an instance of communication may or may not select itself to be an object of itself, once it happens, it becomes available to be rendered as the environment of another communication. Hence, even when the communication ‘A-OK full go’ does not refer to itself in a way in which the communication ‘This sentence is short’ does, once it has taken place, it can be referred to as an environment by any other communication which follows it in time. Thus, the environment of a communication expands: it ecompasses not only what it refers to, or is being referred to, but also all the communications that perform the referring. In the era of spoken word, a communication was available to become an environment of only a limited number of other communications in its spacetime neighborhood. In the era of written word and print—once written, a communication was made available for all consecutive communications in time that happened to neighbor the instances of its recurrence in every new reading. In the era when the Internet is taking over an ever-increasing share of all human communications, both the number of communications immortalized in this way and the spatio-temporal scope

E:CO 2016 18(2): 55-89 | 65

of their reach expands dramatically again1. Today, a digitized communication transmitted over the Internet is available to become an environment of any other consecutive communication in the world. Therefore, even if a communication does not implicitly refer to itself and thus position itself in a relation to its own environment, it has an endless potential for being referred to—and for being thus positioned. Since all communications are endlessly available to be referred to, also the environments that they delineate become available endlessly. Each such an environment has a potential of becoming evoked by a following occurrence of communication and thus, by the means of repetition of such occurrences, has a potential of becoming more or less stabilized. Once communications interact and individuate into more entangled and interrelated sequences, the stabilization of their mutually fashioned environment increases. The more such a shared environment is referred to (and every communication may add another instance of such reference), the more opportunities arise for the following communications to anchor there as well. Furthermore, mutual referring and selfreferring of the communications themselves include them as part of that increasingly stabilized environment. As a result, specific sequences of communication become bundled more or less tightly with their respective environments and turn into patterns of communication that are increasingly likely to be further referred to as ‘belonging’ to that specific environment/context. Thus, the whole socially constructed reality74 comes into existence. On one hand, the description of the environment may grow and thicken into a fully operational worldview45,46, while on the other, some occurrences of communication that are shaping that worldview, may become increasingly labeled as the ones, that are part of it. The consolidating worldview is accompanied with specific language games, which are expected and accepted in its context75. This very process has been well captured in Anthony Giddens’76 theory of structuration. Communication x

This is the new motto of NASA

Example 3.2 Construction of the environment of communication (NASA) When we say "this is the new motto of NASA" and point to a specific communication, we render both that communication and the interrelated sequence of communications referred to as ‘NASA’ to forge an environment for our own utterance, and we link them all together.



Then, another communication may occur, which points to ours, saying: Communication y

NASA has just announced its new motto

While this second communication has a quite similar environment to our own (consisting of one occurrence of communication and the sequence called ‘NASA'), it constructs it differently: it presents one as being sourced within the other. But, even more interestingly, a third communication may follow stating: Communication z

No no, NASA does not have an official motto it has only a mission statement

While discussing the concept of social system's components, we have adopted Luhmann's assumption of communication processes as diverging from mental processing by the means of their triple combination. This combination binds communications together and externalizes them from the human mind. While, as Luhmann has put it, a ‘redundancy is produced’ in the sense that an original mental processing becomes accompanied by an additional sort of processing via communication, this does not mean, of course, that the former becomes irrelevant or idle. We can easily observe the interrelation between the two modes of processing, when communications like [y.] and [z.] collide. Within our ‘mental environment’ of the above three occurrences of communication, we—most likely—instantly need to clarify whether the motto was indeed ‘sourced within NASA', or perhaps the communication [z.] was. The lack of coherence observed between the three witnessed occurrences of communication mobilizes our attention to search for or to initiate additional occurrences, until coherence is established. This urge reveals a third layer of the environment of communication as its source: mental (and possibly technological) processes on the basis of which communication emerges. It also reveals the important role of this layer of environment in the individuation of entangled sequences of communication into more and more consolidated ones. Just like wind and water support the consolidation of rocks, by either washing out or gluing dust to their surface, the mental processing of the occurrences of communication actively engages to determine how they are anchored into larger bundles. Once a certain degree of coherence is achieved, the mental environment of communication actively facilitates its further individuation by searching for and/ or initiating new instances of communication that promote clarity, coherence and the determination of yet undetermined details in previous communications. In fact, all three conceptual ‘layers’ of the environment of communication, as described above—i.e., (1.) the selections rendered by each communication, (2.) other

E:CO 2016 18(2): 55-89 | 67

related communications, and (3.) the mental processing mobilized for the relationmaking—can be seen as both constructing that communication and being constructed by it. While whatever communication refers to and is being referred to (layer 1) is in Luhmann's constructivist tradition seen as constructs of communication, not constructors of it, at the third layer, human thought has a tendency to attribute all the agency to itself. Of course, the exclusivity of both attributions may be (and have been) questioned, but even if they were not, the dynamics of the mutual co-construction would still be fully revealed at the middle conceptual layer: of the environment of communication as consisting of other occurrences of communication. While an initial occurrence may refer to a completely fluid sequence of non-stabilized processes, a following communication is already confronted with this selection: its space is, thus, partially constructed before it occurs. And, equally importantly, by referring to its predecessor and positioning it within its environment, the following communication completes and refines its construction. Returning to the three occurrences of communication, presented in Example 3.2., we can ask: what is being (co-)constructed there? While at the first, ‘landscape’ layer, one of the selections being delineated is clearly the selection called ‘NASA', at the second, ‘inter-communication’ layer, that selection is related (and un-related) to a single occurrence of communication. This oscillation creates a tension at the third, ‘mental’ layer of the environment, which becomes motivated to either confirm or disconfirm that relationship. What is, then, being constructed by the three occurrences of communication [x, y, x] is not only the selection of NASA and its communication-constituted environment, but a tension within the environment, an action window13, which elicits a dynamic geared towards an increased clarification of the relationship between the two. If we choose to draw the boundary of the observed system in a way which positions this particular source of action outside it (since after all drawing of boundaries is mostly the observer's choice), we can consider the mental tensions elicited by particular patterns of communication, and absorbed by them, to play a role similar to the one played by wind for the technical system of a windmill, or the role of oxygen for a plant. For the system to function, it is crucial that this particular agency of the environment is present. Systems are built, self-organize and evolve with the assumption that this the case. While for some purposes it is more useful to treat such sources of action as belonging to the system, for others it is better to consider them as part of the environment. In our case, the investigation of the hypothesized autonomous cognition located in social systems, obviously sets the boundary of our observation between humans and the processes that are argued to bring about cognition. And 68 | Lenartowicz, Weinbaum & Braathen


this apparently goes right against the common sense. In the modern western worldview the only agency lies firmly within the processing performed by a human mind: it is a human who feels compelled to clarify and who seem to be in the position for an arbitrary filling in the gaps. But by inspecting of how such filling-in might actually happen, we will notice that it has to take the form of either locating of an already existing communication or the initiating of a new one. Thus, markedly, the precise locus of agency shifts back to the process of communication. Moreover, a peculiar constraint is reached as to the kind of communication capable of resolving the dilemma. It starts to be apparent that the dilemma whether an occurrence of communication (e.g., the motto) does or does not ‘belong’ to an individuating interrelated sequence of communication (e.g., NASA) cannot be settled through any occurrence of communication. Being experienced participants of the social reality, the readers and us know it already: our exemplary dilemma can be clarified only by either searching for a communication that is clearly positioned ‘within NASA’ (an official NASA publication, the NASA website, an official speech, etc.), or by uttering a communication which is both meant and understood as a communication originating within NASA. What that ‘within’ actually means, and how is that positioning of the locus has come about, remains as yet to be explicated in the following. For now let us note that while any communication is free to position the selection called ‘NASA’ in relation to any selection of the environment, not all communications are capable of selecting where (in the web of various instances of communication) does NASA end and its environment start. At some stage of the process of individuation, the locus of control over the boundary between the environment and the individuating sequence of communications (which at this point can be called a system) has started to be positioned within it2. This way the Luhmannian social systems arise, which ‘have the ability to establish relations with themselves and to differentiate these relations from relations with their environment’1. We hope to have shown so far that, once social complexity is approached as constituted of instances of distinction-making communications, at some point of the ongoing reflexive referencing among various instances of communication, an emergent complex dynamic can be observed. While, as we have discussed, the environment of a single instance of communication can be rendered arbitrarily by any other communication to follow, sooner or later a point is reached where it becomes obvious that the delineation of sequences of interrelated communications within their respective environments is no longer arbitrary. It turns out that it is no longer up to any communication to follow, to position a motto as either ‘belonging to the sequence’ called ‘NASA’ or only ‘relating to that sequence’ from within the environment. Of course, we may formulate any communication we wish in that respect, but we can also expect

E:CO 2016 18(2): 55-89 | 69

that a confirmation or denial of such an attribution may follow in a form of communication which is meant, formed, and understood as a communication sourced within the sequence. Thus, the positioning of the boundary of this sequence becomes a function of its internal dynamics, and it no longer can be easily shaped by a communication sourced ‘outside'. This brings forth the second notion widely accepted today as describing social systems: the notion of adaptability.

Concept 4: Adaptability

S

ocial systems widely considered as complex adaptive systems (CAS), are not mere aggregates of interacting components delineated by external observers. These are aggregates which self-maintain their own coherence/identity through their own dynamics. Thus, whenever a change happens in the system's environment, it adapts. The interactions of the system's components adjust and change in a way that best supports the continuity and coherence of the whole—as if the whole ‘consciously’ mediated the risk, ‘knowing’ that its further existence in the changed environment would otherwise be compromised. Such adaptive capability may seem obvious in living systems, but quite mysterious if attributed to other systems. There are, however, several theoretical explanations of how this happens even in systems that are not living (such as cities, markets, etc.) or not self-conscious (such as insect colonies, ecosystems, etc.) Existing explanations typically fall into one of two broad categories: the system is either seen as responding to the changing environment4,5,47,48, or reacting to it7,49,26: •

The ‘responsive adaptation’ approaches describe the way, in which a system develops a model of its environment: a model that dynamically reflects external changes, adjusting accordingly. The concept of how that happens was independently developed by Donald Hebb4 and Friederich August von Hayek5 as a model of learning and memory. The central idea is that external stimuli generate interactions within the system's internal network of components—and a pattern of such interactions becomes a map of the environment, as experienced by the system. Since interactions are reinforced by the repetition of the stimuli, or weakened by lack of thereof, the resulting map gets continuously updated. This way the system remains flexibly responsive to its environment. This explanation fits well into the cybernetic paradigm, which refers to living organisms as cybernetic systems50.

•

The ‘reactive adaptation’ approaches draw quite a different picture; sketched for the first time by by Humberto Maturana and Francesco Varela7. Maturana and Varela



tackled the fundamental difference which sets apart the system-environment relations maintained by living systems from those held by man-created machines. They posit that while living systems are open in the von Bertalanffy's51 sense, i.e., they do interact with their respective environments, they are also operationally closed. This basically means that all the operational responses to external changes or perturbations a system may display, only depend on the inner structure and the state of the system at the time of change and can only induce further changes to its inner structure and state (in cases when that the system does not disintegrate). Therefore, the so-called external stimuli is not instructive in regards to the system's options of operation. It follows, that the system-environment interactions take place only in a way that allows just that: the system's recursive production of its own identity pattern under ever-changing conditions. Whenever a change in the environment forces an internal shift in the system, the shift is compensated by some other internal changes. Whereas Luhmann's own choice in understanding the adaptability of communication-constituted social systems notably favors the ‘reactive’ path of explanation, it seems that the dominant way of applying the concept of adaptation to social systems follows the model of the ‘responsive’ one. The so called ‘Hayek-Hebb model’6 has been found relevant in a diverse array of applications within the social sciences. E.g., it has been used to explain adaptability of markets and scientific projects47, employed to explain global learning arising from local decisions prompted by agents’ individual self-interest52, and applied to a model of the evolution of collective intelligence48. On the other hand the controversy whether or not the concept of autopoiesis can be extended from biology to social systems, which would unquestionably justify the application of the ‘reactive’ model of adaptation, has not been settled. The idea was refuted at first53, following Maturana's and Varela's own argument that social systems are not self-producing in a sense that they do not produce, but only co-ordinate people—considered to be the components. While after Luhmann's ‘communicative turn’ this particular argument is no longer valid, and while—as it has been recently argued Hugo Cadenas and Marcelo Arnold54—the concept of autopoiesis does prove to be productive in social sciences, its status remains controversial. This, however, does not prevent the model of ‘reactive adaptation’ from being explored. To that end the concept of systems autonomy2 provides a sufficient theoretical justification for the perturbation-compensation mode of adaptation to be derived from a dynamics weaker than biological autopoiesis. What is needed for such an application is merely understanding the dynamic of systems as structurally defined, i.e., that they will not be able to produce any consequent behavior which is not encoded already in their current

E:CO 2016 18(2): 55-89 | 71

structure and state. And there are already numerous examples of how such adaptation may be modeled and observed in social systems55,56,57,58. To find out which explanation of systems adaptability can be in fact more useful for our framework, let us consider how each of these two theories might shape on its own terms our observations of the boundary of our exemplary social system.

Concept 4.1: Responsive adaptation

T

he example below explores the application of the ‘responsive adaptation’ approach.

Communication a (2002)

Communication b (2006)

Communication c (2011)

To understand and protect our home planet; To explore the Universe and search for life; To inspire the next generation of explorers …as only NASA can73

To pioneer the future in space exploration, scientific discovery, and aeronautics research78

Drive advances in science, technology, and exploration to enhance knowledge, education, innovation, economic vitality, and stewardship of Earth77

Example 4.1 Responsive adaptation of a mission statement (NASA) As proposed in the Example 3.1., the simplest computational way for charting of the system's environment is to identify the nouns which have been used: Communication a (2002)

Communication b (2006)

Communication c (2011)

Home planet; Universe; Life; Next generation of explorers

Future; Space exploration; Scientific discovery; Aeronautics research

Advances; Science; Technology; Exploration; Knowledge; Education; Innovation; Economic vitality; Stewardship of Earth

Now, let us observe the adaptive changes that are noteworthy. Especially between the year 2002 (a) and 2006 (b) the system's Hayek-Hebbian model of its environment (i.e., the pattern which emerges through repetitive interactions with the environment and gets updated through responsive adaptation) has become very different. First, the ‘home planet’ as a part of NASA's environment, has disappeared altogether. This change has been noticed, debated, and criticized by many at that time, e.g.:


Social systems: Complex adaptive loci of cognition Communication d

[..] the change comes as an unwelcome surprise to many NASA scientists […] Without it, these scientists say, there will be far less incentive to pursue projects to improve understanding of terrestrial problems like climate change caused by greenhouse gas emissions.59

Explained in the cybernetic terms, the Hayek-Hebb responsive adaptation happens because the system's map of the environment gets updated as a result of a particular pattern of system-environment interactions getting weakened and another— strengthened. It suggests that, prior to the shift within the model, there must have been a weakening in NASA's everyday interactions with the ‘home planet’, as an object of its investigations. The author of the newspaper analysis quoted above seems to have observed this very process: Communication e

The shift in language echoes a shift in the agency’s budgets toward space projects and away from earth missions, a shift that began in 2004, the year Mr. Bush announced his vision of human missions to the Moon and beyond.59

Moreover, in 2002 NASA appears to be interacting with and building representations of the very objects of its explorations (the home planet, universe, life) and those in the future who will carry the activity on (future explorers). However, in 2006 it is interacting with and building representations of much more abstract phenomena: the future, exploration, discovery, and research. It seems as if the exploratory activities, which were previously placed within the boundaries of the system, have now been exported outside, to the environment which is being mapped. We could speculate that this particular shift may be attributed to the spectacular increase of the NASA's ‘selfawareness’ which started after Columbia disaster in 2003. Not only NASA's organization, with ‘the very essence of what the NASA family holds so dear60, has been found then to be blamed, but it was also claimed to have demonstrated a failure ‘to learn from its previous mistakes', which caused the Challenger catastrophe in 198661. The dramatic increase in the number of interactions addressing the system's own operating has most probably led to a ‘rewiring’ of its Hayek-Hebb model of the environment, making the NASA's exploratory activities a central part of it. Interestingly, the 2006 shift to a second-order auto-observation did not reverse. It has become even more apparent in the next time-step presented above, co-occurring with the progressing privatization of NASA's missions and the public debate on outsourcing of NASA's task62. While concepts employed in the analysis presented above -such as interactions and mapping- do not easily fit as yet to our line of argument, the analysis seems to

E:CO 2016 18(2): 55-89 | 73

be a good example of how the modelling of social systems’ adaptation might be performed when based on the Hayek-Hebb approach. The vocabulary may be adjusted, but there is an already apparent flaw that will not be easily overcome: the map-making system, which is interacting with its environment, is clearly being approached here with an object-based Aristotelian manner, not the process-based Heraklitian one. The resulting analysis of the adaptive shifts in NASA mission statements takes the existence of the system-environment boundary, whose emergence we wanted to observe, already as a given: the relation between the map and the mapped territory, as well as the interaction between the mapped and the mapping, would be impossible without a clear a priori delineation between the two. Once we will adjust the vocabulary in the following, the limitation of the Aristotelian point of departure of this analysis will become even more apparent. This is not to say that such modelling cannot be useful. In fact, the example 4.1. presents a simplified, potentially computable variant of one of the most prominent approaches to the social reality which is extensively used in the humanities: analyzing it as constituted of texts, discourses, or stories. Such an analysis may be particularly helpful for capturing the relationships between the investigated fraction of the social reality (delineated a priori as the organization of interest) and what we have identified as ‘the first layer’ of its environment: the ‘whatever communication refers to or is being referred to', which can be understood as Hayek-Hebb's mapping. In that sense a social system, such as an organization, can be seen as a story, a narrative about how the world is to be approached, as seen through the lenses of that system. But it is apparent that approaching the adaptability of sequences of communication in the Hayek-Hebb manner, necessarily reduces our three-layered environment (referred— communications—humans) to the single aspect of the ‘referred’ only. Consequently, since the map-making is considered to be an outcome of interactions between the system and its environment, this approach to adaptation requires an identification of an agent of such actions on the system's side. That is, agents that participate in occurrences of communication in such way that their manner of participation in future occurrences changes as a result. The third human layer of our environment is thus necessarily called upon to be the agent, while the agency of the construct called ‘NASA’ remains only a metaphorical, totum pro parte, rhetorical figure. This particular result also does not have to be necessarily problematic: in fact, locating of all agency in this way reflects a cherished cornerstone assumption of the western worldview in general. What is most problematic, however, is that such positioning of the first (referred) and the third (human) layers necessarily flattens and instrumentalizes the middle one: the one that consists of other occurrences of communication. As a result, a large part of



the environment in which the individuation of communication happens, encompassing all intertwined triple-selection-making instances of communication that relate to the individuating sequence, disappears from our sight and get clearly cut into two distinct sets. The first is a set of instances considered to be external to the system, which may potentially become the ‘referred’ parts of the environment (e.g., the new vision announced by Mr. Bush in the example above). The second set of internal instances are employed by the system in its interaction with the environment (e.g., the mission statement). The former set merges with the environment, which is being mapped, and the latter become the mapmaking tools: extensions of human agents. Our ‘middle layer’ of the environment—the fabric of occurrences of communication, out of which social systems individuals as such—disappears from our sight. The above effect (of the ‘middle layer’ being lost, as an environment) is not an error of perception, but a consequence of the fact that, when the Hayek-Hebb ‘interacting—mapping’ vocabulary is applied to explain the adaptability of social systems, the system-environment boundary is assumed to exist a priori, separating the mapmaking interior from what is being mapped. If only one aspect of the environment (i.e., whatever can be mapped) is selected to be considered an environment, another aspect of the environment (i.e., human agents) gets to be granted the agency of the mapmaking. As a result, the entire domain fashioned out of occurrences of communication becomes a mediator between the two, a transparent lens, cropped to fit the arbitrary contour of the agents’ group—and used by them as a means of engagement with their exterior. The notion of adaptability of a social system, in relation to its milieu, gets equated with an adaptability of a group of agents, who use communication to capture and update their own shared or merged worldview. Thus, approaching social systems adaptability by highlighting one (referred), downplaying second (communication) and super-powering the third (human) of the three crucial layers of the environment, in relation to which adaptation takes place, necessarily positions the adaptability of social systems as a function of the adaptability of humans. While such an approach may help to investigate cognition and behavior of human beings, when grouped, it fails to encourage the investigation of social complexity as the realm in which not only individuation of humans, but also an individuation of communication-constituted assemblages might be taking place. By failing to address the full complexity of the social realm, it reduces our conceptual capacity to account for emergent phenomena, which to a large extent might be actually shaping the human condition. We conclude therefore that the Hayek-Hebb theoretical approach, however useful it might be for certain purposes, does not allow us to track the emergence of the boundary between an individuating sequence of occurrences of communication and E:CO 2016 18(2): 55-89 | 75

its multi-layered environment, let us test how the other, ‘reactive’ account of adaptation could be used to reveal this dynamics.

