A co-evolutionary complex systems perspective on ... - Springer Link

Journal of Information Technology (2006) 21, 229–238

& 2006 JIT Palgrave Macmillan Ltd. All rights reserved 0268-3962/06 $30.00 palgrave-journals.com/jit

Research article

A co-evolutionary complex systems perspective on information systems Peter M Allen, Liz Varga School of Management, Cranfield University, Bedford, UK Correspondence: Peter M Allen, School of Management, Cranfield University, Bedford MK43 0AL, UK. Tel: þ 44 1234 758080; Fax: þ 44 1234 758084; E-mail: [email protected]

Abstract The co-evolution of information systems (IS) and the processes that underpin the construction and development of IT systems are explained from a complex systems perspective. Evolution operates at the microscopic level; in organizations, this is the individual or agent. Each agent has an idiosyncratic view of the organization, using to some extent personal constructs in dealing with the reality of organizational life. These objects or constructs can be described and measured by most agents; they are well defined. Many of these objects are represented in electronic, IT systems. Each agent also has their own view as to how they know what they know, that is, their epistemology, which we argue is their IS, and is wider than the IT systems they use. The IS of each agent coevolves, by interaction with other agents, based on the agent’s view of reality. The interaction of all agents constitutes the organization. Even more importantly, different values and interests motivate each agent. This is their axiology and it is what motivates them to learn and to develop their IS. An agent-based axiological framework is essential to understanding the evolution of organizations. It is the interaction of agents that builds consensus as to the shared reality of the organization, and this affects each agent