Concept 4.2: Reactive adaptation

T

he ‘reactive adaptation’ approach posits that operational responses of a system in relation to external changes (perturbations) depend only on the inner structure and the state of the system and can only induce further changes to its inner structure and state. To observe reactive adaptability in sequences of communication, we should therefore look for instances in which an occurrence of communication (X) points to some other -previous- occurrences (Y), using their selections as a rationale for the way it refers/responds to a change (a perturbation). It is important to note that the criterion of the selection of Y, as an orientation point to be used by X, already indicates that both occurrences ‘belong’ to the same sequence of communications. Finally, the resulting way in which X refers to its changing environment gets to be addressed by consequent occurrences of communication (Z), which clearly refer to changes that have been indicated by X within the entire sequence by the manner it has referred to the external change. And, again, the later occurrence of communications Z is justified on the grounds of the X's and Z's mutual ‘belonging’ to the same sequence. Generally speaking, when a reactive adaptation is taking place, we can expect that a change happening in the environment of a sequence will lead to the occurrence of a number of communications within this sequence, which refer to the composition of the sequence itself. Let us see if we can detect such a pattern in the NASA case. In contrast to the attempt described in the Example 4.1, this time there is no need to arbitrarily delineate any foundational set of communications (like the organization's mission statements) and treat them as the focal window through which the system's interactions with its environment and the mapping operations are happening. It is enough to start with any communication that appears to be performing either the function of the communication X, or Z, as described in the general pattern above, and to follow back and forth in time the mutual co-referring in which the specific chosen communication is participating. We start, then, with a communication, which seems to display the characteristic of the Z type:


Social systems: Complex adaptive loci of cognition Communication a

“We refer to the mission statement in all our research proposals that go out for peer review, whenever we have strategy meetings,” said Philip B. Russell, a 25-year NASA veteran […]. “As civil servants, we’re paid to carry out NASA’s mission. When there was that very easy-to-understand statement that our job is to protect the planet, that made it much easier to justify this kind of work.”59

Example 4.2 Reactive adaptation of a mission statement (NASA) If we attribute the characteristics of Z to this entire occurrence of communication, we can see the proposals “that go out for peer review” as communications X and, then, the NASA mission statement as Y. Selections apparent within the Y (here: the 2006 version of the mission statement) are explicitly described as constraining the possible ways in which NASA project proposals are allowed to relate to various calls and emerging research opportunities. Should there be a different Y, like the mission statement from 2002, the relating would be “much easier”. And, naturally, the above attributions of our X/Y/Z functions are by no means permanent. The 2006 mission statement, playing the role of Y for our current X (research proposals), have also had the function of X, when it was relating to a change (e.g., the Mr. Bush's announcement made in 2004, mentioned in the communication e, Example 4.1.) and using the previous version of the NASA mission statement as its own constraining Y. At that moment, however, a perturbation appears to have happened: the 2006 mission statement has rather ‘responded’ than ‘reacted’ to the new vision and funding ideas announced by the Bush administration. It has enforced coherence with the political vision and loosened the coherence with other communications already present in the NASA sequence. By this, the new mission statement has perturbed the sequence. It is exactly at such moments, when patterns of the reactive adaptation may be observed in sequences which operate as already individuated, autonomous social systems. The perturbation induced the production of new occurrences of communication, compensatory ones, seeking to re-establish the coherence of the entire sequence. The ‘NASA veteran's’ criticism publicized in the mass media is one example of such communications and, as we already know, in this particular case there were many more communications like this one. In fact, the counterbalance continued until the home planet concept was reintroduced in the 2011 ‘stewardship of Earth’ phrase and the internal coherence of the system was restored.

E:CO 2016 18(2): 55-89 | 77

What still remains to be addressed, is the identification problem: how do various occurrences of communication ‘identify’ each other, as belonging, or not belonging, to the same sequence? Why the vision announced by Mr. Bush could not be accepted as a valid communication of the Y type? Why did adhering to this particular communication work as an external perturbation, while adhering to the NASA mission statement by research proposals did not? Taking this back to the triple selection structure, that each communication is made of, we can see that it is impossible to equate this identification making with only one type of selection: it seems to be present in all of them. Communications that are most likely to be considered as unambiguously sourced within the NASA system are both meant (selection 1) and understood (selection 3) as exactly that: communications of NASA. And, while there are many subtler ways in which this can be manifested in their form (selection 2), one of the most common forms of manifestation of such belonging is their labelling with a name, which signifies the social system they belong to. Thus, the name of NASA is used as a signifier, which selects the entire sequence of NASA communications. We can see that the above method of observing social systems adaptiveness does not require the definite assumption of the prior existence of an already individuated NASA, as it was the case in the ‘responsive adaptation’ attempt discussed in example 4.1. Such an assumption is in fact much weaker here: it is rather an hypothesis being explored, not a presupposition. For this reason the ‘reactive adaptation’ framework seems to be much better aligned with the process ontology perspective of social systems as it accommodates varying levels of coherency and dynamic becomings with various degrees of individuation. Should a pattern of reactive adaptation be detected in such a fluid realm, this may imply (prove) a temporary existence of an individuated sequence, coherent enough to display an adaptive behavior. Therefore, while the ‘responsive adaptation’ approach appears to require such existence to be assumed a priori, the ‘reactive adaptation’ approach may indeed become useful for its verification. Providing a sound operationalization of the above method, the ‘reactive adaptation’ approach has thus a potential of forming the methodological basis for tracking and delineating the dynamics of social systems (organizations, movements, groups, etc.) within big unstructured and unordered datasets of various occurrences of communication, such as abundantly found on the Internet. Another significant difference between the two discussed methods of understanding systems’ adaptation lies in how each approach influences our perspective of the environment of communication. While in the responsive adaptation approach



the middle layer of the environment, i.e., the one constituted of all other instances of communication, was remarkably lost from our sight, the reactive approach renders this layer as the key one. It becomes clear that this layer is the environment within which the individuation of communication-constituted systems actually takes place. The fluid, processual milieu populated by various occurrences of communication is exactly where the boundaries of the individuating assemblages are formed. It happens by distinguishing between the communications that belong to or owned by a specific system and those which do not. In the reactive approach, the other two layers of the environment of communication—the referred reality and the human minds involved in the process—could be to some extent disregarded as we focus on the individuating process. This time, it seems that these layers are becoming ‘flattened and instrumentalized', just as the middle layer of the environment turned out to be in the responsive adaptation approach. If we consider the adaptability of communication-constituted sequences to be happening in a reaction to the selection-making ‘behavior’ of the surrounding occurrences of communication, we may conclude that the worldviews—the Hayek-Hebb's ‘maps’ of reality being ‘interacted with’ via symbolic communication—are ultimately mere byproducts of the self-organizing activity taking place in the domain of communicative occurrences. Human minds, as the cognitive agencies necessary for selection-making, are involved quite instrumentally: they monitor the coherencies and incoherencies in particular sequences of communicative occurrences and perform selections that increase the former and decrease the latter. The map of the environment presented to them is an outcome of previous operations. The minds involved are guided not so much by the actual ‘territory of reality’ and its dynamics, as by the internal composition and coherence of particular sequences of communication, that fold and unfold with various degrees of arbitrariness. Whether or not employees of NASA will ultimately relate to the ‘home planet’ in the communications that constitute their work, does not depend, then, on whether the planet is actually there, in their environment. It depends solely on the internal structure of the communication-constituted social system which orients, mediates, and guides such activities. The social system may enable or disable certain activities, make them irrelevant, undesirable, etc. All these based only on the unfolding over time of the sequence which constitutes it. While not denying that the cognitive agency of human minds is instrumental for the operation of social systems (at least until sufficiently replaced by communicating machines as it is already happening), we can clearly see these human agencies as mobilized in the environment of communication by communication itself. Following this line of reasoning, we are in a position to address the question can the agency asE:CO 2016 18(2): 55-89 | 79

sociated with communication systems, as clearly demonstrated by our approach, be designated as cognitive?

Concept 5: Cognition

S

o far we have advocated three shifts in approaching social systems. First: focusing on becoming rather than being as what gives rise to their complexity. Second: a triple-layered understanding of the environment in which (more or less fluid) identities of social systems individuate and shape their own boundaries. And third: a reactive rather than responsive approach to their adaptiveness. Our final argument is that these shifts reveal social systems as cognitive systems. This position does not stretch Luhmann's own thinking too far. He frequently described a social system's activity in its environment using terms such as ‘observing’ and ‘coding’1,15,16. We wish, however, to address this more explicitly and argue that this is in no way merely a metaphor: a communication-constituted social system is a cognitive system and its on-going constitution is a process of cognitive development. We will make the argument in two steps. First, we argue that all individuating processes, inasmuch as they are boundary and distinction forming processes, can be considered as processes of cognitive development. With that we generalize the concept of cognition following the enactive cognition approach2. Second, we use this approach to explicate the intrinsic cognitive nature of communication-constituted social systems. The phenomenon of cognition is definitely complex, multifaceted and gives itself to quite a few diverse definitions. Still, in a somewhat limiting approach, the activity of cognition is naturally associated with certain situations when there is an agent operating in its environment, and whose operation can be described as an on-going problem-solving activity. The question remains however how is it that this setup of agents, environments and their dynamic problematic relations emerge in the first place? Even while writing (or reading) these words, we make use of sensible objects that are already, at least partially, formed and related to each other. Perhaps they are vague and require further determination to become clearer; some may change the meaning (sense) in which they are understood; others may just emerge in the ?ow of thought or disappear; and yet others may merge or diverge. Crossing this, often unseen, boundary between the unknown and the known, the unformed and the formed is what we may call sense-making. Sense-making is the bringing forth of a world of distinctions, objects and entities and the relations among them. Even primary distinctions such as ‘objective-subjective’ or ‘self-other’ are part of sense-making. A relatively new appearance on the stage of 80 | Lenartowicz, Weinbaum & Braathen


cognitive science, the so called enactive cognition approach, regards sense-making as the primary activity of cognition. The term ‘enactive', synonymous with ‘actively bringing forth', makes its first appearance in the context of cognition in the book “The embodied Mind”81 and has been since then the subject of many developments and debates2,83,84,82). A guiding idea of the enactive approach is that any adequate account of how the body (i.e., any embodied system) can either be or instantiate a cognitive system must take account of this fact that the body is self-individuating: [...] By saying that a system is self-constituted, we mean that its dynamics generate and sustain an identity. An identity is generated whenever a precarious network of dynamical processes becomes operationally closed. [...] Already implied in the notion of interactive autonomy is the realization that organisms cast a web of significance on their world. [...] This establishes a perspective on the world with its own normativity[.]2

The enactive theory of cognition therefore incorporates the idea of individuation rather naturally as it asserts cognition to be an on-going formative process, sensible and meaningful (value related), taking place in the co-determining interactions (i.e., communications in our case) of agents and their environment2. We assert that the concept of sense-making captures two distinct meanings: the first is synonymous with cognition as a concrete capacity of an already individuated system, the second, with the individuation of cognition as intrinsic to cognition itself. The latter meaning of sense-making is the one corresponding to the acquisition and expansion of concrete cognitive capacities and it also generalizes the concept of cognitive development beyond its psychological context85 and make it applicable to general individuating systems38. Furthermore, in the broadest sense, every individuation process where boundaries, distinctions and relations are progressively determined, is a sense-making process and therefore is cognitive. Still, being based on the earlier works of Maturana and Varela on autopoiesis and the biological basis of cognition7,80, the theory of enactive cognition asserts the necessity of an autonomous and relatively stable identity to sense-making. In contrast, we argue that the broader understanding of cognition as sense-making precedes the existence of systems as already individuated identities (cognitive agents) and is actually a necessary condition to their becoming. Only that at this pre-individuated stage there is still no one for whom sense is being made. It is only a habit of thought grounded in an ontology of fully defined individuals to assume the pre-existence of the sensemaking agent to the emergence of the sensible.

E:CO 2016 18(2): 55-89 | 81

By ‘cognition of a social system’ we do not imply the experience of the human ‘ANTCOG', i.e., adult, normal, typical cognition63, being projected onto a distributed social phenomenon. Our understanding of cognition derives from the broader sense of social systems as individuating systems that enact sense-making via on-going communications. They make and manipulate distinctions which shape the system's unique perspective(s) of its environment, of itself in it and the resulting relations that are possible between the two64. Since forging of a perspective of the environment, of an own identity, and of possible relations between the two are the core characteristics of the cognitive, at least according to the enactive approach, the operation of social systems is just that—cognitive. Even more importantly, if cognitive development is intrinsic to cognition as argued by Weinbaum & Veitas38,39, cognizing is not only a core activity of social systems but also a vehicle for their evolution. As a cognitive system, a social system is distributed yet embodied and situated once these designations are understood in the proper context. First, we need to release the associations of such designations from their narrow physical or topological interpretation. Embodiment can be understood as a combination of the ‘raw material’ constituents, in our case communication instances, and their coordinated organization, in our case the way communications are related and associated reflecting complex distinct structures. The situatedness of a social system can be understood as the totality of its immediate interactions over already established boundaries. In other words, the situation of the system is the immediate circumstances of enacting its sense-making. Of course for social systems both embodiment and situatedness are distributed and fluid. In a communication-constituted operational domain, the process of individuation may be initiated by a difference of strength of association between a few contingent communications39. A recurrent set of occurrences of communication which are more or less consistent and coherent constitutes a semiotic boundary or part of it. Associative relations among signifier-signified pairs may bring forth temporal patterns of interaction across the boundary where patterns may themselves become the object of further recursive significations. Such associations and significations may be dynamically reinforced or weakened depending on some fitness criteria that are encoded in communications as well. An example might be a case of several scientific papers, published by different researchers in different parts of the world, using a similar set concepts to denote similar phenomena, which was rarely described before. Such a pattern may be strong enough to deserve a communication that encodes it with a name, i.e., a new signifier. The sig-



nifier, selecting the entire group of these communications as its signified, may then become a frequent element of subsequent communications. It comes along with a network of associations that imply a more or less diverse and more or less stable set of meanings depending on the phase of individuation of the system. Luhmann16 refers to such progressive encoding of patterns into new signifiers as a process of ‘self-description’ but one should not read in that any kind of self-reflectivity or self-awareness in the experiential sense but rather spontaneous instances of compression of recurrent information patterns and the progressive consolidation of identity thereof. With such individuating activities, by repeatedly linking signifiers and signified the social system maintains its own continuity and coherence even in the face of changing circumstances and values. Specifically, it can be said to possess (quasi-stable) perceptions, actions and conceptions (percept-action associations) that dynamically bind them. The system thus becomes a locus of identifiable cognitive activity, temporarily stabilized within the flux of communication.

Conclusions

W

hile the biologically embodied cognitive system of humans appears to be the most advanced one on Earth, it may be a mistake to reduce the collective cognitive processes performed by human societies to mere aggregations of the cognitive activities performed by human individuals. In this paper we made the case that human social systems are concrete, non-metaphorical, cognitive agencies in themselves and are operating within their own self-constructed environments. Furthermore, we make it visible that, though not biologically embodied, these cognitive agencies self-organize and operate in a way with characteristic similarities to many self-organizing processes of life and specifically the individuation of human cognitive competences. Our explication of how this happens derives from Niklas Luhmann's theory of social systems. We find Luhmann's focus on communication, instead of communicating people, to be a close derivation of the ancient Heraklitian view of reality as ontologically constituted of processes and not objects. This is naturally integrated with Gilbert Simondon's theory of individuation and readily applied to the individuation of cognitive systems. This results in a novel view of social systems as complex sequences of occurrences of communication, which are capable of becoming consolidated to the degree in which they start to display an emergent adaptive dynamics characteristic to cognitive systems.

E:CO 2016 18(2): 55-89 | 83

While adhering with the prevailing view of social systems as complex adaptive systems (CAS), we offer a novel re-interpretation of the applicability of both complexity and adaptability to social systems. The three major conceptual building blocks of CAS, namely, components, environments, and adaptability are discussed and we demonstrate how they may be conceptualized in a manner consistent with our claims. Components: We follow Luhmann's view on social systems as constituted of communications. We assume that an occurrence of communication happens as a combination of three difference-making selections: the selection of information, the selection of the utterance of this information, and the selection of understanding (meaning assignment) of this utterance. Environment: We distinguish three conceptual layers of the environment of communication engaged by the occurrences of communication via the above selections: (1.) the selections of information rendered by each communication, i.e., ‘whatever the communication refers to and is being referred to', (2.) the milieu consisting of other related occurrences of communication, and (3.) the mental (and possibly technological) agencies whose selections for coherency facilitate the emergence of communication instances. Adaptability: We clarify the relations of social agencies with their respective multi-layered environments by examining two dominant perspectives on systems adaptiveness, which we refer to as ‘responsive’ and ‘reactive'. Here, we discover that while each displays different and complementary facets of the operation of social systems, only the latter reveals social systems to be the loci of an autonomous agency.

The final argument affirms that this distributed social agency is indeed cognitive. The argument derives from a broader understanding of cognition as sense-making, which precedes the existence of a consolidated cognitive agent to whom we could conventionally attribute the activity of sense-making. Instead, we see the cognitive activity as a formative process, which actually brings forth actual agents. This brings us to conclude that though there is `nobody there’ in the conventional sense, human social systems constitute distributed yet coherent loci of an autonomous cognitive activity.

References 1. Luhmann N. (1996). Social Systems, ISBN 9780804726252. 2. Di Paolo, E., Rohde, M. and De Jaegher, H. (2010 “Horizons for the enactive mind: Values, social interaction, and play,” in J. Stewart, O. Gapenne, A.I. Di Paolo (eds.), Enaction: Towards a New Paradigm for Cognitive Science, ISBN 9780262014601. 84 | Lenartowicz, Weinbaum & Braathen


3. Simondon G. (1992). “The genesis of the individual,” in J. Crary, S. Kwinter (eds.), Zone: Incorporations 6, ISBN 9780942299298. 4. Hebb D.O. (1949). The Organization of Behavior, ISBN 9780805843002. 5. Hayek F. (1999). The Sensory Order: An Inquiry into the Foundations of Theoretical Psychology, ISBN 9780226320946. 6. Edelman, G. (1987). Neural Darwinism: The Theory of Neural Group Selection, ISBN 9780465049349. 7. Maturana, H. and Varela, F. (1980). Autopoiesis and Cognition: The Realization of the Living, ISBN 9789027710161. 8. Simon, H. (1962). “The architecture of complexity,” Proceedings of the American Philosophical Society, ISSN 0003-049X, 106(6): 467-482. 9. Hutchins, E. (1995). Cognition in the Wild, ISBN 9780262581462. 10. Tollefsen, D.P. (2006). “From extended mind to collective mind,” Cognitive Systems Research, ISSN 1389-0417, 7(2-3): 140-150. 11. Theiner, G. and Sutton, J., (2014). “The collaborative emergence of group cognition,” Behavioral and Brain Sciences, ISSN 0140-525X, 37(3): 277-278. 12. Theiner, G., (2015). “Group-sized distributed cognitive systems,” in K. Ludwig, M. Jankovic (eds.), The Routledge Handbook of Collective Intentionality, ISBN 9781138783638. 13. Lenartowicz, M., (2016). “Creatures of the Semiosphere: A problematic third party in the ‘humans plus technology’ cognitive architecture of the future global superintelligence,” Technological Forecasting and Social Change, ISSN 0040-1625, in press. 14. Heylighen, F. and Beigi, S., (2016). “Mind outside Brain: a radically non-dualist foundation for distributed cognition,” in A. Clark (ed.), Socially Extended Epistemology, Oxford University Press, in press. 15. Luhmann N. (2012). Theory of Society, Vol. 1, ISBN 9780804739504. 16. Luhmann N. (2012). Theory of Society, Vol. 2, ISBN 9780804771603. 17. Buckley W. (1968). “Society as a complex adaptive system,” in W. Buckley (ed.), Modern Systems Research for the Behavioral Scientist, ISBN 9780202300115, pp. 490-513. 18. Miller J, and Page, S. (2007). Complex Adaptive Systems: An Introduction to Computational Models of Social Life, ISBN 9780691127026. 19. Moeller, H.G. (2011). The Radical Luhmann, ISBN 9780231153799. 20. Deleuze, G. (1990). The Logic of Sense, ISBN 9780231059831. 21. Deleuze, G. (1994). Difference and Repetition, ISBN 9780231081597. 22. Weinbaum, D.R. (2015). “Complexity and the philosophy of becoming,” Foundations of Science, ISSN 1572-8471, 20(3): 283-322. 23. Turchin, V. (1993). “The cybernetic ontology of action,” Kybernetes, ISSN 0368-492X, 22: 10-30.

E:CO 2016 18(2): 55-89 | 85

24. Metzinger, T. and Gallese, V. (2003). “The emergence of a shared action ontology: Building blocks for a theory,” Consciousness and Cognition, ISSN 1053-8100, 12(4): 549571. 25. Heylighen, F. (2011). “Self-organization of complex, intelligent systems: An action ontology for transdisciplinary integration,” http://pespmc1.vub.ac.be/papers/ECCOparadigm.pdf. 26. Heylighen, F., Beigi, S. and Veloz, T. (2015). “Chemical organization theory as a universal modeling framework for interaction, self-organization, and autopoiesis,” http://pespmc1. vub.ac.be/Papers/COT-applicationsurvey.pdf. 27. Rescher, N. (1996). Process Metaphysics: An Introduction to Process Philosophy, ISBN 9780791428184. 28. Whitehead, A. (1978). Process and Reality: An Essay in Cosmology, A, ISBN 9780029345702. 29. DeLanda, M. (2005). Intensive Science and Virtual Philosophy, ISBN 9780826479327. 30. Luhmann, N. (2002). Theories of Distinction, ISBN 9780804741231. 31. Peirce, C.S. (1931). The Collected Papers of Charles Sanders Peirce, ISBN 9781570851858. 32. Peirce, C.S., and Welby, V. (1977). In Semiotic & Significs: The Correspondence Between Charles S. Peirce & Victoria Lady Welby, ISBN 9780253351630. 33. Latour, B. (2007). Reassembling The Social: An Introduction to Actor-Network-Theory, ISBN 9780199256051. 34. Latour, B. (2007). “Can we get our materialism back, please,” ISIS, ISSN 0021-1753, 98(1): 138-142. 35. Cohen, S., Kimelfeld, B., and Koutrika, G. (2012). “A survey on proximity measures for social networks,” in S. Ceri, and M. Brambilladoi (eds.), Search Computing: Broadening Web Search, ISBN 9783642342134. 36. Akcora, C.G., Carminati, B., and Ferrari, E. (2013). “User similarities on social networks,” Social Network Analysis and Mining, ISSN 1869-5469, 3(3): 475-495. 37. 37.\tAdali, S., Escriva. R., Goldberg. M.K., Hayvanovych. M., Magdon-Ismail, M., Szymanski, B.K., Wallace, W.A., and Williams, G.T. (2010). “Measuring behavioral trust in social networks,” Intelligence and Security Informatics: IEEE International Conference on Intelligence and Security Informatics, ISBN 9783540320630, pp. 150-152. 38. Weinbaum, D.W, and Veitas, V. (2016). “Synthetic cognitive development: where intelligence comes from,” https://arxiv.org/pdf/1411.0159.pdf. 39. Weinbaum, D.W. and Veitas, V. (2016). “Open ended intelligence: the individuation of intelligent agents,” https://arxiv.org/abs/1505.06366. 40. Spencer-Brown, G., (1969). Laws of Form, ISBN 9780517527764. 41. Lotman, J.M. (1990). Universe of the Mind: A Semiotic Theory of Culture, ISBN 9780253214058. 42. Saussure, F. de. (1974). Course in General Linguistics, ISBN 9780812690231.



43. Tayla, L., and Bingul, F., (2007). “NASA stands ‘for the benefit of all’: Interview with NASA’s Dr Suleyman Gokoglu,” http://www.lightmillennium.org/2004_newyear/gokoglu_nasa_ stands_forall.html. 44. Seidl, D. (2005). Organizational Identity and Self-Transformation: An Autopoietic Perspective, ISBN 9780754644583. 45. Aerts, D., Apostel, L., De Moor, B., Hellemans, S., Van Belle, H., and Van der Veken, J. (1994). “World views: From fragmentation to integration,” http://www.vub.ac.be/CLEA/ pub/books/worldviews.pdf. 46. Vidal, C. (2008). Wat Is Een Wereldbeeld, ISBN 9789033470783. 47. McQuade, T.J., and Butos, W.N. (2009). “The adaptive systems theory of social orders,” Studies in Emergent Order, 2: 76-108. 48. Heylighen, F. (2014). “Challenge propagation: Towards a theory of distributed intelligence and the global brain,” Spanda Journal, ISSN 2210-2175, 2: 51-63. 49. Dittrich, P., and Speroni di Fenizio, P. (2007). “Chemical organization theory,” Bulletin of Mathematical Biology, ISSN 1522-9602, 69(4): 1199-1231. 50. Ashby, W.R. (1962). “Principles of the self-organizing system,” in H. Foerster, G.W. Von Zopf Jr. (eds.), Principles of Self-Organization, Oxford: Pergamon Press. 51. Bertalanffy, L Von. (1968). General Systems Theory, ISBN 9780807604533. 52. Watson, R.A., Mills, R., and Buckley, C.L. (2011). “Global adaptation in networks of selfish components: emergent associative memory at the system scale,” Artificial Life, ISSN 1064-5462, 17(3): 147-166. 53. Mingers, J. (2007). “The problems of social autopoiesis,” International Journal of General Systems, ISSN 0308-1079, 21(2): 197-205. 54. Cadenas, H. and Arnold, M. (2015). “The autopoiesis of social systems and its criticisms,” Constructivist Foundations, ISSN 1782-348X, 10(2): 169-176. 55. Seidl, D. (2006). Organizational Identity and Self-Transformation: An Autopoietic Perspective, ISBN 9780754644583. 56. Holdschlag, A. and Ratter, B. (2013). “Multiscale system dynamics of humans and nature in the Bahamas: Perturbation, knowledge, panarchy and resilience,” Sustainability Science, ISSN 1862-4065, 8(3): 407-421. 57. Lenartowicz, M. (2015). “The nature of the university,” Higher Education, ISSN 1573-174X, 69: 947-961. 58. Shaw, M., Lenartowicz, M. (2016). “Humboldt is (not) dead: A social systems perspective on reforming European universities,” in R.J. Leemann, C. Imdorf, J. Powell, and M. Sertl (eds.), Die Organisation von Bildung. Soziologische Analysen zu Schule, Berufsbildung, Hochschule und Weiterbildung, ISBN 9783779915935, pp. 272-285 59. Revkin, A.C. (2006). “Nasa’s goals delete mention of home planet,” http://www.nytimes. com/2006/07/22/science/22nasa.html.

E:CO 2016 18(2): 55-89 | 87

60. O’Keefe S. (2005). “Preface,” in W. Starbuck and M. Farjoun (eds.), Organization at the Limit: Lessons from the Columbia Disaster, ISBN 9781405131087. 61. Hall, J.L. (2003). “Columbia and Challenger: Organizational failure at NASA,” Space Policy, ISSN 0265-9646, 19: 239-247. 62. Pasztor, A. (2010). “White house decides to outsource NASA work,” http://www.wsj.com/ articles/SB10001424052748704375604575023530543103488. 63. Eckardt, B. Von (1995). What Is Cognitive Science? ISBN 9780262220460. 64. Heylighen, F. (1990). “Autonomy and cognition as the maintenance and processing of distinctions,” in F. Heylighen, E. Rosseel, and F. Demeyere (eds.), Self-Steering and Cognition in Complex Systems, ISBN 9782881247293. 65. Heylighen, F. (2009). “The science of self-organization and adaptivity,” in L.D. Kiel (ed.), Knowledge Management, Organizational Intelligence and Learning, and Complexity, ISBN 9781848269132, pp. 253-280. 66. Heylighen, F. (2014). “Cognitive systems: A cybernetic perspective on the new science of the mind,” http://pespmc1.vub.ac.be/papers/cognitivesystems.pdf. 67. Heylighen, F., Heath, M., and Van Overwalle, F. (2004). “The emergence of distributed cognition: A conceptual framework,” http://pespmc1.vub.ac.be/papers/distr. cognitionframework.pdf. 68. Searle, J.R. (2006). “Social ontology: some basic principles,” Anthropological Theory, ISSN 1741-2641, 6(12): 12-29. 69. Space Quotations. “Into orbit quotes,” http://www.spacequotations.com/orbit.html. 70. DeLanda, M. (2006). A New Philosophy of Society: Assemblage Theory and Social Complexity, ISBN 9780826491695. 71. NASA (1968). “Public Law #85-568: U.S. National Aeronautics and Space Act of 1958,” http://history.nasa.gov/spaceact.html. 72. Quora. “What is NASA motto?” https://www.quora.com/What-is-NASAs-motto. 73. NASA (2002). “NASA Vision,” http://www.hq.nasa.gov/office/codez/plans/Vision02.pdf. 74. Berger, P.L. and Luckmann, T. (2011). The Social Construction of Reality: A Treatise in the Sociology of Knowledge, ISBN 9780385058988. 75. Wittgenstein, L. (2009). Philosophical Investigations, ISBN 9781405159289. 76. Giddens, A. (1986). The Constitution of Society: Outline of the Theory of Structuration, ISBN 9780520057289. 77. NASA (2011). “2011 NASA Strategic Plan,” https://www.nasa.gov/pdf/516579main_ NASA2011StrategicPlan.pdf. 78. NASA (2006). “2006 NASA Strategic Plan,” http://www.nasa.gov/pdf/142302main_2006_ NASA_Strategic_Plan.pdf. 79. “NASA,” in Wikipedia, https://en.wikipedia.org/w/index.php?title=NASAandold id=725727501.



80. Maturana, H.R., and Varela, F.J. (1987). The Tree of Knowledge: The Biological Roots of Human Understanding, ISBN 9780877736424. 81. Varela, F.J., Rosch, E., and Thompson, E. (1992). The Embodied Mind: Cognitive Science and Human Experience, ISBN 9780262220422. 82. Jaegher, H.D., and Paolo, E.D. (2007). “ Participatory Sense-Making: An enactive approach to social cognition,” Phenomenology and the Cognitive Sciences, ISSN 1572-8676, 6(4): 485-507. 83. Thompson, E. (2007). Mind in Life: Biology, Phenomenology, and the Sciences of Mind, ISBN 9780674057517. 84. Di Paolo, E. (2006). “Autopoiesis, adaptivity, teleology, agency,” Phenomenology and the Cognitive Sciences, ISSN 1572-8676, 4(4): 429-452. 85. Piaget, J. (2013). Principles of Genetic Epistemology: Selected Works, ISBN 9780415168908.

E:CO 2016 18(2): 55-89 | 89

A cladistics and Linnaean exploration into the Darwinian selection of favorable varieties of the ideal / textbook manufacturing species Christen Rose-Anderssen University of Sheffield, ENG Dr. C. Rose-Anderssen was involved in the project ‘Cooperation Environment for Rapid Design, Prototyping and New Integration Concept for the Factory of the Future’ at Advanced Manufacturing Research Center with Boeing, the University of Sheffield, UK. Previously, he was a Research Associate at the AMRC. He was engaged in the ESRC research project Modelling the Evolution of the Aerospace Supply Chain. Before that he worked as a Research Officer in the project New Product Development as a Complex System of Decisions at the Complex Systems Research Center, Cranfield University. He worked as a naval architect and manager in the shipbuilding industry in Northern Europe for many years. He worked as a consultant in shipbuilding in Asia and as a manager in the Norwegian offshore engineering industry.

James Baldwin University of Sheffield, ENG James S. Baldwin is a Lecturer in Manufacturing Technology at AMRC, the University of Sheffield. Research interests include the development and application of evolutionary theory and classification science in the context of engineering management, operations, production and supply chain management, strategic management, and organizational behavior.

Keith Ridgway University of Sheffield, ENG Keith Ridgway (CBE, Fellow of the Royal Academy of Engineering) is executive dean of the University of Sheffield AMRC, and research director and co-founder of the AMRC with Boeing. He also takes additional role as Executive Chair of the Advanced Forming Research Centre (AFRC) at the University of Strathclyde. Focusing on strategy development of three major manufacturing research centres in complementary areas to guide future manufacturing policies in the UK and support collaborating companies to gain access to the facilities and expertise in high value manufacturing.

The paper explores the Darwinian idea of natural selection through the preservation of favorable variations and the rejection of injurious variations. This is shown through focus on the evolutionary processes of variation and selective retention. Variability is necessary is necessary for success in a rough and unpredictable

90 | Rose-Anderssen, Baldwin & Ridgway

A cladistics and Linnaean exploration into the Darwinian selection...

environment. It is the micro-diversity that drives evolving, emerging organizational structures. The paper has tried to answer how manufacturers can make sense of variety and see opportunities for the future. Thus how can these processes be explained through the complexity of interactive entities. The methodology through which the evolutionary processes of variation and selective retention is explored is through cladistics and Linnaean classifications. The concept of evolutionary stable strategy is applied to these systems. This is demonstrated through the examples on the Varieties of Product Centered Genus. The paper then suggests a three level approach to variation, selection and retention, namely a genetic analogy where the phenotypic or interactor manifestation is taken, the concern about the fitness of the Variety within the external environment, and finally the implementation of a new manufacturing Variety through human action.

Introduction

W

ith the increasingly fiercer competition in the marketplace for industrial product this can be argued to ask for innovative solutions in terms of technologies, practices and products. Manufacturing cladistics sets out to explore available solutions both from literature and through field research. In order to better understand and visualize the levels of relationships cladistic classification is presented, supplemented by a hierarchical or Linnaean classification. This paper takes such an approach. And it means to try and select for the appropriate manufacturing Species that will both sustain in the present environment and be favorable in a rapidly changing market environment.

The paper firstly presents principles of the evolutionary aspects of cladistics and Linnaean classification. And then tries to understand the inherent diversional capacity of a Species through exploring Darwin1 and his idea of natural selection through the preservation of favorable variation and the rejection of injurious variations. A discussion follows on variation or diversity as a prerequisite for evolution, organizational change2,3 and survival. The variation in behavior amongst players in the environment is then discussed in light of the concept of evolutionary stable strategies4, where interactive dependencies are developing the strategies5. The paper then discusses how manufacturers can make sense of variety and thus make strategic choices for change. This is followed by the discussion of the evolution of manufacturing Species and the development of its classification system.

E:CO 2016 18(2): 90-118 | 91

Finally, the discrete manufacturing systems is presented. A selection of varieties of the ideal / textbook manufacturing Species are presented and discussed.

Relationships, variations and the evolutionary aspects of cladistics

M

cCarthy6 points out that understanding and making sense of organizational variety, change and survival has long been a concern to academics involved with the management of change within several field of management.

Campbell7 advocates not to focus on the direction of evolution but rather on the underlying process of variation and selective retention. An explanation of why organizational form may remain separate is required for a more correct classification of organizations. This is referred to by Campbell as the retention process. Following this tradition, Breslin8 based his research on the evolutionary mechanisms of variation, selection and retention. Thus why some Species and Varieties are retained within an environment will be addressed in the paper. Cladistics is an evolutionary classification scheme that not only describes the attributes of existing entities but also the ancestral characteristics. Cladistics is also distinguished by its emphasis on parsimony and hypothesis testing, particularly falsification, rather than subjective decisions that some other taxonomic systems rely on. The principle of falsification advocated by Popper9 is based on his critical approach to science. This approach proceeds through trial and correction of error10. In other words, for Popper9, truth is understood as an approximation to truth. Cladistics (ancient Greek, klados = branch) is really an approach to classify in which items are grouped together based on whether or not they have one or more shared unique characteristics that come from the group's common ancestor and are not present in more distant ancestors. Therefore, members of the same group are thought to share a common history and are considered to be more closely related. Change in characteristics occurs in lineages over time. It is only when characteristics change that we are able to recognize different lineages or groups. The outcome of a cladistics analysis is a cladogram, a tree shaped diagram also called a dendrogram that represents a phylogenetic hypothesis on evolutionary relationships.



Erasmus Darwin, the paternal grandfather of Charles Darwin, formulated the first formal theories on evolution in Zoonomia - The laws of organic life11. He was concerned about how a species reproduced itself. His idea was that the strongest and most active animal should propagate the species, which thereby became improved. He thus anticipated natural selection. The methods of cladistics were originally developed by linguists to classify the evolution of languages. Saphir12 investigated the evolutionary relationships between aboriginal American languages, and Kroeber and Chretien13 classified the relationship between Indo-European languages. Cladistics was later adapted to biology by the German entomologist Willy Hennig while he was working on phylogenetic classifications. He referred to it as phylogenetic systematics. The use of the terms cladistics and clade was popularized by other researchers. Cladistics focuses on branching points in phylogenetic lineages16. It is at the branching points where variation and change occurs. 14,15

Classification, as a science, essentially began with biologists. Taxonomy and clas-

Figure 1 Darwin's (1859) evolutionary tree E:CO 2016 18(2): 90-118 | 93

sification have been useful tools in managing the information on living entities, their genetics, form and behavior. The system of hierarchical biological classification was originally described by Carl Linnaeus (later von Linne) in his book, Systema Naturea originally written in 173517. Here von Linne describes systematics as the scientific inquiry into biological differences. The group into which organisms are placed are referred to as taxa (singular: taxon). The taxa are arranged in a hierarchy. He grouped Species according to shared physical characteristics. In Systema Naturea he divided nature into three Kingdoms: Mineral, Vegetable and Animal. His hierarchy of biological classification was limited to Kingdom, Class, Order, Genus, and Variety. The taxa are arranged in a hierarchy. Cladistics • •

•

Linnaean hierarchy

A cladogram represents a phylogenetic hypothesis on evolutionary relationships A group’s common ancestors are not present in more distant ancestors• Items are grouped together based on whether they have one or more shared unique characteristics It is only when characteristic change that we are able to recognize lineages or groups

• • •

Species grouped according to shared physical characteristics Taxa are arranged in a hierarchy In this paper hierarchy is represented by Class, Order, Family, Genus and Species

The essential aspects of cladistics and Linnaean hierarchy

Darwin, variation and the evolution of Species

I

n his classification, Darwin1 uses a tree of life to explain the evolutionary history of biological Species. The Darwinian approach is about the long-term evolution of Species through variation. These variations are small but significant and result in irreversible changes to a Species. Darwin1 argues that Species are not completely unique, but they share morphological similarities. Species can therefore, he suggests, be classified into a pedigree or evolutionary tree (see Figure 1) based on the similarities between them. In his evolutionary tree, Darwin1 basically illustrates how Species A after a thousand generations has produced two fairly well marked Varieties a1 and m1. They are slightly modified forms of their parent generation. And they have inherited those advantages that made their parent generation more successful than their competing Varieties. In his tree, Darwin1 shows the evolution of Varieties a1 to a2, and m1 to m2 etc., selected by nature through producing advantageous variations that make them



sustain. Darwin1 argues that it is never straightforward to ascertain whether two forms should be defined as different Varieties of a Species or simply be ranked as two different Species. More specifically, the degree of difference between Varieties is much less than the difference between Species of the same Genus (hierarchical level above Species). The principle of divergence of character1 happens in the long-term, in thousands of generations, as Varieties become more distinct from each other. From an evolutionary perspective, Darwin1 argues that Varieties are actually Species in the process of formation. The split between Varieties is a major bifurcation of evolution leading to a new Species. The most severe competition for survival is between members of the same Species and Species of the same Genus, because they frequent the same habitat for the same food1 using the same performance characteristics. Therefore, variability is important for the evolution and the sustainability of Species. For as Darwin1 observes in his chapter on Variation under Nature: ‘These individual differences are highly important for us, as they afford materials for natural selection to accumulate, in the same manner as man can accumulate in any direction individual differences in his domesticated producers'1. Darwin1 defines Natural Selection as the preservation of favourable variations and the rejection of injurious variations. Where many species of a genus have formed, on average many are still forming; and this holds good according to Darwin. With variation under domestication1 we are not looking at a thousand generation perspective but at a one to maybe just a few generations perspective. Belyaev37 had a 20 year focus on a selective breeding program with the intention of reproducing a single major factor, namely a selection pressure for tame-ability of red foxes18. For the breeding of competitive sport-horses or racing sled dogs, where the major factor would be running performance, there would be 2 or 3 generations perspective only. If favorable results are not obtained within this perspective the experiment is discontinued. If successful a new 2 or 3 year perspective is taken. That is a more rapid change and it is selected for different characteristics than nature would have done. Also he has the opportunity of a greater variety choice. So he inter-breeds between greater Varieties and thus gains a different Variety in the offspring. He has intervened into the natural selection of nature. Belyaev37 argues that varieties or sub-varieties of cultivated or domesticated plants or animals differ more from each other than do individuals of any species or variety in nature. His conclusion to this is that the vast diversity is simply due to the domestic productions having been raised under conditions not so uniform as for the parent species having been exposed to under nature. He then argues that a high degree of variability is favorable as it gives nature more freedom to select from.

E:CO 2016 18(2): 90-118 | 95

Species classification

Species can be classified into an evolutionary tree based on similarity Difference between Varieties much less than difference between Species of same Genus Divergence of character – Long-term development over thousands of generations

Survival of Species

Firstly, competition between genes having an effect on embryo development. Secondly, difference between genes emerge only in their effect. Thirdly, successful genes are those that have a beneficial effect on the adult and are likely to reproduce the same genes on to future generations. Fourthly, the interactor is used for the bodily manifestation of the gene. Fifthly, severe competition between members of same Species. Sixthly, competition between different Species of same Genus

Evolution of Species

Nature favors a mutation that increases the fitness of an individual in its environment. Mutation – Important mechanism by which variation arises. Dominant or recessive alleles – Recessive alleles are hidden physical characters. Varieties are Species in progress of formation

Natural selection

Competition as a means of preservation of favorable variations; rejection of injurious variations

Variation under domestication

Human intervention into pure natural selection in the domestic environment resulting in a different three level competition between; a) genes, b) members of same Species within the same household, c) members of the larger and more global domestic environment. Vast diversity of domestic production due to less uniform conditions?Less variety under natural selection due to more uniform conditions.

Table 1 Variation and Darwin’s evolution of Species Mutation is an important mechanism by which variations arise. A mutation is a change in the chemical constitution of the chromosomes of an organism. This can produce an inherited change to the organisms which develop from them. It is due to the natural selection of strains of organisms which have become better adapted to their environment, as a consequence of genetic mutation, that the evolution of species has taken place. However, natural mutations are rare events, and when they occur they almost always produce injurious characteristics19. And this is about the changes in chromosome numbers that may create the divergence of a Species population, and thus produce a new Species20. Nature therefore favors a mutation that increases the fitness of the individual in its environment, and it culls mutations that decrease the fitness of the individual. In this way nature through natural selection is trying to economize in every part of its organization of a Species1. Dawkins4 argues that for all cases in which natural selection has favored genes for manipulation, those same genes have



extended phenotypic effects on the body of the manipulated organism. By that he says that natural selection favors those genes that manipulated the world to ensure their own propagation. He defines the central theorem of extended phenotype as; an animal's behavior tends to maximize the survival of the genes for that behavior, whether or not those genes happen to be in the body of the particular animal performing it. Dawkins4 argues that the difference between genes emerge only in their effect. That means the effects on the embryonic development and thus on bodily form and behavior. The successful genes are those that have beneficial effects on the adult and are likely to reproduce the same genes on to future generations. The phenotype is used for the bodily manifestation of the gene. Natural selection therefore according to Dawkins favors some genes rather than others because of the consequences they produce, that is their phenotypic effects. Each gene of an individual has different alleles. An allele may be dominant or recessive to another allele. Where it is dominant, its phenotype may be a typical physical character of the organism. The phenotype of the recessive allele is a hidden physical character. Therefore, Dawkins4 like Darwin1 lays stress on competition as a means of economizing survival in an environment of limited resource. That is less-favored varieties must have become less numerous because of competition, and ultimately many of their lines must have become extinct. Early life was not capable of supporting an infinite number of replicator molecules. In today's generalized Darwinian terminology, genotype has been replaced by replicator. The replicator is the information/code/program/meme. The phenotype has been replaced by interactor. The interactor is the expression of the information.

Figure 2 Multidimensional performance landscape E:CO 2016 18(2): 90-118 | 97

Variability

Necessary for success in rough and unpredictable environment

Micro diversity

Drives evolving, emerging organizational structure

Darwinian micro-diversity

Random and independent in the natural selection of Species Human intervention and innovation in variation under domestication

Table 2 Variation and organizational change Dawkins argues that at some point a remarkable molecule was formed by accident. This molecule was able to create copies of itself. He calls this the replicator. The becoming of life on Earth started off with populations of stable varieties of molecules. Stable in the sense that lasted a long time, replicated rapidly, or replicated accurately.

Variation or diversity as a prerequisite for organizational change and survival

A

once sufficient variety of a species may later have become an injurious variation. When this is acknowledged as a fact an organization might try and imitate the closest ideal or typical text-book species. In the real world, however, there might choices between several varieties of this text-book species. Single varieties that might be a better fit into the very specific environment that the organization wants to pursue its existence and sustain. Kondra and Hining21 say that there has to be some variation and diversity in organizational forms in order for change to occur. And this they argue institutional theory has tended to ignore. In general, diversity is understood as the state or quality of being different or varied22. Similarly, variation is understood as the act, process, condition, or result of changing or varying, i.e. diversity; variety being the quality or condition of being diversified or various. In these senses diversity and variety are synonymous. Allen and McGlade2 found that variability is necessary for success in a rough and unpredictable environment. The climbing of the hills of a multidimensional performance landscape23,24. See Figure 2 below. show that the mechanism of variability could be adjusted by the evolutionary process, itself, leading to the idea that evolution is driven by the noise to which it creates. For3 the underlying mechanism of evolution involve micro-diversity within a system, and how this drives an evolving, emerging system structure that is characterized by a changing level of structural diversity.3 says that in Darwinian thinking the microdiversity that occurs is considered to be “random” and independent of the selection 2



process that follows, while in human innovation we may think that there is intention, calculation and belief that may ‘channel’ diversity into some narrow range. Darwin1 does take human innovation into consideration in the case of discussing elaborate variation under domestication as shown in the previous section. This is about the human intervention into the otherwise slow natural evolution of species. And this is what we intentionally make available in this paper by offering many variants of Species to strategically select from.

The struggle for existence within the environment and evolutionary stable strategy

T

he environment is tough and demanding. Darwin1 observed that a species during its lifetime is constantly suffering from enemies and competitors occupying the same place and searching for the same food. The enemy or competitor having only a slight favorable characteristic fitting a slight change in the environment will prevail. The less favorable species will decrease in number. This is the principle of survival of the fittest. Darwin1 has called the principle of struggle for existence where any variation if it be profitable to an individual of any Species in relation other organic beings and to external nature, Natural Selection. Darwin's idea is that nature tends to the preservation of that individual and in general this is inherited by its offspring. Viewed from the generalized ‘Darwinian’ principles of variation, selection and retention, the successful variation is retained over time.

Figure 3 Activity triangle E:CO 2016 18(2): 90-118 | 99

Dawkins4 argues that the concept of Evolutionary Stable Strategy (ESS) invented by Maynard Smith25 is one of the most important advances in evolutionary theory since Darwin.ESS is an application of Game Theory to biology. Game Theory apples to a wide range of behavioral relations. To be fully defined Rasmusen26 refers to four essential elements of the game, namely the players of the game, the information, the actions available to each player at each decision point, and the payoffs for each outcome. Basically, these elements, along with a solution concept of their choosing, to deduce a set of equilibrium strategies for each player such that, when these strategies are employed, no player can profit by unilaterally deviating from their strategy. The payoff for games in biology are often interpreted as corresponding to fitness. In biology, game theory has been used as a model to understand many phenomena. One such phenomenon is known as biological altruism. This is a situation in which an organism appears to act in a way to benefit other organisms and is injurious to itself. An ESS is one where most members of a population adapt to it, cannot be bettered by an alternative strategy4. That is; the best strategy for the individual depends on what the majority of the population are doing. Since the rest of the population consists of individuals each trying to maximize his own, the only strategy that persists will be the one which, once evolved, cannot be bettered by any deviant individual. Essentially therefore, an ESS is stable not because it is particularly good for the individuals participating in it, but simply because it is immune to treachery from inside. Following a major environmental change, there will be a brief period of evolutionary instability. But once an ESS is achieved it will stay as selection will penalize deviation. The elements of game theory and ESS may be presented in Engeström's27 activity theory model (Figure 3). This illustrates the interactive dependencies of the elements. Each player tries through his actions to maximize his own payoffs. To achieve this he applies his information, solution concepts and language. However, the population of the co-existing players are governed by biological altruism that are immune to strong individual actions that could work against the benefits of population as a whole to sustain in the environment. Similarly, the population takes advantage of the individual expertise that benefits the population. The individual player has actions for his goals, whilst the population as such can be seen as part of an activity that is characterized by interacting elements directed towards a common more collective object. In that communicative interaction28 through



dialogue23 becomes important. As the individual players, and thus their mutual community learn, their information, solution concepts and the ability to communicate through language evolve. The evolution of information and concepts takes place as a transformation of knowledge5. The knowledge transformation takes place in interactive communities through three levels of learning. Adaptive learning happens when people adapt to practices and systems developed by others. Reactive learning occurs when routine practices are applied in solving problems. Reactive learning is therefor about taking corrective action to perceived mistakes and learning from that. Expansive learning takes place as an expansion of the given context27. This form of learning may occur at the boundaries where people meet and interact to form new meanings that go beyond the limits of the individual alone. This multi-voiced interaction goes against the strong individual actions that could work against the benefits of the population. This influences the co-development of their ESS and thus the adaptation to the reality of a changing environment. This object gives a direction for the future. The equilibrium stable strategy slowly evolves. In the case of major environmental change, each individual's actions become more dynamic until the population has made the ESS adapt to the changes. This ESS is retained over time, until it is subject to new major environmental changes. Game Theory and Evolutionary Stable Strategy Game Theory

Elements

Equilibrium strategy

Biological Altruism

Actions beneficial to all involved organisms

Injurious to single organism

ESS

Immune to treachery from inside

Will penalize deviation

Interactive dependencies of elements

Benefits of population as a whole sustains

Major environmental change

Interactive learning may enhance the evolution of the ESS; ESS evolves until population make ESS adapt to change; Successful ESS is retained over time

Players; Information; Actions; Payoffs; Solution concept

E:CO 2016 18(2): 90-118 | 101

Manufacturers sense of variety and the strategic choices for change

C

onsumer expectations and the demands of mass customization and personalized products mean that manufacturers need to use optimum production layouts and systems, and to continually redesign and reorganize the manufacturing technologies and other resources. Reorganizing factories is expensive, loses valuable production time and it is often necessary to introduce new technologies and systems which are untried. Furthermore, manufacturing organizations form a very diverse population made up of firms with varying sizes, markets, operating methods, manufacturing systems and technologies used. The major challenge is to achieve the integration of this vast and diverse information set and to produce a solution, which is applicable to a wide range of diverse manufacturing organizations. It was therefore needed to question the following; how can manufacturers make sense of variety and opportunities available for change and future survival, secondly, how can these change processes be explained in terms of the complexity of the interconnection of systems, processes and technologies. The paper tries to answer these questions. The text-book species is only the preliminary stage of a human intervention for change starting at the level of the hierarchical and cladistics classification. The real is defined by the present identity and also by the result after exploring and implementing a new species or variety of species. The mechanism by which variation can be presented as follows; the gene manipulation alias character state (CS) manipulation. From this point of view successful CSs are those that have beneficial effects on a manufacturing system, and that are likely to be reproduced in future generations of a manufacturing Species. CSs that have been tried out without beneficial effect are ignored in the historical account of Species evolution represented by the cladogram. In the real world these CSs can be said to be formed by accident or by trial and error activities. In that sense there is a competition among CSs that will characterize a manufacturing system. It is not necessarily the CS that is more advanced or superior that wins but rather the CS that fits best with other CSs of the manufacturing system. At the next level, a Species of manufacturing is represented by a number of slightly different varieties. Only varieties which endured competition sustains in the historical representation of the Species. Only these may produce new varieties themselves. 102 | Rose-Anderssen, Baldwin & Ridgway


Changing or developing a manufacturing Species depends highly on the strategic choices being made. However, according to Game Theory, equilibrium strategies are employed that no player can deviate from. The payoff interpreted in biology corresponds to fitness where the best strategy for fitness depends upon what the majority is doing. That is it depends on what environment the majority of Species have created.

The evolution of manufacturing Species and the development of its classification system

T

o construct the 1st generation (basic) cladogram, the most evolutionary significant characters and states were selected and refined and this continued throughout the paper. These characters are phenotypic in nature. For the initial character search, two types of variables were identified - continuous and discrete. Discrete variables are typically used directly in the cladistic analysis. The coding used was preliminary and was amended when the ‘Determine the characters’ step has been completed. To explain this further it is necessary to look at the distinction between the phenotypic and genotypic nature of the characters identified in the paper. Basically, the term phenotype is used to describe the observable characteristics or outward physical manifestations of an organism. The term genotype denotes the organism's genetic make-up29. In terms of evolution, it is interesting to know how the phenotype and the genotype are related. Clearly, the genotype defines the phenotype, but how does the phenotype influence the genotype? In terms of natural selection this acts directly on the phenotype. The differential reproduction and survivorship depend on the phenotype. Therefore the phenotype is the observable expression of the genes and therefore the genotype that affects the traits30. To figure out the true genotype, the family history can be examined or the organism can be bred and the offspring can show whether or not it had a hidden recessive allele. That is traditionally genes were seen as abstract entities dependent entirely on inferences from the phenotypes of organisms involved in various breeding experiments31. Given a knowledge of the phenotype the underlying causal genotype could be unambiguously inferred and vice versa. However, the actual correspondence between genotype and phenotype is problematic as any given genotype corresponds to many different phenotypes. The ambiguity in the relationship between genotype and phenotype requires special experimental techniques to reveal it31.

E:CO 2016 18(2): 90-118 | 103

Figure 4 1st generation factual hierarchical classifications


Figure 5 Primary species-defining characters and states


E:CO 2016 18(2): 90-118 | 105

Figure 6 Factual 1st generation cladistics classification 106 | Rose-Anderssen, Baldwin & Ridgway

Figure 7 1st generation Variety-Defining and Secondary Product-, Process- and Systems character and states


E:CO 2016 18(2): 90-118 | 107

As mentioned previously, in today's generalized Darwinian terminology, genotype has been replaced by replicator. The replicator is the information/code/program/ meme. The phenotype has been replaced by interactor. The interactor is the expression of the information. Similarly, as a cladistics exercise, it is therefore necessary to try and search out the interactor-replicator duality. That is to search out how an interactor manifestation is also represented in the history of a Species. As can be argued it is only when characteristic change and are shared we are able to recognize different lineages or groups. Then the characteristics have become more than an interactor manifestation. In practice, several generations of lineages or groups have to be worked at through testing and refuting in order to approach a more true representation of manufacturing Species relationships. The observable characteristics from literature and industry are the interactors that have been subject to the selection by academics and the industrial environment respectively. The understanding and knowledge of these characteristics are the replicators that are made available for developing interactors in new situational contexts. This explains the interactor-replicator duality applied to manufacturing change and evolution in practice. Using the cladistic approach, the evolutionary relationships between fifty-three candidate species manufacturing systems, using ‘descriptors’ drawn from a library of thirteen characters with established themselves a total of eighty-four states (see Table 2), are hypothesized, described and presented diagrammatically. The manufacturing species are then organized in a hierarchical classification with fifteen genera, six families and three orders under one ‘class’ of discrete manufacturing systems (see Figure 4).

The discrete manufacturing system

T

he characters and states describe Species that have been universally accepted text-book interpretations of manufacturing Species. These can be understood as Species that have under natural selection in the world of manufacturing. They are presented in Figures 2 and 3, and in tables 2 and 3.

The greatest innovation of Linnaeus was the general use of binominal nomenclature. That is the combination of a Genus name and a second term to identify the Species. For instance, in the Figure 4 above, Species 1 will be denominated Product Centred Workshop. 108 | Rose-Anderssen, Baldwin & Ridgway


Examples of favorable varieties of discrete manufacturing Species

M

cKelvey16 argues that as a consequence of ongoing environmental pressures and ongoing selection and retention of certain varieties, an organization takes on a certain form. That aspects of a given organization's form which meets adaptive needs tend to be retained by the organization. Thus these forms or Species may be acquired by others facing similar environmental pressures. In this example, it has been chosen to focus on the Varieties of the Product Centered Genus.

In the following section varieties to the Species shown in Figures 4 and 5 have been included in order to tell the evolutionary history of discrete manufacturing systems more comprehensively. The suggested varieties are potentially new species in formation if successfully implemented. They may initially be experimented with for adapting a better fit to a different or more rapidly changing environment. Thus they are interventions into the more established and general text-book solutions. As such they are variations under intended domestications.

The out-group

T

he evolutionary history, depicted in the factual cladistics classification (See Figure 5) must again begin with an Out-Group32, which represents Self-Production (Species 0). This primitive system of manufacturing shares many of the characters to theIn-Group or clade passed on from a common ancestor. Self-Production has a multi-product capability (CS 1-1) but manufactures articles for personal use, in a fixed position (CS 2-1), in an undercover site (CS 3-1) and usually in the place of living. Simple, universal, processing techniques and tools are employed, in the form of manual or hand tool manipulation (CS 4-1). All the necessary processes are performed and the full article produced, by the one person (CS5-1) in one go, i.e., without WIP or ‘buffer’ between the processes (CS 6-1). Primary material handling is primarily manual (CS 7-1) and, in some instances, mechanized (primitive pulleys, winches, etc.). The earliest example of this would be in hunter and gatherer social systems in which clothing, simple tools and weaponry would be made for personal use, e.g., stone cutting tools, spears, dwelling materials, etc.33; more recent examples are craft items for personal use and decoration. E:CO 2016 18(2): 90-118 | 109

The Self-Production Species is an example of a well-established and primitive ESS. An ESS that through thousands of years has been beneficial to a population that has experienced no environmental change. And this is the reason why this Species has been selectively retained over time.

Multi-family order and fixed position family

T

he first Species to evolve from the common ancestor starting what is now the Class of Discrete Manufacturing is the Product Centered Workshop (Species 1)33 and belongs to the Multi-Product Order of manufacturing systems. In this Order, state-changes of the majority of the above-mentioned characters are evident (with the exception of material handling) in addition to two new characters to emerge - the style of management and the power over resources that are managed in project-managed products.

Again following Darwin's idea in his evolutionary tree (Figure 1), the evolution shown in Figure 5, the branching points16 are caused by the changes in characteristics. And this creates varieties or potentially new species in formation. The most significant CS change in this Order is the General Layout Approach with the fixed position layout (CS 2-1) being the most defining CS for the Fixed-Position Family and the process layout (CS 2-2) the most defining CS for the Process Family (Slack et al. 2006). The Fixed-Position Family comprises three Genera, the Product Centered, Remote, and Organizational.

Product centered genus

T

his paper proposes offshoots of the three Species Workshop (Species 1), Assembly Plant (Species 2), and Assembly / Fabrication Yard (Species 3). The offshoots or Varieties of the Species will be subject to scrutiny by their internal organizational environment and their external environment.

As mentioned above, the first Species of the Product Centered Genus is the Product Centered Workshop34; the primary difference from the Out-Group is that an entrepreneurial spirit (CS 8-1) has emerged where the manufactured products are sold to customers. That is, the multi-product capability is retained but is complemented with a multi-order capability (CS 1-2) and capable of make-to-order, make-to-stock, engineer-to-order, assemble/configure-to-order, and assemble-to-stock. Speculatively, this Species may have evolved thousands, perhaps millions of years ago when one



Figure 8 The varieties of the species of the Product Centered Genus: Workshop, assembly plant and assembly/fabrication yard or more people had a particular skill in producing articles, and another, a particular skill in hunting; the former may have agreed with the latter to supply weapons (bow and arrows, spears) in return for a share of the catch (Rose-Anderssen et al.2011). This Species is evident today with, for example, Specimens of jewelry makers, fly-fish makers, carpet weavers, clockmakers, along with a lot of the other handicrafts although the monetary system is now the primary trading mechanism. The second Species in the Product Centered Genus is the Product Centered Assembly Plant (Species 2)35. In the Product Centered Assembly Plant, products are more complex, require more workers, who still perform significant product tasks, but only produce part of the product (CS 5-2) albeit a significant part. With more workers and more complex products and production sequences, a more centralized management capability is evident where skilled resources are scheduled according to non-routine tasks at hand (CS 8-2). Final assembly of cars around the turn of the twentieth century is a good example Specimen of this Species whereas the final assembly of large aircraft such as the A380 and Boeing 787 are more recent examples24,36. The third and final Species in the Product Centered Genus is the Product Centered Assembly / Fabrication Yard (Species 3)35. Here, a change in the Location of Production character is evident featuring an on-site but uncovered (or external) dedicated facility

E:CO 2016 18(2): 90-118 | 111

(CS 3-2). This also represents a variation in the size and nature of the resource pool. Boat- and shipyards, throughout history, are good Specimens of this Species. There are thirty Varieties of each of the Species in this Product Centered Genus (see Figure 6). To elaborate, the Workshop, Assembly Plant and the Assembly / Fabrication Yard may exhibit one of all states of the Variety-Defining, Specific Order Type character (see Table 3) such as make-to-order (CS 14-1), make-to-stock (CS 14-2), engineer-to-order (CS 14-3), assemble/configure-to-order (CS 14-4) or assemble-tostock (CS 14-5). Of the Universal Process Capability, each Species may exhibit just the manual and/or hand/power tool (CS 15-1), but may primarily employ any of the states of the Modular Universal Process Capability character: modular mechanized machine tools (CS 16-1), or modular CNC machine tool/centers (CS 16-2). And finally, the three Species may display one of two Product Centered Layouts - the standalone (CS 17-1) or the parallel production (CS 17-2). As an example, a Variety of the Product Centered Workshop Species, may be identified and named a Parallel, Modular CNC Machine Tool, Engineer-to-Order Product Centered Workshop. The paper suggests a multi-level approach of variation, selection and retention. At level 1, a genetic analogy is presented. This is about the phenotypic or interactor manifestations of the character. The concern is about manifestations that have beneficial effects on the manufacturing organization as such. From the position of ESS one should consider the competition versus the adaptation of single characters of a variety. That is the action each of them exerts within the character population of the specific Variety. However, the collective object of the Variety would be to achieve an ESS that makes the Variety sustain into the future. This is valid until the Variety is subject to new and major external environmental change. The information or solution concept held by the player as a character should not work against the benefits of the character population as a whole for the specific Variety. It must be adapted to the overall functioning of the elements of the internal variety Game. For example, it is a disadvantage if one character of a Variety is about running at high speed when the other characters does not fit high speed and the aim of the Variety is not about high speed. In practice this level is about the fitness of the Variety within itself. At level 2, the concern is about the fitness of the Variety within the external environment. The variety should be favorable in its intended environment. At the same time it should in the long run have adapted to the other competitors / players in the



Game where a mutual ESS assist the larger and interdependent manufacturing community sustain. The idea being that no manufacturing player can profit by unilaterally deviating from a shared ESS. At level 3, comes the implementation of a new manufacturing Variety through human action. Choosing and implementing an appropriate Variety from the classification may assist a manufacturer moving into the future. The manufacturer is likely to firstly choose a variant that do not deviate much from their present Variety of the text-book manufacturing Species. This is also costly change to embark on. However, to achieve a more successful result, it is necessary to determine which environment they want to exploit for the future. Then they need to try and understand which Variety of their Species could best fit this environment. This is a collective effort where ideally it should involve all the players affected by the changes. Human behavior is always complex. Therefore the collective effort is necessary for achieving benefits for the organization as a whole. The individual player's hands on expertise, in terms of information and solution concepts, should through communicative interaction be directed towards the object that gives a vision for the future. The individual player's actions would be governed by biological altruism in terms of social rues they have co-developed in order to successfully collaborate on what is beneficial for their work community and hence their manufacturing organization.

Conclusions

T

he manufacturing classifications in this paper has been produced in order for manufacturers to explore the opportunities available in way of best practice solutions for their manufacturing systems improvements.

Through a combination of cladistics and Linnaean classifications, the paper explores the Darwinian idea of natural selection through the preservation of favorable variations and the rejection of injurious variations. In the paper the focus is on the underlying evolutionary processes of variation and selective retention. The evolutionary aspects of cladistics and Linnaean classification is explored in order to better understand and visualize the levels of relationships between species. Organizational variety is presented in the evolutionary classification scheme of cladistics, where both the attributes of existing entities and also the ancestral characteristics are described. The cladogram represents a phylogenetic hypothesis on evolutionary relationships. E:CO 2016 18(2): 90-118 | 113

The Linnaean classification is a hierarchical supplement to the cladistics. The scientific inquiry is into biological differences arranged in a hierarchy. Species are grouped according to shared physical characteristics. Darwin1 is concerned about the long-term evolution of species through variation. The idea being that variations are small but significant and may result in irreversible changes to a species. Specie can be arranged into an evolutionary tree based on similarity. There the difference between varieties are much less than between species of the same Genus. The survival of species may be characterized by the following; (1) the competition between genes will have an effect on embryo development, (2) the difference between genes emerge only in their effect, (3) successful genes are those that have a beneficial effect on the adult and that are likely to reproduce the same genes on to future generations, (4) the interactor is used for the bodily manifestation of the gene, (5) there is severe competition between members of the same species, and (6) there is competition between species of the same genus. The evolution of species is about variation. Mutation is the important mechanism by which variation arises. Nature favors a mutation that increases the fitness of an individual in its environment. Varieties are species in formation. Natural selection is characterized by competition as a means of preservation of favorable variations and the rejection of injurious variations. In variation under domestication, human selection intervention into pure natural selection in the domestic environment result in competition; (a) between genes, (b) between members of the same species within the same household, and (c) between members of the larger and more global domestic environment. There is a vast diversity of domestic production due to less uniform environmental conditions than is the case in the nature. Variability is necessary for success in a rough and unpredictable environment. It is micro-diversity that drives evolving, emerging organizational structures. Darwinian micro-diversity is characterized by random and independent natural selection of species. It is present through human intervention and innovation under domestication. The struggle for existence has been viewed from the position of Game Theory and Evolutionary Stable Strategy (ESS). In Game Theory the elements of players, infor-



mation, actions, payoffs and solution concept come together resulting in equilibrium strategies. The population of co-existing players are governed by biological altruism were actions are beneficial to all players but is injurious to the single player.' The ESS is immune to treachery from inside and will penalize deviation. There is thus an interactive dependency of the elements, and the benefits of the population as a whole to sustain. In the case of major environmental change, interactive learning may enhance the evolution of the ESS. The paper has tried to answer; (1) how manufacturers can make sense of variety and opportunities for future survival, (2) how can these processes be explained in terms of the complexity of interconnected systems. Character states that have had beneficial effects on various manufacturing systems are represented within the textbook species. Only varieties which endured competition have sustained in the historical representation of species. The observable characteristics from literature are the interactors that have been selected. The knowledge and understanding of those characteristics are the replicators that are made available for developing interactors in new situational contexts. The discrete manufacturing system was presented in terms of a factual hierarchical classification, primary species defining characters and states, factual cladistics classification and variety-defining characters and states. This was the basis for the discussion in the rest of the paper. The paper has chosen to demonstrate the theories discussed through the examples on the Varieties of the Product Centered Genus. The idea is that aspects of a given organizational form meeting adaptive needs tend to be retained by the organization. The factual cladistics classification had to start off with an out-group. Here the Self-Production Species. And this species shows many characters with the In-group. The Self-Production Species is the result of a well-established ESS. An ESS that has become beneficial to the population of this Species, and has thus been selectively retained.

E:CO 2016 18(2): 90-118 | 115

The paper presents three Species of the Product Centered Genus; Product Centered Workshop, Product Centered Assembly Plant, and Product Centered Assembly / Fabrication Yard. There are thirty Varieties of these Species in the Product Centered Genus. The paper suggests a three-level approach to variation, selection and retention: •

At level 1, a genetic analogy is presented. That is, the phenotypic or interactor manifestations is taken. The collective object of the Variety would be to achieve an ESS that can make the Variety sustain in the future.

•

At level 2, the concern is about the fitness of the Variety within the external environment. Here the competitors / players have achieved a mutual ESS to assist the interdependent manufacturing community sustainability.

•

At level 3, this is about the implementation of a new manufacturing Variety through human action. This is a collective effort where all the players affected by the changes should be involved. The individual player's hands-on expertise are directed towards the object that gives a vision for the future. This is governed by biological altruism in order to successfully collaborate on what is beneficial to their work community.

References 1. Darwin, C. (1859). The Origin Of Species, ISBN 9780192834386. 2. Allen, P.M., and McGlade, J.M. (1987). “Evolutionary drive: The effect of microscopic diversity, error making, and noise,” Foundations of Physics, ISSN 0015-9018, 17: 7. 3. Allen, P.M., Strathern, M., and Baldwin, J.S. (2006). “Evolutionary drive: New understanding of changes in socio-economic systems,” Emergence: Complexity & Organization, ISSN 1521-3220, 8(2): 2-9. 4. Dawkins, R. (1989). The Selfish Gene, ISBN 9780198788607. 5. Rose-Anderssen, C., Baldwin, J.S., Ridgway, K., Allen, P.M., and Varga, L. (2009). “Knowledge transformation, learning and changes giving competitive advantage in aerospace supply chains,” Emergence: Complexity & Organization, ISSN 1521-3250, 11(2): 15-29. 6. McCarthy, I.P. (2005). “Toward a phylogenetic reconstruction of organizational life,” Journal of Bioeconomics, ISSN 1387-6996, 7 (3): 271-307. 7. Campbell, D.T. (1965). “Variation and selective retention in sociocultural evolution,” in H.R. Barringer, G.H. Blanksten, and R.W. Mack (eds), Social Change in Developing Areas: A Reinterpretation of Evolutionary Change, Schenkman, Cambridge, MA, pp. 19-48. 8. Breslin, D. (2014). “Calm in the storm: Simulating the management of organizational coevolution,” Futures, ISSN 0016-3287, 57: 62-77.



9. Popper, K. (1959). The Logic of Scientific Discovery, ISBN 9780415078924. 10. Corvi, R. (1997). An Introduction to the Thought of Karl Popper, ISBN 9780415129572. 11. Darwin, E. (1794). Zoonomia, Volume 1: Or, the Laws of Organic Life, ISBN 9781108005494. 12. Saphir, E. (1916). Time Perspective in Aboriginal American Culture: A Study in Method, ISBN 9781297576201. 13. Kroeber, A.I. and Chretien, C.J. (1937). “Quantitative classification of Indo-European languages,” Language, ISSN 0097-8507, 13(2). 14. Hennig, W. (1950). Grundzuge Einer Teori Der Phylogenetischen Sysematic, Deutsche Zentralverlag, Berlin. 15. Hennig, W. (1966). Phylogenetic Systematics, ISBN 9780252068140. 16. McKelvey, B. (1982). Organizational Systematics, Taxonomy, Evolution, Classification, ISBN 9780520042254. 17. Linnaeus, C. (1958). Caroli A Linn, ISBN 9781248143360. 18. Trut, L. (1999). “Early canid domestication: the farm-fox experiment,” American Scientist, ISSN 0003-0996, 87(2): 160. 19. Uvarov, E.B., Chapman, D.R., and Isaacs, A. (1964). A Dictionary of Science, ISBN 9780140510010. 20. Ayala, F.J. and Collazzoi, M (2005). “Chromosome speciation: Humans drosophila, and mosquitos,” Proceedings of the National Academy of Sciences, ISSN 1091-6490, 102: 6535-6542. 21. Kondra, A.Z., and Hinings, C.R. (1998). “Organizational diversity and change in institutional theory,” Organization Studies, ISSN 0170-8406, 19 (5). 22. Collins English Dictionary (2000). 21st Century Edition, Harper Collins Publishers, Glasgow. 23. Rose-Anderssen, C., and Allen, P.M. (2008). “Diversity and learning for innovation: dialogue for collaboration,” Journal of Management Development, ISSN 0262-1711, 27(3). 24. Rose-Anderssen, C., Baldwin, J.S., Ridgway, K., Allen, P.M., Varga, L., Strathern, M. (2009). “A cladistic classification of commercial aerospace supply chain evolution,” Journal of Manufacturing Technology Management, ISSN 1741-038X, 20(2): 235-257. 25. Maynard Smith, J. (1982). Evolution and the Theory of Games, ISBN 9780521288842. 26. Rasmusen, E. (2007). Games and Information: An Introduction to Game Theory, ISBN 9781405136662. 27. Engeström, Y. (1987). Learning by Expanding: An Activity-Theoretical Approach to Developmental Research, ISBN 9781107074422. 28. Rose-Anderssen, C., Baldwin, J.S., and Ridgway, K. (2010). “The effects of communicative interactions on meaning construction in group situations,” Qualitative Research in Organizations and Management: An International Journal, ISSN 1746-5648, 5(2): 196-215.

E:CO 2016 18(2): 90-118 | 117

29. Weatherall, D.J. (2011). “Genotype-phenotype relationships,” in Encyclopedia of Life Sciences, ISBN 9780470066515. 30. Johannsen, W. (1911). “The genotype conception of heredity,” American Naturalist, ISSN 0003-0147, 45(531): 129-159. 31. Stanford Encyclopedia of Philosophy (2001). “The Metaphysics Research Lab, Center for the Study of Language and Information (CSLI), Stanford University,” ISSN 1095-5054. 32. Leseure, M.J. (2000). “Manufacturing strategies in the hand-tool industry,” International Journal of Operations and Production Management, ISSN 0144-3577, 20(12): 1475-1487. 33. Rose-Anderssen, C., Baldwin, J., and Ridgway, K. (2011). “Cladistic classification of ancient manufacturing forms and technologies,” in H.A. ElMaraghy (ed.), Enabling Manufacturing Competiveness and Economic Sustainability, ISBN 9783642238598, pp. 551-556. 34. Alizon, F., Dallery, Y., Essafi, I., and Feillet, D. (2009). “Optimizing material handling costs in an assembly workshop,” International Journal of Production Research, ISSN 0020-7543, 47(14): 3853-3866. 35. De Toni, A., and Pannizzolo, R. (1992). “Repetitive and intermittent manufacturing: comparison of characteristics,” Integrated Manufacturing Systems, ISSN 0957-6061, 3(23): 23-39. 36. Rose-Anderssen, C., Baldwin, J., and Ridgway, K. (2011). “Commercial aerospace supply chains: the empirical validation of an evolutionary classification scheme,” Journal of Manufacturing Technology Management, ISSN 1741-038X, 22(1). 37. Belyaev, D.K. (1979). “Destabilizing selection as a factor in domestication,” Journal of Heredity, ISSN 0022-1503, 70(5): 301-308.


Complexity, conceptual models, and teacher decision-making research

Complexity, conceptual models, and teacher decisionmaking research

Applied

Marla Robertson Utah State University, USA Marla is an Assistant Professor at Utah State University in the Department of Teacher Education and Leadership in the Emma Eccles Jones College of Education and Human Services as of August, 2016. She has also worked at the University of Texas at Arlington and the University of North Texas. Her PhD is from Texas Woman's University in Reading with an emphasis on Curriculum and Instruction.

Leslie Patterson University of North Texas, USA

Informed by complexity research and models for analyzing conditions in complex adaptive systems such as schools, I describe findings from a descriptive case study of influences on teacher decision-making about writing instruction in a high-stakes writing assessment grade. I highlight how the use of complexity as a theoretical framework for research provides a unique look at education systems, particularly looking at one teachers decisions across a school semester. I focus specifically on two conceptual models from the field of human systems dynamics (HSD), one used as a conceptual framework for complex adaptive systems, and the other used as a retrospective analysis tool in describing and explaining underlying conditions at work at a particular time for a particular decision.

Introduction

W

hen I first began my teaching career, one of the many challenges in learning to be good at my job was figuring out what that meant. What did it mean to be an expert teacher? My background in business management and operations systems analysis was useful. I understood that the larger education system had many interconnected parts and that these parts worked together in ways that clearly constrained what I could do. As a beginning teacher, I often received contradictory instructions and was required to perform tasks that did not make sense to me. I saw that policies and practices at the local, state, and federal level influenced my teaching. This long-term interest led me to develop research to discover how policies

E:CO 2016 18(2): 119-136 | 119

created far from the classroom influence decisions at the classroom level—even in unintended ways. During one of my doctoral courses I was introduced to theories about complex systems and saw these as a potential theoretical framework for my research. Like Hetherington1 who explored the implications of complexity thinking and methodology in educational case study research, I was interested in conducting a qualitative case study that took into consideration the complexity of educational contexts. This article discusses what I learned from conducting qualitative research in classrooms when I used complexity science as a theoretical framework2. In fact, I found one particular approach from that body of work particularly useful—the field of human systems dynamics (HSD)3,4. HSD is a theoretically grounded yet practical approach to working in complex systems3 that proved useful in researching the classroom system I studied. The following discussion is drawn from a larger study of the decisions made by a high school writing teacher who was responsible for preparing students for a highstakes writing test. I focus on how forces in larger educational systems influenced this teacher, specifically on how assessment policies influenced decisions about writing instruction. The research study discussed here was designed from beginning to end focusing on complexity in schools. According to Cilliers5 education systems are not just complicated, these systems are complex because they involve “living things, language, cultural, and social systems"6:41. The behavior of complex systems must be determined by describing relationships rather than rules6. For this study, questions were asked about how the system was defined and structured around literacy to determine relationships among subsystems and levels in the system from state to district to school. Data were gathered to determine who or what drives change in this particular school system. Questions were asked about “not just what people do but why they do it, how they might imagine things being different, and what they would really want to do”7:123 by asking questions about what the teacher was thinking and doing, and what was contributing to those thoughts and actions8. Interviewing and observing in the system over a period of time acknowledged that education systems are self-organizing, dynamic, and emergent9,4,10. Descriptions and explanations about the system from the district level to the teaching in a teacher's classroom were gathered to showcase the “multi-dimensional, non-linear, interconnected, far from equilibrium and unpredictable” nature of complex education systems10:182. This study focused on one level in an education system, the teacher. However, focusing on one level with a complexity approach does not “reduce the multi-dimen-

120 | Robertson & Patterson


sionality, non-linearity, interconnectedness, or unpredictability” that are encountered at other levels of the system10:182. A complexity lens can provide insight in understanding underlying relationships and patterns where they happen with teachers, classrooms, and schools as human systems because the whole is present in the parts11,12. Thus, this research was designed to discover and describe relationships in this education setting, acknowledging that understanding context in education systems is absolutely critical13. Using complexity as the theoretical framework for this study influenced decisions from beginning to end from whom to interview to what questions to ask. In order to understand how a teacher makes decisions in the systems in which she works, the district language arts coordinator and principal were interviewed using a semistructured interview protocol to learn about state, district, and school policies and practices around literacy. The classroom teacher chosen for this study, Ms. Anderson (pseudonym), was selected specifically as a purposeful sample14 because she taught in a writing assessment grade in a state where a high-stakes writing assessment was part of the state accountability system. She taught Regular English I (ESL), Regular English II (ESL), and Pre-AP English I in a high school in Texas where both English I and English II require a writing assessment at the end of the school year. She was interviewed across a fall semester using a semi-structured interview protocol and observations were done in her classroom and at grade level team meetings in order to learn about these systems. The research questions were:What does writing instruction look like in a writing teachers classroom(s) in a high-stakes testing context?What influences this teacher's decisions about writing instruction? Initial data analysis for this study was done using the constant comparative method using field notes and transcriptions from interviews, classroom and grade-level team meeting observations, and student and teacher artifacts. Data were coded and patterns were identified. Using these patterns, a secondary analysis was done using one particular model from HSD as an analysis tool to further describe and explain this teacher's decisions about writing instruction. 15

Research about complex human systems

H

orn12 describes the science of complexity as the emergence of a new paradigm to help us understand social systems and subsystems and their emerging behaviors. This paradigm challenges some of the foundational assumptions of positivism because complexity science assumes that human social systems are open, diverse, nonlinear, and interdependent4. An explanation of each of these features of E:CO 2016 18(2): 119-136 | 121

complex, adaptive, and self-organizing systems and the implications for writing instruction provides a brief but useful definition of complexity. •

Open-The whole and parts of complex systems are susceptible to influence. In a classroom, learners can be viewed as agents in multiple, layered, and overlapping systems, both inside and outside of schools. On an individual level, classroom interactions can clearly be influenced by peer relationships, family expectations, recent events on campus, and even the weather. On a larger scale, classroom interactions are also open to influence from external policy mandates, public pressure, availability of resources, and changing demographics.

•

Diverse-Participants in the system are different across many dimensions-age, gender, language, ethnicity, race, sexual preference, and socio-economic status, just to name a few. The contexts and conditions that potentially might influence the system are also diverse.

•

Nonlinear-Parts of the system interact in nonlinear ways. In other words, interactions between and among students and teachers are interdependent; each response influences ongoing interactions. It is futile to look for one “root cause” of an action because the influences are many and massively entangled. This nonlinearity, of course, calls into question any assumptions we may have about direct causal links between policy and practice.

•

Unpredictable-Particular actions are not predictable, although patterns emerge over time. In schools, all of these diverse, nonlinear interactions make for unpredictable student (and teacher) responses-responses to one another, to the curriculum, to high stakes tests, and to teachers.

These attributes contribute to the continual emergence of new patterns in these complex systems. Some theorists use the term “self-organizing” to point to the fact that change is generated within these systems-from the interaction among these interdependent components or agents of the system. This perspective offers a powerful lens for the study of teacher decision making. Lemke and Sabelli7 propose the development of a conceptual framework for analyzing education as a complex system, calling for a change in perspective. In their research on analyzing the effects of educational reforms they learned that education systems are complex and challenging systems to research and that using a complexity perspective acknowledges that changes in education systems are not independent. For example, in their research on interventions Lemke and Sabelli note that “proposed changes at the classroom level have implications at school and district levels (e.g., for teacher development, parental expectations, school resources, accountability, and 122 | Robertson & Patterson


so on) and need to be supported by related interventions across multiple levels”7:128. Complexity offers explanations and descriptions of how things actually are in many school systems—self-organizing, dynamic and emergent10. In encouraging complexity perspectives, Kuhn10 addresses complexity and education this way: Complexity and education may be brought together because in the language of complexity, such human cultural settings, productions and institutions as educational endeavor are complex and dynamic. Individual human beings (learners, educators, and administrators), various associations of individuals (classes, schools, universities, educational associations) and human endeavor (such as educational research) are multi-dimensional, non-linear, interconnected, far from equilibrium and unpredictable.10:182

A complexity approach notes that there is complexity in this human system at all levels and that focusing on one level does not “reduce the multi-dimensionality, nonlinearity, interconnectedness, or unpredictability encountered”10:182 at the other levels. With human systems, the whole is present in the parts. Complexity offers insight into teachers, classrooms, and schools as human systems, looking at learning patterns and shifts in those patterns where they happen11,12. Complex systems approaches are becoming more common in education research, for example, in the study of educational reform16,7, at-risk populations17, learning culture18, curriculum19,20, and professional development21. Other researchers using complexity theories in education include perspectives on teaching and classroom practice in social studies classes19, considering small-group project based learning from the perspective of complex adaptive systems22, and analyzing teacher learning and learning communities as complex systems23,24,25,26. In another example, a recent review of the literature on teachers’ professional development practices highlights the need for a “complex conceptualization of teacher professional learning”27:377 drawing heavily on complexity theories. Also, many literacy researchers have published studies using complexity thinking across various topics28,25,29,30,31. My research was designed to contribute to this wide array of educational research using complexity.

Complexity and human systems dynamics

I

found the work of Eoyang in human systems dynamics32 particularly useful in studying teacher decision making. Complexity theories up until now have been mainly descriptive, but human systems dynamics not only describes but can also be used to explain how they work and how they can be influenced to bring about change. Eoy-

E:CO 2016 18(2): 119-136 | 123

ang32, in analyzing human systems, developed an integrated model of self-organizing human systems by bringing together principles from mathematics, physical and social sciences. The field of theory and practice emerging from Eoyang's research3 has been applied in multiple disciplines and in multiple contexts. It continues to evolve, but two foundational concepts have been essential to my research about teacher decisionmaking. The first is “pattern logic,” or the assumption that in complex adaptive systems, agents interact to generate patterns of behavior, discourse, expectations, etc., and that any agent within the system can look for those patterns, name them, interpret them, and generate options for action that may have the potential for shifting those patterns. The second foundational concept in HSD is that in all complex systems, we can identify the three underlying conditions for self-organization. These two concepts, or models, are described in detail below.

Pattern Logic

A

lthough there are many definitions and representations of complex adaptive systems33,34 the Eoyang Pattern Logic model is represented as a process with a pattern-forming cycle4. This provided the conceptual model for complex adaptive systems used in my research, and the Conditions for Self-organizing Systems model (described below) was used during data analysis as a retrospective analysis tool as a way to see and set conditions for patterns (see Figure 1). In Eoyang and Holladay's work, a complex adaptive system “is a cluster of individual parts that interact with each other, and over time system-wide patterns appear... Those patterns then influence later interactions of the agents”4:15-16. The Pattern Logic model also incorporates foundational philosophies of perception and knowledge that are particular to the workings of human systems. The model is designed to capture, in both theory and practice, “the dynamics of human systems at all scales in ways that inform decisionmaking and action taking in complex and uncertain environments”3:636. In Figure 1, the agents (people, groups, or ideas) are the circles at the bottom that interact with each other. The arrow at the right shows how, over time, the interaction of the individual parts creates a coherent pattern. After this pattern appears, it then feeds back into the individual agents encouraging those agents to adopt the pattern in future cycles. According to Eoyang and Holladay4, because relationships between agents are in constant flux, “a stable, permanent reality is impossible; uncertainty becomes the rule”4:16. Also, systems are not limited to members within the system as members of other systems may be shared and out of view. For example, “one individual agent may be a member of a team, and be influenced by patterns outside of the team inside the organization, by the patterns in his or her own family and community, even by political forces on a national or global level”4:17. 124 | Robertson & Patterson


Figure 1 Human Systems Dynamics Pattern Logic (Complex Adaptive Systems) diagram35:9. Used with permission. There are many examples of complex adaptive systems in human interactions. In human systems, emergent patterns are generated when parts interact at the same time as “patterns influence parts and their interactions. The result is a self-generating self-organizing reality of human systems dynamics”4:18. This worldview applied to human systems requires different ways of thinking and is foundational for this study of teacher decision-making in a school context.

Conditions of self-organizing systems (CDE model)

T

he second foundational concept or model developed by Eoyang that was particularly useful for studying teacher decision-making describes the three conditions of self-organizing systems3. I will refer to this model as the Conditions of Self-Organizing Systems model or the CDE model interchangeably throughout the paper. This model is designed to explain self-organizing processes in human systems, although Eoyang has challenged scholars to engage in inquiry that may move this model from “the realm of conceptual ... into the realm of computational modeling”3:637. A pattern is defined as “similarities, differences, and connections that have meaning across space and time”4:4. The CDE model is also designed to provide information on ways to influence or act upon the system, although only the piece designed to describe and explain the system was utilized for my research study. Thus, this model is used as a tool in this research study to describe and explain patterns in the similariE:CO 2016 18(2): 119-136 | 125

ties, differences, and connections in the data gathered from the participating teacher about the systems in which she is a part. Use of this model as an analysis tool provided insight into decisions that the teacher was making during my study at the moment the decision was being made. The CDE model is described in detail below. This model contains three meta-variables representing conditions that influence processes for self-organizing in a human system. Container The first meta-variable is called container (C) and represents any conditions or parameters that bind the agents of the system “close enough and long enough that they will interact to create a new pattern”4:27. Containers can be physical, conceptual, or social - anything that is similar in the system. For example, in an educational system a physical container can be a classroom, a school building, or a state boundary. A conceptual similarity may be a shared idea or philosophy of teaching and learning or a shared language. A social boundary may be age or participation in a school club. A conceptual container may be a state identity, a school mission, or a religious belief. A container can be “a bounding condition (fence), an attractive condition (magnet), or a combination of multiple mutual attractions (network)”3:636. Sometimes containers are referred to as similarities. Difference The second meta-variable in the CDE model is difference (D). This variable is designed to capture all the differences that may influence change in a human system. Differences can be any kind as long as they are significant to the agents in the system. Differences can show a pattern as it emerges, but differences can also show potential for change in the system. “At any given moment, in any given human system, at any given scale, an indeterminate number of differences articulates the systemic pattern and holds the potential of the system to change”3:636. The differences that matter are the differences that influence self-organizing processes and are sometimes called “differences that make a difference”4:28. In one example, state or local policies in schools are sometimes put in place over the summer when teachers are not at work. Sometimes these policy changes require teachers to adjust room assignments, teaching assignments, lesson plans for instruction, curriculum choices, or other parts of their work quickly at the beginning of the school year. Some of these required changes may be small, but others may require major decisions by those affected by the changes. In another example, a collaborative teaching partner may get sick and go on an extended leave from school leaving the remaining teacher to adjust to the loss of a colleague in various ways. The main idea, though, is that some differences make a difference to the agents in the system and some do not.



Exchange The third meta-variable in the CDE model is exchange (E). An exchange is a connection that carries energy, resources, or information among or between the parts of the system. Exchanges show connection between parts of the system and provide information about relationships during the entire self-organizing process. Eoyang and Holladay call exchanges the “engine for self-organizing change”4:29. For example, a school newsletter may be distributed once a semester to the parents through the regular mail in English. This exchange of information may be delivered in alternative ways (e.g. e-mail, posted on a website). It may be delivered more or less often (e.g., once a week, once a month, twice a semester), and it may be translated into other languages depending on the demographics of the student population. Any of these would be a change in an exchange that may influence the school as a self-organizing system in a variety of unpredictable ways. The CDE model helps name and explain underlying relationships that influence the process of emergence in self-organizing human systems. Change in one (C, D, or E) results in changes in the other(s) as they are connected and dependent on each other. It is also important to note that the model is scale free (not hierarchical)4 and can be used at multiple scales in human systems. For this research, the model was used at the level of the teacher. The Conditions of Self-organizing Systems model has been used in research to analyze complexity in various disciplines. For example, researchers used the model to study international relations and foreign policy36, social policy37, and education35,38. In education, Patterson et al.35 used the model in their work with schools as a way to teach agents in a complex school system to notice, understand, and influence patterns around change in schools. They learned that when agents in the system know how to recognize patterns that represent conditions for self-organization in the system, those agents can then make decisions and take action to shift one or more of the conditions which could influence the emergent patterns. In other research in schools, high school English teachers used their understanding of containers, differences, and exchanges as conditions for self-organization to make decisions that influenced learning in their classrooms, particularly for English language learners38.

Use of CDE model in research of teacher decision-making

A

complexity theoretical framework informed by Eoyang's Pattern Logic model was used as a foundation for the design and implementation of my research. The initial data analysis phase yielded many patterns about Ms. Anderson's teaching and about what influenced her decisions about writing instruction. For exE:CO 2016 18(2): 119-136 | 127

ample, for research question one, about what writing instruction looks like in a writing teachers classroom(s) in a high-stakes testing context, I found a variety of patterns, such as: (a) Ms. Anderson used a variety of resources and new ways to teach her class, (b) Ms. Anderson promoted choice, audience, and discussion in her classroom, and (c) Ms. Anderson modeled herself as the kind of learner she wanted her students to be. Other patterns were found for this research question as well. For research question two about what influences this teacher's decisions about writing instruction, I also found patterns, such as: (a) Ms. Anderson's opportunities to write curriculum for her school, district, and for outside of school curriculum influenced her decisions about writing instruction, (b) Ms. Anderson was influenced in a variety of ways by testing practices at all levels of the system - state district, school, and grade level, and (c) Ms. Anderson's instruction was influenced by her beliefs about students. Other patterns were found as well. The CDE model was used in this research as a data analysis tool. Any of the patterns found in the initial analysis phase could be examined more closely using the CDE model as a retrospective analysis tool to speculate about or explain what conditions may have been at work at a particular time and place for a particular decision. I chose to focus on the pattern that testing influenced Ms. Anderson's decisions about writing. Once I chose a pattern to analyze, I reviewed the data that contributed to the pattern of interest looking for points in time where Ms. Anderson identified a challenge, tension, or constraint that led to a decision. HSD uses the word tension as it is used in physical systems (like the tension in a rubber band)4. Tensions emerge where there are differences and tend to influence decisions made in the system. Through this process I chose three decisions made by Ms. Anderson that the initial data analysis showed to be directly or indirectly related to testing policies and practices. These decision points represented three scales of decisions, a policy decision (why), a curriculum decision (what), and an instruction decision (how). I then analyzed these separate decisions using the Conditions of Self-organizing Systems model to try to identify, describe, and explain conditions that may have triggered or generated the decision at that point in time.

Use of conditions of self-organizing systems model for analysis In order to show how I used this model as an analysis tool at this point in my research, I describe the analysis of Ms. Anderson's decision to use a series of lessons culminating in concept maps (a curriculum decision). This decision point was identified in the 128 | Robertson & Patterson


initial analysis as a decision that was influenced by multiple patterns noted in the data. One pattern was that Ms. Anderson's experience with curriculum writing influenced her decisions about writing instruction in her classroom. This teacher decision was also indirectly influenced by End of Course (EOC) testing requirements by the state because one of the factors included in this decision related to particular types of questions on the EOC test that required a written answer. Also, particularly for Ms. Anderson's Pre-AP students, a testing influence on this decision was consideration of the type of AP writing those students would be required to do on future AP tests. This particular teacher decision also was influenced by curriculum in that it revolved around the type of lessons that would benefit students most for particular requirements from the curriculum for the course - analyzing themes in literature, providing textual evidence, and embedding quotes. CDE analysis. In previous years, Ms. Anderson required students to write an essay after analyzing the theme in a novel using textual evidence to support their thesis, a requirement of the curriculum. This year she chose to have students create concepts maps instead. For my analysis of this decision I chose three containers (C) identified from the data that pertained to this decision. One container was the English department chair and Ms. Anderson planned together for Pre-AP English I on a regular basis. Although Ms. Anderson chose to incorporate concept maps for all of her classes (Pre-AP and regular), the fact that she had the English department chair to work with on this curriculum piece for her Pre-AP classes was an important factor. A second container was the nature of writing assignments in this course. The third container was curriculum criteria as Ms. Anderson had definite ideas about how curriculum fit into her decisions for her classroom. For each container I identified in the data three differences (D) and three exchanges (E) that were relevant to this decision. Following is a CDE analysis of this decision (Table 1). To show the type of data that was used to create this table, here is an excerpt from a transcript of Ms. Anderson's thinking about her decision to use concept maps this year. During an interview, I asked her where the series of concept map lessons came from and if these lessons were something the rest of her grade level team members were using. She replied that she and her neighbor English I Pre-AP teacher (the English department chair) were doing them and described her thought processes about this decision. [I]t came from a combination of things but it was the idea of a Teaching Channel ... videos. One of them is laying the foundation and she's talking about literary analysis and then the other one is pattern holders ... then I read a blog ... of a teacher who is

E:CO 2016 18(2): 119-136 | 129

Containers

Differences

Exchanges

1) English department chair plans together with Ms. Anderson for Pre-AP classes

•

•

•

•

2) Nature of writing assignments

• • •

3) Curriculum criteria

•

• •

Consistency in planning calendar for Pre-AP Coherence with curriculum for all Pre-AP English I students (not used by any other teachers in the school) Coordinate use of lesson plans for various concept map activities – grammar and Ender’s Game

•

•

Collaborate often in the hall and after school Share ideas and resources for all curriculum units for PreAP Share ideas for other English courses taught by Ms. Anderson

Engage in lengthy writing • projects from idea to publication • Incorporate different types of writing – literary analysis with embedded quotations Incorporate school initiatives – critical writing and project-based learning •

State and AP curriculum requirements Outside of school -professional development, professional books, National Writing Project (NWP) colleagues Internet through The Teaching Channel, blogs, etc.

Provide foundational thinking for future learning (literary analysis and analyzing themes) Fits multiple rigorous requirements – learning by doing Consistency with state standards and assessment requirements (particularly to provide textual evidence and writing similar to short answer and crossover questions on the EOC)

State curriculum requirements State reading and writing assessments Voluntary professional development

• • •

Table 1 CDE Analysis for Decision to Use Concept Maps Lessons talking about a concept map ... that was his culminating project that matched with this work ... and then there's another blogger who [wrote] ... about bringing creativity back into the English classroom. I had come across that before but then actually Kylene Beers referenced him because he took her signposts stuff and was showing how he had his kids use the signposts, and they do concept maps all the time ... and so all of that thinking basically helped me decide how I wanted my kids to show me that they could analyze a theme in a novel using the signposts and using textual evidence.



Other data (classroom documents, curriculum documents, grade level team meeting notes, transcripts from other interviews on this topic, etc.) were also used to analyze this decision. The CDE analysis showed that there were several reasons why Ms. Anderson chose to use concept maps with her students. For one, she was searching for new resources. When she came upon these lessons in her search, there were enough similarities in the lessons with the curriculum for all of her classes and with the requirements for the EOC tests at the end of the semester that choosing to incorporate concept maps in her curriculum for all of her classes was an effective teacher decision. The fact that Ms. Anderson still had the English department chair available to collaborate with her on these concept map lessons (they were also incorporating close reading lessons together) made it possible for her to also include concept maps into her Regular English I and Regular English II classes even though there were no other teachers of those classes using these lessons. A difference that made a difference for this decision was that the concept map project required higher level thinking from all of her students in all of her classes than what she had done in previous years, filling a need in her curriculum while also fitting in with a school initiative on critical writing identified in a previous interview. Also, this analysis provided additional insight supporting patterns from the initial data analysis. For example, the analysis provided more descriptive and explanatory detail on this particular curriculum decision that affected Ms. Anderson's Pre-AP and Regular classes through sharing ideas and resources in various ways with the English department chair. This supported a pattern from the initial analysis that Ms. Anderson valued collaboration with her colleagues. The analysis also supported initial patterns noted that writing needed to fit the needs of students, that writing assignments needed to involve the writing process and have purpose, that writing assignments must also consider curriculum requirements, and that resources were available from a variety of places to support this idea. Other patterns from the initial analysis of data corroborated in this analysis were that (a) curriculum should have multiple purposes, (b) curriculum needs to fit state curriculum and assessment requirements, (c) curriculum resources can be found from outside of school sources, and (d) curriculum should provide foundational learning for students. The CDE analysis of Ms. Anderson's decision to use concept maps this year showcased how an idea emerged out of her self-organizing process in response to many factors, curriculum requirements, school initiatives, and EOC testing requirements, to name a few. This specific instance also helped to see the pattern more clearly that Ms.

E:CO 2016 18(2): 119-136 | 131

Anderson's instructional decisions were significantly influenced by state curriculum and state testing requirements, among many other things. Although I have only included one example of using the Conditions of Self-organizing Systems model as an analysis tool, using this model to analyze these decision points provided more detailed description and explanation of a variety of decisions made by Ms. Anderson during this study. The purpose for these additional analyses was to look more closely at a pattern found during the initial data analysis that state testing influenced Ms. Anderson's decisions about writing instruction by using three scales of influence—policy, curriculum, and instruction. These analyses provided additional insight into the conditions at work in this system that helped shape decisions at each of these levels for Ms. Anderson. Analysis of all three decision points showed that high-stakes testing was extremely influential in decision-making at all of these scales, supporting a finding in the research.

Discussion

U

sing a complexity lens and HSD tools for analysis of decisions helped make visible many patterns in this unique education context. For example, patterns showed that for Ms. Anderson collaboration was important, writing assignments needed to meet the needs of students, and it was important for her to know what students were learning, to name a few. Also, many agents were identified using this lens as influential in her decision-making process. These included a wide variety of people outside of school, colleagues, contextual factors, and teacher's beliefs about teaching and learning that interconnected in complex ways to influence teaching decisions for her. Use of a complexity lens made visible many of these influences and connections that would not otherwise have been noted. The lens also provided data on the unique context for Ms. Anderson and the differences that made a difference to her in her work and in her ability to make decisions. The patterns showed those differences included these important factors: she was treated as a professional, she used a flexible framework for class instruction, she was a continual learner, she had the ability to integrate multiple factors, and she saw the big picture when making decisions. Use of the CDE model as a retrospective analysis tool in conjunction with a complexity research framework provided me with a way of thinking about the complexity involved in deceptively simple decisions that teachers make on a daily basis. Looking for containers, differences, and exchanges helped me identify, describe, and explain possible influences on a particular decision at a specific point in time that may have triggered or generated decisions made by Ms. Anderson. In this case I chose decisions 132 | Robertson & Patterson


related to tensions noted about testing policies and practices although this type of analysis could be used for any decision at a moment in time. This analysis was useful in helping me answer my research question about what influences teachers’ decisions about writing in a high-stakes testing grade and contributed to the findings from this research study. The use of a complexity lens and HSD conceptual models provided data that led to several major findings in this study. They include: (a) teaching is a complex adaptive nonlinear decision-making process, (b) there are networks of influence and networks of decisions that affect decisions that teachers make, (c) influence can go both ways, from the system to the teacher and from the teacher to the system, (d) teacher beliefs are not static, (e) HSD offers descriptions and explanations of decisions made by teachers, (f) use of HSD models offers a step towards understanding underlying conditions that are in place that potentially explain decisions that teachers make, and (g) contextual factors are important in understanding teacher decision-making. These findings would not have been possible without a complexity lens and the use of HSD conceptual models. Complex systems theories helped to describe the “individual, surprising, and not a little perverse” context for Ms. Anderson in her decision-making for her classroom7:129. Even though analysis from this research study was not predictive, knowledge gained from this research shows the overall features of how change takes place in the work of a particular teacher and how she fits into her individual educational system. From this research I learned possible reasons why she made the decisions that she made, what she was thinking when she made them, and how she was planning to go forward in helping her students reach their goals for the school year and for their future lives. This type of detailed knowledge can inform future decisions about how to train teachers to make effective decisions about their work from the types of coursework offered in universities to professional development provided in schools. This type of detail about education systems and what similarities, differences and connections influence what emerges in the system has the potential to inform policy decisions about education in general. Although “[w]e cannot have complete knowledge of complex systems”5:528, use of this framework and these analysis tools have the potential to provide researchers with more detailed knowledge of complex systems such as schools. Even though the descriptions and explanations were partial, based on the frameworks used, these descriptions can provide some of the “qualitative features of the change process that may not be intuitively evident to a linear logic of cause and effect”7:124. In this study

E:CO 2016 18(2): 119-136 | 133

I learned that teaching is a complex process taking place in complex human systems called schools and was able to describe and explain potential conditions that triggered or generated decisions made by this particular teacher in her context. A complexity lens informed by the Pattern Logic model and analysis tools such as the Conditions of Self-Organizing Systems model show promise as concepts and tools for research in complex systems. As educational researchers we must acknowledge that an understanding of complex systems is critical in educational research if we are to learn more about how schools work and how to improve them.

References 1. Hetherington, L. (2013). “Complexity thinking and methodology: The potential of ‘complex case study’ for educational research,” Complicity: An International Journal of Complexity and Education, ISSN 1710-5668, 10(1/2): 71-85. 2. Robertson, M.K. (2014). “Influences on teacher decision-making about writing instruction in a high-stakes writing assessment grade,” http://gradworks.umi.com/36/37/3637176. html. 3. Eoyang, G. (2012). “Human system dynamics: Toward a computational model,” Numerical Analysis and Applied Mathematics, ISSN 0094-243X, 1479: 634-637. 4. Eoyang, G., and Holladay, R. (2013). Adaptive Action: Leveraging Uncertainty in Your Organization, ISBN 9780804787116. 5. Cilliers, P. (2005). “Complexity, deconstruction and relativism,” Theory, Culture and Society, ISSN 0263-2764, 22(5): 255-267. 6. Cilliers, P. (2000). “Rules and complex systems,” Emergence, ISSN 1521-3250, 2(3): 40-50. 7. Lemke, J.L., and Sabelli, N.H. (2008). “Complex systems and educational change: towards a new research agenda,” Educational Philosophy and Theory, ISSN 0013-1857, 40(1): 118129. 8. Rink, J.E. (1993). “Teacher education: A focus on action,” Quest, ISSN 0033-6297, 45: 308320. 9. Davis, B., and Sumara, D. (2006). Complexity and Education: Inquiries Into Learning, Teaching, and Research, ISBN 9780805859355. 10. Kuhn, L. (2008). “Complexity and educational research,” Educational Philosophy and Theory, ISSN 0013-1857, 40(1): 177-189. 11. Davis, B., Sumara, D., and D’Amour, L. (2012). “Understanding school districts as learning systems: some lessons from three cases of complex transformation,” Journal of Educational Change, ISSN 1389-2843, 13: 373-399. 12. Horn, J. (2008). “Human research and complexity theory,” Educational Philosophy and Theory, ISSN 0013-1857, 40(1): 130-143.



13. Trombly, C.E. (2014). “Schools and complexity,” Complicity: An International Journal of Complexity And Education, ISSN 1710-5668, 11(2): 40-58. 14. Patton, M.Q. (2002). Qualitative Research and Evaluation Methods, ISBN 9780761919711. 15. Glaser, B. G., and Strauss, A. L. (1967). The Discovery of Grounded Theory, ISBN 9780202302607. 16. Goldspink, C. (2007). “Rethinking educational reform: A loosely coupled and complex systems perspective,” Educational Management Administration and Leadership, ISSN 1741-1432, 35(1): 27-50. 17. McGee, E. (2013). “Threatened and placed at risk: high achieving African American males in urban high schools,” Urban Review, ISSN 0042-0972, 45(4): 448-471. 18. Zhang, J. (2010). “Technology-supported learning innovation in cultural contexts,” Educational Technology Research and Development, ISSN 1042-1629, 58(2): 229-243. 19. Collins, S., and Clarke, A. (2007). “Activity frames and complexity thinking: honoring both public and personal agendas in an emergent curriculum,” Teaching and Teacher Education, ISSN 0742-051X, 24: 1003-1014. 20. Doll, W. E. (2008). “Complexity and the culture of curriculum,” Educational Philosophy and Theory, ISSN 0013-1857, 40: 190-212. 21. Adams, R.S., and Felder, R.M. (2008). “Reframing professional development: a systems approach to preparing engineering educators to educate tomorrow’s engineers,” Journal of Engineering Education, ISSN 1069-4730, 97(3): 239-240. 22. Mennin, S. (2007). “Small-group problem-based learning as a complex adaptive system,” Teaching and Teacher Education, ISSN 0742-051X, 23: 303-313. 23. Brown, K.D., and Kraehe, A.M. (2010). “The complexities of teaching the complex: examining how future educators construct understandings of sociocultural knowledge and schooling,” Educational Studies, ISSN 0305-5698, 46: 91-115. 24. Davis, B., and Sumara, D. (2001). “Learning communities: understanding the workplace as a complex system,” New Directions for Adult and Continuing Education, ISSN 1052-2891, 92: 85-94. 25. Davis, B., and Sumara, D. (2007). “Complexity science and education: Reconceptualizing the teacher’s role in learning,” Interchange, ISSN 0826-4805, 38: 53-67. 26. Fenwick, T. (2012). “Complexity science and professional learning for collaboration: a critical reconsideration of possibilities and limitations,” Journal of Education and Work, ISSN 1363-9080, 25: 141-162. 27. Opfer, V. D., and Pedder, D. (2011). “Conceptualizing teacher professional learning,” Review of Educational Research, ISSN 0034-6543, 81(3): 376-407. 28. Sumara, D. (2000). “Critical issues: Researching complexity,” Journal of Literacy Research, ISSN 1086-296X, 32(2): 267-281.

E:CO 2016 18(2): 119-136 | 135

29. Jordan, M.E., Schallert, D., Cheng, A., Park, Y., Lee, H., Chen, Y., and Chang, Y. (2007). “Seeking self-organization in classroom computer mediated discussion through a complex adaptive systems lens,” Yearbook of the National Reading Conference, ISSN 0547-8375, 56: 39-53. 30. Robinson, R., and Yaden, D.B. (1993). “Chaos or nonlinear dynamics: implications for reading research,” Reading Research and Instruction, ISSN 0886-0246, 32(4): 15-23. 31. Schwartz, R.M., and Gallant, P.A. (2009). “Literacy learning and instruction: in search of complexity,” Journal of Reading Recovery, ISSN 1538-6805, 1: 61-65. 32. Eoyang, G. (2001). Conditions for Self-Organizing in Human Systems, https://www. researchgate.net/publication/252600227_CONDITIONS_FOR_SELF-ORGANIZING_IN_ HUMAN_SYSTEMS. 33. Buckley, W., Schwandt, D., and Goldstein, J.A. (2008). “Society as a complex adaptive system,” Emergence: Complexity & Organization, ISSN 1521-3250, 10(3): 86-112. 34. Hays, J. M. (2010). “Mapping wisdom as a complex adaptive system,” Management and Marketing, ISSN 1842-0206, 5(2): 19-66. 35. Patterson, L., Holladay, R., and Eoyang, G. (2013). Radical Rules For Schools: Adaptive Action For Complex Change, ISBN 9780615766263. 36. Lehmann, K.E. (2012). “Unfinished transformation: The three phases of complexity’s emergence into international relations and foreign policy,” Cooperation and Conflict, ISSN 0010-8367, 47: 404-413. 37. Eoyang, G., and Yellowthunder, L. (2005). “Beyond bureaucratic boundaries: A case study in human systems dynamics,” https://www.researchgate.net/publication/237459001_ Politics_and_International_Affairs_Beyond_Bureaucratic_Boundaries_A_Case_Study_in_ Human_Systems_Dynamics. 38. Patterson, L., Wickstrom, C., Roberts, J., Araujo, J., and Hoki, C. (2010). “Deciding when to step in and when to back off,” The Tapestry Journal, ISSN 1949-8268, 2(1): 1-18.


Four domains of complexity

Classic Paper

Four domains of complexity Gerald Midgley Hull University, ENG

In this short paper, which reflects on one of my contributions to the systems literature in 1992 (Pluralism and the Legitimation of Systems Science), I discuss the context at that time. Systems scientists were embroiled in a paradigm war, which threatened to fragment the systems research community. This is relevant, not only to understanding my 1992 contribution, but also because the same paradigms are evident in the complexity science community, and therefore it potentially faces the same risk of fragmentation. Having explained the context, I then go on to discuss my proposed solution to the paradigm war: that there are four domains of complexity, three of which reflect the competing paradigms. The problem comes when researchers say that inquiry into just one of these domains is valid. However, when we recognise all four as part of a new theory of complexity, we can view them as complementary. The four domains are natural world complexity, or “what is” (where the ideal of inquiry is truth); social world complexity, or the complexity of “what ought to be” in relation to actual or potential action (where the ideal of inquiry is rightness); subjective world complexity, or the complexity of what any individual (the self or another) is thinking, intending or feeling (where the ideal of inquiry is understanding subjectivity); and the complexity of interactions between elements of the other domains of complexity in the context of research and intervention practice. Following a discussion of the relevance of this theory for complexity scientists, I end the paper with a final critical reflection on my 1992 paper, pointing to some theoretical assumptions and terminology that I would, in retrospect, revise.

E:CO 2016 18(2): 137-176 | 137

Introduction

I

would like to start by thanking Kurt Richardson, the editor of Emergence: Complexity and Organization (E:CO), for asking me to write this introduction to my own paper, Pluralism and the Legitimation of Systems Science1. It is very rare to have the opportunity to publicly reflect, 25 years later, on one's own older work, and I am honored that Kurt considered the paper significant enough to be included in E:CO's “classic papers” series. Below, I will do three things. First, I will explain the context in which I wrote the paper, as the whole framing of it might be puzzling for those reading this in 2016 or later.

Second, I will briefly summarize what I believe is of value in this paper for complexity theorists and practitioners. The paper was not intended as a contribution to the literature on complexity (it was primarily aimed at the systems science research community), but I was aware at the time that, if researchers were to follow up the implications for developing our understanding of complexity, it might lead complexity scientists down a considerably different road than the one that the majority have taken. This is now a chance to start that follow up discussion—better late than never! The third and final section below explains what I would do differently if I could go back 25 years and write the paper again. Back then I was nearing the end of my doctoral studies, and this paper1 and three others2,3,4 together summarize the arguments in my PhD dissertation5. I smile now at the number of theoretical assumptions and uses of terminology that I would revise today, if writing the paper again with the benefit of hindsight. Having said this, I still believe that much of my 1992a analysis of four types of complexity stands up to scrutiny today, even if I now question some of the social/linguistic theory that I drew upon to develop it. Further, I reckon that the argument has practical implications for understanding how complexity theory can be translated into methodology and practice in new ways.

The context

I

n the late 1980s and early 1990s, systems scientists were dealing with what might be termed an “existential crisis”, with the clashing of several incommensurable paradigms and a consequent fragmentation of their research community into competing camps. The history of this paradigm war is instructive, not just because it provided 138 | Midgley


the context for my own contribution to the literature at that time, but also because we can see the same divisions in complexity science today6, and hence the potential is there for the fragmentation of the complexity research community too. In brief, following the consolidation in the 1940s and 1950s of some important transdisciplinary theories in the fields of systems science7,8,9,10, cybernetics11,12,13 and complexity science14,15, there was a push in the systems community to embody these theories in methodologies for intervention to support systemic organizational and social change. These methodologies included systems analysis16,17, systems engineering18,19, system dynamics20,21 and viable system modelling22,23. The authors of these methodologies tended to adopt an approach that assumed the need for expert-driven modelling of real world complex systems, and they strove for comprehensiveness in their models while emphasizing quantification, prediction and control24. This whole approach ran into problems in the 1960s and 1970s, and strong criticisms were advanced of the assumptions built into it25,26,27,28. As a consequence, the late 1970s and early 1980s saw the emergence of a new paradigm with its own methodologies29,30,31,32,33 based on very different assumptions. The expert researcher was replaced by a facilitator, whose role was to include stakeholders in participative, qualitative modelling32. The meaning of the term “expertise” was thereby democratized to refer to relevant knowledge held by those involved in and affected by a problematic situation34. The emphasis was no longer on systems as real world entities, but instead attention was switched to how collaborative groups could develop better systemic understandings of potential actions: a “system” became a useful way of viewing the world rather than something that can be assumed to exist objectively30. With this shift came recognition of the inevitable lack of comprehensiveness in every analysis34,35, and hence a relaxation in assumptions about prediction and control, with more of an emphasis on the need for better mutual understanding between stakeholders30, dialogue35,33 and learning36,37,38,39. It was at this time that the terminology of “hard” and “soft” systems was first proposed30, with hard systems methodologists being those who wanted experts to quantify analyses of real world systems, and soft systems methodologists being those who wanted facilitators to support dialogue around different ways of seeing systems and possible actions to change them. Thankfully, in more recent years, this divisive language has become largely redundant40. Of course, the advocates of the first paradigm didn't go away when the second one was proposed, and a paradigm war ensued. By the time we had hit the late 1980s, when I wrote my own first contributions to the literature on systems methodology41,42,43,44, it was evident that the war was tearing the systems research com-

E:CO 2016 18(2): 137-176 | 139

munity apart, with advocates of the different paradigms denigrating their opponents and refusing to participate in cross-boundary dialogues. It was in this context that a new paradigm called “critical systems thinking” was proposed45,46, and this had two principle aims: first, to deepen our understanding of how power relations can be addressed during intervention35,47,2,48, given that both previous paradigms were arguably rather naive in their analyses of power; and second, to develop an adequate theory and practice of methodological pluralism so we could transcend the paradigm war and draw upon the best from both previous paradigms to create a much more flexible and responsive approach to systemic intervention49,50,51,24,52,53,47,54,45,55,56,57,1,5,58,59,60,61,46,62,63. My 1992a paper was a relatively early contribution to critical systems thinking, with a specific focus on developing a theory of complexity to underpin the practice of methodological pluralism, and thereby transcend the paradigm war. My proposal was for the identification of four domains of complexity, and the methods from the various competing methodologies could be aligned with these according to the ideal of inquiry they embodied. More details are provided below. It is my contention that this argument may be of value to complexity theorists and practitioners who may have to deal with paradigmatic divisions in the complexity research community along similar lines to those that were previously encountered and addressed in the systems community.

The value of the paper for complexity theorists and practitioners

T

he four domains (or types) of complexity that I proposed in my 1992a paper were:

•

“Natural world” complexity, or the complexity of “what is”. The ideal of inquiry into this form of complexity is truth—but note the term “ideal” which, following Popper64,65, indicates that truth is something we aim for, but we can never know for certain whether it has been achieved.

•

“Social world” complexity, or the complexity of “what ought to be” in relation to actual or potential action. The ideal of inquiry into this form of complexity is rightness.

•

“Subjective world” complexity, or the complexity of what any individual (the self or another) is thinking, intending or feeling. The ideal of inquiry into this form of complexity can be called understanding subjectivity

140 | Midgley


•

.We very often have to deal with interactions between phenomena in the above three domains of complexity. This means that there is also the meta-level complexity of these interactions, which needs to be a focus of inquiry. However, it's important to note that, in the context of intervention (rather than just observation), metalevel analyses may not stay “meta” for long: communication of them, and action upon them, may feed back to change the original pattern of interactions.

In my 1992 paper1, I drew upon Habermas's66,67,68 linguistic theory of ‘three worlds’ to underpin the first three of the above domains of complexity (the fourth comes about because the first three interact). In brief, Habermas argues that any sentence intended for communication has three implicit validity claims: a claim that its propositional content is true; that it is the right thing to say in the context; and that the speaker is sincere in saying it. These validity claims refer to three worlds: our external natural world; our normative social world; and my (or your) internal subjective world. According to Habermas, a rational, free and fair dialogue is one where anything that is said can, in principle, be opened up to critique on the basis of truth, rightness or sincerity. When some aspect of potential critique is repressed (for example when a company allows its employees to participate in discussing the means to achieve already-given ends, but those ends are not open for discussion), this produces ‘distorted communication'. In three short, logical steps, we can move from Habermas's linguistic theory to my own proposal for the first three domains of complexity above. First, we can view Habermas's three “worlds” as foci for research and inquiry, and not just rational argumentation in dialogue. Second, this broadening of the focus beyond dialogue means that we have to be concerned with more than just the sincerity of speakers when we consider subjectivity: it is the whole panoply of intentions, thoughts and feelings that come to be of interest in inquiry. Third, why would we need research and inquiry if there were no uncertainties, and hence underlying complexities, to deal with? Seeing Habermas's “worlds” as the first three domains of complexity therefore makes sense— and, as we have seen, the fourth domain of complexity concerns how phenomena in the other domains interact. Now, Habermas67,68 said his theory was ontological, as he claimed that it is the intrinsic properties of language that enable us to distinguish between the natural, social and subjective worlds. In 1992, I therefore labelled the fourth (meta) level of complexity ‘ontological complexity’ to indicate that all three of the other forms of complexity and their interactions are essential to consider when dealing with any non-trivial issue requiring systemic action research: ignoring one or more of the domains will result in

E:CO 2016 18(2): 137-176 | 141

missing a significant source of complexity, and this will impoverish analysis, understanding and action. I think the implications of this for complexity theory are clear. There are now multiple complexity paradigms represented in the literature6, and most of these focus on just one of the “worlds” identified in my 1992 paper1. Indeed, it is arguably still the case that the majority concentrate on describing the generic characteristics of complex adaptive systems, network interactions, etc., and then either apply their theories to interpret empirical findings, or refine their theories through the analysis of findings. Essentially, they produce theories of “natural world” complexity, guided by the ideal of truth (acknowledging, of course, that their truth judgements are not absolute, as any reasonable scientist would). However, for the most part, they do not inquire into the normative social world of moral judgements concerning actions that people might want to take. The kind of research needed for this would be substantially different: in a particular context, it would involve exploring the complexities and uncertainties around possible human actions that people can envisage, and the perceived moral implications of these35,69 or the focus might be on how people's values contribute to the setting of purposes that then limit their framing of issues, and both values and framings can be shifted through action research70,59. Because the social world is about what “ought to be” in the context of action, exploring these things in action research mode makes sense. It is also worth asking, how much complexity research has a focus on the purely subjective perspectives of individuals? As far as I am aware, there is very little, although Snowden71 is a notable exception, advocating the collection of multiple individuals’ stories and then looking for patterns across these. There are major opportunities for developing new theories relating to the social and subjective complexity domains, and the biggest challenge of all is arguably to produce theories that explain repeating patterns in the interactions between the three types of complexity. We might thereby be able to offer generic insights into the fourth (meta) domain of complexity, as well as enable bespoke analyses of the interactions that are relevant to particular local and temporary contexts of practice. For complexity practice, the distinction between the four domains could also be valuable. Remember that, in 1992, I advocated the theory of the four domains of complexity to support methodological pluralism: drawing upon and mixing methods from across a range of systems (and other) approaches. It would be possible to reinterpret and harmonize the existing complexity paradigms in terms of the first three domains of complexity (concerning the natural, social and subjective worlds), thereby making them complementary. It is then reflecting on the interactions between the three types of complexity (i.e., beginning to get to grips with the fourth domain of complexity) in 142 | Midgley


any action research project that can guide the mixing of methods to support inquiry and change in practice. While the systems research community has placed considerable emphasis on the development of methodologies and methods to support systemic inquiry over the past 60 years24,52,59,72,73, I suggest that less emphasis has been placed on this by complexity scientists. Methodology and methods provide a bridge from theory to practice, and more concentration on this by complexity scientists would be useful—not just to inform practice with complexity theories of the natural world, but to support the exploration of normative and subjective complexities too.

Reflections on theoretical assumptions and terminology with the benefit of hindsight Having said earlier that I still believe that much of this theory of the four domains of complexity stands up to scrutiny today, it is perhaps unsurprising that I would now, with 25 years of hindsight, choose to change some assumptions and terminology. Over the years, I have developed an increasing skepticism of “grand theories”: sweeping theories of universals in human nature and society (see 74, for an examination of grand theories in the social sciences). For a start, many theories of supposedly “universal” characteristics of human beings or societies have been found to be culture dependent in light of evidence from comparative anthropology. Perhaps the most famous recent example concerns color perception, where it now appears that discriminating between colors has a lot to do with expectations of colour distinctions established in linguistic categories that have evolved in particular ecological and social contexts75. One of the problems with grand theories is that a lot of the particular, unique complexity in social situations is passed over in favor of relatively simple generic observations: almost the reverse of the old adage that “we cannot see the wood for the trees”. With grand theories, it's mostly wood and the trees become blurred. Even when I wrote my 1992 paper1 (and indeed my 1992c PhD thesis, which I was writing at the same time5), I struggled with a dilemma. I was unsure whether or not to accept Habermas's66,67,68 ontology, which (in the typical manner of a grand theory) roots categories of inquiry in the universal properties of language. I was advocating methodological pluralism, and of course different methodologies draw upon different (sometimes incompatible) theories, so would it then be contradictory to say that there is one theory of language that can organize all the methodological diversity?

E:CO 2016 18(2): 137-176 | 143

In the end I found a way through this. As I saw it, simply accepting a diversity of methodological paradigms and saying we can draw upon them pragmatically as we see fit meant also accepting all the incommensurable philosophical assumptions that come with those paradigms. Can we credibly say that our knowledge reflects a real world (the epistemological assumption of much “hard” systems science) and also, at the same time, believe that we only have access to our subjective and intersubjective understandings, which cannot necessarily be assumed to reflect anything real at all (the epistemology of much “soft” systems thinking)? Surely this leaves us in a philosophical muddle. This is plurality without any theory that explains how and why the various aspects of the plurality are valid or legitimate. I therefore decided, after much reflection, to go for a unifying theory through which the plurality of methods (not methodologies, with all their accompanying, potentially incommensurate theories) could be explained. I argued that any unifying ontology had to be ‘multi-faceted’ in order to have the requisite variety to contextualize methods drawn from different paradigms1. I therefore moved beyond Habermas's purely linguistic theory, arguing that the external natural world, the normative social world and our internal subjective worlds all exist, and indeed it is possible to show that the existence of each of them is dependent on the existence of the other two1. The other issue I struggled with at the time of writing my 1992 paper1, but I ended up ignoring my first instincts on this, was that judgements concerning beauty cannot be reduced to one of the three ideals of inquiry: truth, rightness and understanding subjectivity. Where did aesthetic judgement fit in Habermas's analysis of the inherent validity claims in any sentence intended for communication? I was already aware that Habermas's ontology could be viewed as a “grand theory”, which might not be such a good thing, and the fact that beauty wasn't recognized as an ideal of inquiry suggested that his theory could be overly reductive. Although I set aside these concerns about aesthetics in 1992, it finally dawned on me in the late 1990s that I had been right to be concerned. I set out to write a history of systems thinking in a chapter of a book59, and I was discussing how three longstanding traditions had informed the various systems paradigms: pure science (which tried to establish truth claims), applied science (which was also concerned with truth, but with a view to informing right action) and psychoanalysis (which is much more focused on understanding the subjective perspectives of individuals). It suddenly occurred to me that what Habermas might have done when he produced his ontology was to observe the major analytical traditions that mattered to him in society and then reflected the validity claims associated with those traditions in his theory of the universals of language! Although I cannot prove that his logic went in this direction 144 | Midgley


(from the analytical traditions to the ontology, rather than the other way around), it makes some sense of the way in which Habermas66,67,68 talks about those traditions; and the absence of beauty as an ideal of inquiry is arguably because Habermas does not view art and aesthetics as a major analytical tradition in the same sense as the sciences and psychoanalysis. So today I am left feeling somewhat equivocal about my 1992 argument¹. On the one hand, I think that the idea of four domains of complexity stands up to scrutiny and could be useful for understanding how complexity science could develop into the future. On the other hand, I am much more critical than I once was of Habermas's66,67,68 linguistic ontology, although I was already looking for ways to go beyond this in 1992 (such as viewing the natural, social and subjective worlds as real rather than as mere reflections of the universal capacities of language). It might be more appropriate to simply think about historical traditions, such as science (oriented to the ideal of truth), politics (oriented to the ideal of rightness) and therapy (which, when undertaken with individuals, is oriented to the ideal of understanding subjectivity), and we can then argue that these are embodied in the first three complexity domains, with the fourth domain being concerned with their interaction. With this introductory paper, I am putting both interpretations into the literature, and I leave it to the reader to judge which is the most useful and appropriate. Incidentally, one other thing that I would change about my 1992 paper is discussion of ‘ecological harmony'1. Gunderson and Holling76 persuasively argue that ecological systems cycle between periods of conservation (when the complexity of interrelationships grows steadily over time), release (when there is an external perturbation and the complexity of interrelationships makes the ecosystem “brittle” and vulnerable, causing some of these interrelationships to break), reorganization (when fresh interrelationships are formed), exploitation (when new complexity starts to burgeon quickly), and back to conservation again. In the context of this dynamic pattern, Gunderson and Holling argue that the word “harmony” connotes the maintenance of an equilibrium that does not actually exist. In retrospect, I would perhaps have talked about living within sustainable limits, which does not preclude a cycle that, in all its phases, remains within those limits.

E:CO 2016 18(2): 137-176 | 145

Conclusion

I

n conclusion, I recommend my 19921 paper to you, with the proviso that you read it critically, understanding that it is a product of its time. My hope is that the basic argument for four domains of complexity is still relevant now for researchers asking where complexity theory remains under-developed and could go in the future. I also hope that the argument for methodological pluralism is just as relevant for complexity practitioners as practitioners in the systems community. Certainly this paper, and many other books and papers on critical systems thinking45,24,52,46,63,59, helped us win the argument for methodological pluralism in the systems community. While there are still live debates about the theory underpinning the practice of methodological pluralism77, it is undoubtedly the case that most people now accept it as preferable to both a paradigm war and the limitations on practice that come with believing that only a narrow range of methods has validity. If complexity scientists need to transcend a paradigm war themselves in coming years, they do not need to start with a clean slate: there is a lot of prior work in the systems literature, including my own 1992 paper1, reprinted next.

References 1. Midgley, G. (1992). “Pluralism and the legitimation of systems science,” Systems Practice, ISSN 0894-9859, 5(2): 147-172. 2. Midgley, G. (1992). “The sacred and profane in critical systems thinking,” Systems Practice, ISSN 0894-9859, 5(1): 5-16. 3. Midgley, G. (1996). “The ideal of unity and the practice of pluralism in systems science,” in R.L. Flood and N.R.A. Romm (eds.), Critical Systems Thinking: Current Research and Practice, ISBN 9783540615880, pp. 25-36. 4. Midgley, G. (2001). “Rethinking the unity of science,” International Journal of General Systems, ISSN 0308-1079, 30: 379-409. 5. Midgley, G. (1992). Unity and Pluralism, Ph.D. thesis, City University, London. 6. Midgley, G. and Richardson, K.A. (2007). “Systems thinking for community involvement in policy analysis,” Emergence: Complexity & Organization, ISSN 1521-3250, 9(1-2): 167-183. 7. Angyal, A. (1941). Foundations for a Science of Personality, ISBN 9780670003556. 8. Bertalanffy, L. von (1956). “General systems theory,” General Systems, ISSN 0072-0798, 1: 1-10. 9. Boulding, K.E. (1956). “General systems theory: The skeleton of science,” Management Science, ISSN 0025-1909, 2: 197-208.

146 | Midgley


10. Hall, A.D. and Fagan, R.E. (1956). “Definition of system,” General Systems, ISSN 0072-0798, 1: 18-28. 11. Wiener, N. (1948). Cybernetics, or Control and Communication in the Animal and the Machine, ISBN 9780262730099. 12. Ashby, W.R. (1956). Introduction to Cybernetics, ISBN 9781614277651. 13. Bateson, G. (1967). “Cybernetic explanation,” American Behavioral Scientist, ISSN 00027642, 0(8): 29-32. 14. Weaver, W. (1948). “Science and complexity,” American Scientist, ISSN 0003-0996, 36: 536-544. 15. Simon, H.A. (1962). “The architecture of complexity,” Proceedings of the American Philosophical Society, ISSN 0003-049X, 106(6): 467-482. 16. Quade, E.S. and Boucher, W.I. (1968). Systems Analysis and Policy Planning: Applications in Defense, ISBN 9780444000330. 17. Optner, S.L. (ed.) (1973). Systems Analysis, ISBN 9780140809213. 18. Hall, A.D. (1962). A Methodology for Systems Engineering, ISBN 9780442030469. 19. Jenkins, G. (1969). “The systems approach,” Journal of Systems Engineering, ISSN 00224820, 1: 3-49. 20. Forrester, J.W. (1961). Industrial Dynamics, ISBN 9781614275336. 21. Forrester, J.W. (1969). Principles of Systems, ISBN 9781883823412. 22. Beer, S. (1959). Cybernetics and Management, English Universities Press, Oxford. 23. Beer, S. (1966). Decision and Control, ISBN 9780471948384. 24. Jackson, M.C. (1991). Systems Methodology for the Management Sciences, ISBN 9783540713593. 25. Hoos, I. (1972). Systems Analysis in Public Policy: A Critique, ISBN 9780520026094. 26. Lee, D.B. (1973). “Requiem for large-scale models,” AIP Journal, May: 163-178. 27. Lilienfeld, R. (1978). The Rise of Systems Theory: An Ideological Analysis, ISBN 9780471535331. 28. Ackoff, R.L. (1979). “The future of operational research is past,” Journal of the Operational Research Society, ISSN 0160-5682, 30: 93-104. 29. Ackoff, R.L. (1981). Creating the Corporate Future, ISBN 9780471090090. 30. Checkland, P. (1981). Systems Thinking, Systems Practice, ISBN 9780471986065. 31. Mason, R.O. and Mitroff, I.I. (1981). Challenging Strategic Planning Assumptions, ISBN 9780471082194. 32. Rosenhead, J. (ed.) (1989). Rational Analysis for a Problematic World, ISBN 9780471922865. 33. Christakis, A.N. and Bausch, K.C. (2006). How People Harness Their Collective Wisdom and Power to Construct the Future in Co-Laboratories of Democracy, ISBN 9781593114817.

E:CO 2016 18(2): 137-176 | 147

34. Churchman, C.W. (1970). “Operations research as a profession,” Management Science, ISSN 0025-1909, 17: B37-53. 35. Ulrich, W. (1983). Critical Heuristics of Social Planning: A New Approach to Practical Philosophy, ISBN 9780471953456. 36. Ackoff, R.L. (1979). “Resurrecting the future of operational research,” Journal of the Operational Research Society, ISSN 0160-5682, 30: 189-199. 37. Checkland, P. (1985). “From optimizing to learning: a development of systems thinking for the 1990s,” Journal of the Operational Research Society, ISSN 0160-5682, 36: 757-767. 38. Geus, A.P. de (1994). “Modeling to predict or to learn?” in J.D.W. Morecroft and J.D. Sternman (eds.), Modeling for Learning Organizations, ISBN 9781563270604. 39. Sterman, J.D. (1994). “Learning in and about complex systems,” System Dynamics Review, ISSN 0883-7066, 10(2-3): 291-330. 40. Rosenhead, J. and Mingers, J. (eds.) (2001). Rational Analysis for a Problematic World Revisited, ISBN 9780471495239. 41. Midgley, G. (1989). “Critical systems: The theory and practice of partitioning methodologies,” Proceedings of the 33rd Annual Meeting of the International Society for General Systems Research (Volume II), held in Edinburgh, Scotland, on 2-7 July 1989. 42. Midgley, G. (1989). “Critical systems and the problem of pluralism,” Cybernetics and Systems, ISSN XXXX-XXXX, 20: 219-231. 43. Midgley, G. (1990). “Creative methodology design,” Systemist, ISSN 0961-8309, 12: 108113. 44. Midgley, G. (1990). “Critical systems and methodological pluralism,” in B.H. Banathy and B.A. Banathy (eds.), Toward a Just Society for Future Generations. Volume I: Systems Design, Proceedings of the 34th Annual Meeting of the International Society for the Systems Sciences (ISSS) in Portland, Oregon, 8-13 July 45. Flood, R.L. and Jackson, M.C. (eds.) (1991). Critical Systems Thinking: Directed Readings, ISBN 9780471930983. 46. Flood, R.L. and Romm, N.R.A. (eds.) (1996). Critical Systems Thinking: Current Research and Practice, ISBN 9783540615880. 47. Flood, R.L. (1990). Liberating Systems Theory, ISBN 9780306435928. 48. Oliga, J. (1996). Power, Ideology, and Control, ISBN 9780306451607. 49. Jackson, M.C. and Keys, P. (1984). “Towards a system of systems methodologies,” Journal of the Operational Research Society, ISSN 0160-5682, 35: 473-486. 50. Jackson, M.C. (1987). “Present positions and future prospects in management science,” Omega, ISSN 0305-0483, 15: 455-466. 51. Jackson, M.C. (1987). New Directions in Management Science, ISBN 9780566050947. 52. Jackson, M.C. (2000). Systems Approaches to Management, ISBN 9780387240626. 53. Oliga, J.C. (1988). “Methodological foundations of systems methodologies,” Systems Practice, ISSN 0894-9859, 1: 87-112.

148 | Midgley


54. Flood, R.L. (1995). Solving Problem Solving, ISBN 9780471955900. 55. Gregory, W.J. (1992). Critical Systems Thinking and Pluralism: A New Constellation, Ph.D. thesis, City University, London. 56. Gregory, W.J. (1996). “Discordant pluralism: A new strategy for critical systems thinking,” Systems Practice, ISSN 0894-9859, 9: 605-625. 57. Gregory, W.J. (1996). “Dealing with diversity,” in R.L. Flood and N.R.A Romm (eds.) (1996). Critical Systems Thinking: Current Research and Practice, ISBN 9783540615880. 58. Midgley, G. (1997). “Mixing methods: Developing systemic intervention,” in J. Mingers and A. Gill (eds.), Multimethodology: The Theory and Practice of Combining Management Science Methodologies, ISBN 9780471974901. 59. Midgley, G. (2000). Systemic Intervention: Philosophy, Methodology, and Practice, ISBN 9781461368854. 60. Brocklesby, J. (1994). “Let the jury decide: Assessing the cultural feasibility of total systems intervention,” Systems Practice, ISSN 0894-9859, 7: 75-86. 61. Brocklesby, J. (1997). “Becoming multimethodology literate: An assessment of the cognitive difficulties of working across paradigms,” in J. Mingers and A. Gill (eds.), Multimethodology: The Theory and Practice of Combining Management Science Methodologies, ISBN 9780471974901. 62. Mingers, J. and Brocklesby, J. (1996). “Multimethodology: Towards a framework for critical pluralism,” Systemist, ISSN 0961-8309, 18: 101-131. 63. Mingers, J. and Gill, A. (eds.) (1997). Multimethodology: The Theory and Practice of Combining Management Science Methodologies, ISBN 9780471974901. 64. Popper, K.R. (1959). The Logic of Scientific Discovery, ISBN 9780415278447. 65. Popper, K.R. (1972). Objective Knowledge, ISBN 9780198750246. 66. Habermas, J. (1976). Communication and the Evolution of Society, ISBN 9780807015131. 67. Habermas, J. (1984). The Theory of Communicative Action, Volume One: Reason and the Rationalization of Society, ISBN 9780807015070. 68. Habermas, J. (1984). The Theory of Communicative Action, Volume Two: The Critique of Functionalist Reason, ISBN 9780807014011. 69. Friend, J. and Hickling, A. (2004). Planning Under Pressure: The Strategic Choice Approach, ISBN 9780750663731. 70. Cilliers, P. (1998). Complexity and Postmodernism, ISBN 9780415152877. 71. Snowden, D. (2010). “Naturalizing sensemaking,” in K.L. Mosier and U.M Fischer (eds.), Informed by Knowledge: Expert Performance in Complex Situations, ISBN 9781848729117. 72. Midgley, G. (ed.) (2003). Systems Thinking, ISBN 9780761949596. 73. Reynolds, M. and Holwell, S. (2010). Systems Approaches to Managing Change: A Practical Guide, ISBN 9781848828087. 74. Skinner, Q. (1985). The Return of Grand Theory in the Human Sciences, ISBN 9780521398336. E:CO 2016 18(2): 137-176 | 149

75. Roberson, D., Davies, I. and Davidoff, J.B. (2002). “Color categories are not universal: replications and new evidence,” in B. Saunders and J. van Brakel (eds.), Theories, Technologies, Instrumentalities of Color: Anthropological and Historiographic Perspectives, ISBN 9780761822653. 76. Gunderson, L.H. and Holling, C.S. (2002). Panarchy: Understanding Transformations in Human and Natural Systems, ISBN 9781559638579. 77. Zhu, Z. (2011). “After paradigm: Why mixing methodology theorizing fails and how to make it work again,” Journal of the Operational Research Society, ISSN 0160-5682, 62(4): 784-798.

150 | Midgley


E:CO 2016 18(2): 137-176 | 151

152 | Midgley


E:CO 2016 18(2): 137-176 | 153

154 | Midgley


E:CO 2016 18(2): 137-176 | 155

156 | Midgley


E:CO 2016 18(2): 137-176 | 157

158 | Midgley


E:CO 2016 18(2): 137-176 | 159

160 | Midgley


E:CO 2016 18(2): 137-176 | 161

162 | Midgley


E:CO 2016 18(2): 137-176 | 163

164 | Midgley


E:CO 2016 18(2): 137-176 | 165

166 | Midgley


E:CO 2016 18(2): 137-176 | 167

168 | Midgley


E:CO 2016 18(2): 137-176 | 169

170 | Midgley


E:CO 2016 18(2): 137-176 | 171

172 | Midgley


E:CO 2016 18(2): 137-176 | 173

174 | Midgley


E:CO 2016 18(2): 137-176 | 175

176 | Midgley

Adjacent opportunities: Enlightened economics


Forum

Ron Schultz Waterman Aylsworth, USA

E

nlightened Economics sounds a bit oxymoronic. Enlightened is something we envision as a state that is transcendentally non-attached to worldly concerns. And Economics is obviously very earth-bound, tied to every material attachment we can possibly desire and some that extend beyond the imagination. On the surface, not quite the chummiest of relationships.

But what if Enlightened meant recognizing the inseparability and interdependence of all things and non-things and not having to struggle with that inseparable interdependence? Enlightened would be a recognition of our inexplicable linkage to the world as it shows-up, our relationship to, in the vernacular, the whole mishpucka – which is Yiddish for “the whole crazy family.” And within the context of that relationship we are free from the struggle often associated with our hierarchical jockeying within that construct. If we were to translate this idea of enlightened to our relationship to the economy that arises out of those related interactions, perhaps those things by which we measure what is economic might be a bit different. Economics would move beyond the simple gauge of money and such things as Gross National Product and instead include in the calculation: our relationship to money, our relationship to value, our understanding of development versus growth, our relationship to our communities, each other, our concept of ecology, well-being and health. An enlightened economy would therefore also be a healthy economy, not merely in its fiscal robustness, but in its underlying interconnection to the aforementioned mishpucka. Can you imagine the economic indicators of an enlightened economy? The diagnostic for Gross National Happiness could be one such index. But even that fails, for reasons of sheer complexity, to accommodate relationships. Our relationship to money, alone, even on a surface level, is complex. On a physical level, our ability to generate income without having to sell our soul engenders all kinds of gyrations, prostrations, machinations, complexifications, and untold genu-

E:CO 2016 18(2): 177-179 | 177

flextions just to pay the bills. Mentally, the worry, fear and trepidation alone would seem to exceed the earned income return for all the tsuris (indigestion) generated. Then there’s our relationship to value. From a fiscal standpoint, this is often an elusive and intuitive reaction to things and how significant they are to us. Cost is one measure of value, but so is worth, desirability, usefulness, benefit, and profit. But our relationship to value also exists on a more personal level that might make us choose what we value based on things like importance, consequence, meaning and merit. Perhaps of equal importance is how we value our own sense of worthiness. When we move this thinking from the range of personal experience to society and beyond, value, buying power, safety, security, comfort, and growth invariably gravitate toward accumulation, hording, greed and a whole host of nasty human traits. So how could we possibly imagine that we could reconstruct our beliefs and values toward a more enlightened perspective amid the overpowering seduction of these enduring forces? Less we remain in this seemingly inescapable trap brought on by this failure of imagination, let us explore how a more enlightened economic worldview might showup. How we might form a basis for personal worth that is not just based on accumulated currency but on self-worthiness, on purpose and meaning, and on being of benefit to others. Before you ask, how does that pay the rent or put food on the table? Perhaps we better figure out how we reconnect this notion of economy with the people who produce it. As long as economy is viewed from the perspective of measurement, there is no possibility for emergent phenomena to arise. There is no room in that economic description for interaction and something new to emerge. An enlightened economics never strays from the interdependence of the life that generates it. What emerges are sharing economies, collaborative economies, interactive systems based on social solidarity. What in turn emerges from this approach is genuine economic support, interconnected security, inclusion, worthiness, greater purpose and meaning, and the willingness to be of benefit to others. In the economic models of measurement, and more is better, we deliberately create exclusion, addiction, intolerance, distrust, unworthiness, degradation, poverty and its direct relative, poverty mentality, that feeling of never having enough; an addictive perception that having more is somehow a salve to ease pain and suffering. In solidifying these elements, we lock our economic development into increasing degen-

178 | Schultz


eration that perpetuates and accentuates the inequities and failings of our economic structures. In contrast, building an enlightened economics is a dedication to our health and well-being. But talking about its benefits and actually building it are two very distinct activities. First, we have to be willing to let go of the stories we tell ourselves about how the world shows-up. Not an easy task when you consider some of our current political discourse which seems dedicated to creating solid waste-dumps of fantasies about who and how we are in the world. As long as we solidify our separateness out of fear of the other, our economic development will always be arrested and our inability to build something together greatly limited. Enlightened Economics is about building relationships, not infrastructure. Relationships with money, value, worth and each other. These interactive relationships are a continual process of giving back to the economic system. They are generative and truly developmental. Extractive activities, which are designed to pull as much from the system as possible, by their very nature deplete the health of the system. But breaking through the over-riding addiction and greed of these extractive practitioners is not an easy task. What we are seeing emerge out of many of the Millennial communities is a willingness to free themselves from that degenerate portion of the economy, and simply start a parallel path. It is an interesting approach to building an economy based on the health and interdependence of its agents. Let the aging and traditional economy simply die out. Don’t try to fix the old system, but like a revolutionary operating system, pay no further attention to what isn’t working and simply move the system forward with something better. In Buddhism, compassion is, of course, always at the forefront. But there is another aspect to compassion which must also be recognized, idiot compassion. When something is doing everything it can to destroy the system, we can have compassion for the suffering that fuels that destruction, but not an idiot compassion that is willing to take down the whole system based on the suffering of the few. Perhaps the most compassionate action we can take in bringing Enlightened Economics to the fore is to join the youth who have already had enough and move forward unfettered by the old economic values and measurement systems. Perhaps it’s finally time to build and unveil this new interactive structure based on relationship, emergence and interdependence, before the extraction leads to extinction. E:CO 2016 18(2): 177-179 | 179

Calling Notices and Announcements E:CO Issue Vol. 18 No. 2 2016 pp. 181-182

Calling notices

Calling Notices and Announcements

The 35th International Conference of the System Dynamics Society 60th Anniversary Celebration

Cambridge, MassachuseƩs, USA July 16-20, 2017

CLL FOR PER 2017 marks the 60th anniversary of the founding of the eld of System Dynamics. It is thus tting that we hold this milestone conference in Cambridge, Massachusetts, next to the MIT campus where Jay Forrester developed the eld. Today, System Dynamics is used around the world, from K-12 classrooms through doctoral programs, in scholarly research across many disciplines, and in applications from organizational change to climate change, from medicine to management. We will celebrate the accomplishments of the last six decades and explore future directions by showcasing the best work in dynamic modeling being done today. Papers may be submitted from February 2, 2017 to March 22, 2017. Program Chairs John D. Sterman Massachusetts Institute of Technology Nelson Repenning Massachusetts Institute of Technology Workshop Chairs Jack B. Homer Homer Consulting Hazhir Rahmandad Massachusetts Institute of Technology Conference Manager Roberta L. Spencer, Executive Director System Dynamics Society Venue Hyatt Regency Cambridge E-mail: [email protected]

Calling Notices

Tentative threads:

• • • • • • • • • • • • • •

Business conomics nvironment Health Human Behavior Information and Knowledge earning and Teaching Methodology perations Public Policy esources Security Stakeholder ngagement Strategy

Call for Papers, Workshops & Sessions Opening date ebruary 2, 2017 Submission deadline March 22, 2017 Hosted by System Dynamics Group Massachusetts Institute of Technology, Sloan School of Management conference.systemdynamics.org

181

About The Society

SCTP

LS A n

for Cha

s

Publishing Vol. 18 in 2014

Membe rs quarterl receive a sub s y abstrac refereed scho cription to our te la Econlit, d in APA Psyc rly journal, w h h M Index, athSciNet, So Info, Medline, ich is W JE c index s eb of Science ial Sciences C L/ ervices. , and o ther im itation Recent p § Non o specia rtant § Develinear Organiz l issues include: § Crea lopmental Psational Dyna § Medtive Behaviorychopathologmics y § Psycical Practice hom Contro otor Coord ination l and t en pm ide o l w ve de world he o t ience t d c ate r s dic ea De onlin 1 9 n of e 19 c n i s

S!

CTPL Join S

Society

forch y t e i .soc www

os Theo

ry in Ps

nual The S Conf C eren a uniq TPLS Ann ual In ue in ce terna tellec stimu tiona tual a lates l n Confe d single d soc ialogu re ia p e and p apers, wo with sym l atmosph nce offers e ro p r it is th minent g kshops, th osia, roun re that uests e long . Op e business dtables, Prese e s te ru ra m n or me tations ma nning con ting since eeting, fe 1 y th includ odologica be theore rence of it 991, s type tical, l in co e: Bio applie and E . medic ntent. d rg a Ecolo onomics, l sciences Program tr , empirica l, , Cog gy, So Clinic a c k s nitive al Psy ciolog Dyna Ps m ch y, of Sc ics and Org Economic ology, Ma ychology ience th s aniza , Com emati . SCT Nonli tiona mun cs, PL near Scien S also sp l Behavior, ication, G ro o ce Co nfere nsors the and Philos up nces. Intern o ation phy al

ycholog

y & Life

Our members hail from numerous specialties within psychology, other social sciences, and biology, physiology, neuroscience, mathematics, philosophy, physics, computer science, economics, education, management, political science, engineering, and the world of art. Our membership spans more than 30 countries.

y.org r o e h aost

Nonlinear Dynamics, Psychology, and Life Sciences (ISSN 1090-0578)

Science

The Society is an international forum that brings together researchers, theoreticians, and practitioners interested in applying dynamical systems theory, selforganization, neural nets, fractals, cellular automata, agent-based modeling, and related forms of chaos, catastrophes, bifurcations, nonlinear dynamics, and complexity theories to psychology and the life sciences.

forum ided a as prov are insights TPLS h sh C S to , n tio ciplinary ners ss dis s incep ractitio Since it ntists and p nomena acro brings you: e S ie h fic for sc onlinear p hip in SCTPL scienti n about ries. Members n to the top a o d ti n p u ri o , c b ubs nd area • a s rnal in this nferences a co jou ts on n u o c and dis • fields ntists hops, works tions to scie vancing the c d e a e, id nn rs o w e c d d a rl • wo y le ls on industr near theory and tutoria li of non to software affect ce, cess • ac linear scien to positively y it non n . u ld rt oppo the fie . • an growth of web site the secure on our rm fo bership e mem Use th

www.societyforchaostheory.org

182

E:CO Vol. 18 No. 2 2016 pp. 181-182

ABOUT E:CO

Emergence: Complexity & Organization (E:CO) is an international and interdisciplinary conversation about human organizations as complex systems and the implications of complexity science for those organizations. With a unique format blending the integrity of academic inquiry and the impact of business practice, E:CO integrates multiple perspectives in management theory, research, practice and education. E:CO is a quarterly journal published in print and online by The Complexity Society, the Institute for the Study of Coherence and Emergence, Cognitive Edge, and Emergent Publications (formerly ISCE Publishing) in accordance with academic publishing standards and processes.

INTELLECTUAL ECOLOGY

E:CO’s niche is the opportunity to bridge three gaps:

• The distance between academic theory and professional practice; • The space between the mathematics and the metaphors of complexity thinking; and, • The disparity between formal idealizations and actual human organizations. Organizations of all kinds struggle to understand, adapt, respond and manipulate changing conditions in their internal and external environments. Approaches based on the causal, linear logic of mechanistic sciences and engineering continue to play an important role, given people’s ability to create order. But such approaches are valid only within carefully circumscribed boundaries. They become counterproductive when the same organizations display the highly reflexive, context-dependent, dynamic nature of systems in which agents learn and adapt and new patterns emerge. The rapidly expanding discussion about complex systems offers important contributions to the integration of diverse perspectives and ultimately new insights into organizational effectiveness. There is increasing interest in complexity in mainstream business education, as well as in specialist business disciplines such as knowledge management. Real world systems can’t be completely designed, controlled, understood or predicted, even by the so-called sciences of complexity, but they can be more effective when understood as complex systems. While many scientific disciplines explore complexity through mathematical models and simulations, E:CO explores the emerging understanding of human systems that is informed by this research. Engineered and emergent views of human systems can coexist, creating a useful tension that drives organizational evolution. However, neither academics nor practitioners can leverage complexity alone. Academic discussions about complexity are often biased towards quantitative research and mathematical models that are inappropriately prescriptive for systems comprised of actors endowed with free will, who are simultaneously part of and aware of the

system. The metaphors of complexity have a usefulness of their own as well, but too often they are applied without adequate reference to the mechanisms, models and mathematics behind them.

CONTENT IN CONTEXT

Readers of E:CO are managers, academics, consultants and others interested in developing and applying the insights of complex systems theories and models to analysis and management of private-, public- and social-sector organizations and applying insights derived from organizational experience to understanding complex systems theories. E:CO encourages multidisciplinary contributions from all sectors of social and natural sciences and all sectors of organizational practice. The journal’s unique format presents both reviewed and non-reviewed content from three overlapping sources. Peer-reviewed articles are at the heart of our content, but with an emphasis on communicating across boundaries. Academic articles pass double-blind reviews by two academics and one practitioner. When subject matter is theoretical or reporting research findings, authors will be encouraged to discuss practical implications of the ideas. Similarly, practitioner articles also will be double-blind reviewed by two practitioners and one academic. When appropriate, authors will be encouraged to connect to theory or research that has either already been done or needs to be done. Additional non-reviewed content includes feature articles, essays, profiles, conversations and conference summaries, as well as news, commentary, book reviews, etc. Each article will be clearly marked according to which path it took to publication. E:CO incorporates Emergence, originally published by the Institute for the Study of Coherence and Emergence.

SUBMITTING MATERIAL TO E:CO

E:CO is interested in receiving work from a wide range of perspectives:

• Theoretical and practitioner based; • Both conventional and unconventional methodologies; • Case study work; • Approaches to teaching management or leadership; • Work covering a variety of organizational types, size and ownership; • Cross cultural studies and work from Australasia, Africa, Central and South America and the Far East as well as the USA and Europe. We ask that authors set their paper clearly within the context of the notion of complexity and complex systems, however they chose to define such, and that

the practical implications and transferable lessons from their work be clearly described. Note that quantitative studies (including those which focus on survey results and related statistics) are not suitable for E:CO. Authors are limited to one mathematical formula per paper (additional formulae may appear in the technical appendix). If you wish to submit work of a quantitative nature, please represent it qualitatively. Figures and tables should be illustrative. Quantitative and statistically based submissions will be returned without review. Each article in E:CO will be accompanied by space on the E:CO web site for additional materials and discussion forums.

FORMAT

All submissions are electronic and must be made via: https://journal.emergentpublications.com. Suggested length is 4000 to 5000 words. Review pieces and essays should be 2000 to 3000 words. Note: additional material considered relevant and/or related by the author(s) can be posted on the web site, which will be associated with each accepted article. The author(s) will be responsible for securing all necessary permissions for material to be posted on the web site. From August 1st, 2014, all submissions are to be made via the online https:// journal.emergentpublications.com, which is based on the new Anntoum 2.0 (http://annotum.org/) WordPress (https://wordpress.org/) theme. The move to our new workflow environment is to better support the review and revision process, and also provide the foundation for a more diverse distribution system coming early in 2015. This will allow easier distribution of E:CO content across the many mobile platforms. Print will also continue to be available. Early preprints will also be made available before the scheduled issue publication dates. If you have any questions about the new submission please contact Kurt Richardson (Managing Editor) at [email protected].

Notes on Content Figures Figures must be provided in .PNG format, and render well in both color and greyscale.

References From August 1st, 2014 all references will be included as footnotes (as supported by the Annotum 2.0 workflow system). Support for CrossRef (http:// www.crossref.org/) is coming soon, but in the meantime references need to

be entered following a standard format. The main difference between E:CO’s requirements and those for other journals is that for E:CO the ISSN and ISBN numbers for periodicals/journals and books are required. Some examples are provided below: Crissy, W.J.E. and Kaplan, R.M. (1969). Salesmanship: The Personal Force in Marketing, ISBN 0471187550. Richardson, K.A., Tait, A., Roos, J. and Lissack, M.R. (2005). “The coherent management of complex projects and the potential role of group decision support systems,” in K.A. Richardson (ed.), Managing Organizational Complexity: Philosophy, Theory, and Application, ISBN 1593113188, pp. 433-458. Ingram, T.N. and Bellenger, D.N. (1983). “Personal and organizational variables: Their relative effect on reward valences of industrial salespeople,” Journal of Marketing Research, ISSN 0022-2437, 20(May): 198-205. Note that all books and journals must have their ISBN and ISSN included, respectively, where known. From issue 9.1 (2007) the town, state and publisher are no longer needed for books for which there is a current ISBN. Only older books (pre-ISBN) require town, state, and publisher. If you have trouble finding journal ISSNs then try entering the journal’s name within inverted commas and “ISSN” into Google. For example: “Journal of Management” ISSN Failure to format references correctly may create delays in the publication process.

ACCEPTANCE PROCEDURE

The Editors and Managing Editor will review all submissions for suitability. Manuscripts deemed suitable are reviewed independently by members of the editorial review board, and their recommendations guide the Editors in their acceptance decision. The reviews are double blind—neither authors nor reviewers know the identity of each other. However, we encourage a close working relationship between reviewers and authors and the workflow system that is in place can support such collaboration if both parties agree. All reviewing for E:CO is done electronically via our online Annotum 2.0-based workflow system. Authors will be updated via email.

Control Principles of Complex Systems Yang-Yu Liu and Albert-László Barabási

Abstract

A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system’s components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in dynamical systems and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these questions here recent advances on the controllability and the control of complex networks are reviewed, exploring the intricate interplay between the network topology and dynamical laws. The pertinent mathematical results are matched with empirical findings and applications. Uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior. For more information refer to: https://doi.org/10.1103/RevModPhys.88.035006

If you have an image—which may be a photograph, complex computer generated image, or some interesting data—that you’d like to have published in full color on a future issue of E:CO then please send it along to featured_image@ emergentpublications.com along with a paragraph or two describing what the image depicts and how it was created.

Complexity & Organization

Complexity & Organization

Suggest Documents

Complexity and Self-organization

Life's Order, Complexity, Organization, and Its

Measuring Complexity by Measuring Structure and Organization

Self-Organization, Complexity Catastrophes, and ... - Bill McKelvey.org

Measuring the Complexity of Business Organization ...

Self-Organization, Complexity Catastrophes, and ... - Semantic Scholar

Complexity, Self- Organization, and Emergence - Peggy Holman

Life's Order, Complexity, Organization, and Its

Follow the energy Å Emergence: Complexity and Organization

Learned changes in the complexity of movement organization ... - Patton

complexity, self-organization, and emergence at the ...

Complexity of cis-regulatory organization of six3a during ... - Core

An Information-Theoretic Primer on Complexity, Self-Organization ...

Intervention complexity â a conceptual ... - World Health Organization

Applications and Limitations of Complexity Theory in Organization ...

complexity-based metrics for the evaluation of the program organization

Self-organization, Emergence and the Architecture of Complexity

Complexity and Self-Organization in Biological Development and

Self-Organization of the Neuron Collective of Optimal Complexity

Complexity

complexity

Robustness and complexity - CABDyN Complexity Centre

Complexity Anonymous recover from complexity addiction - GitHub

Coding Complexity: The Computational Complexity of Succinct ...

Complexity & Organization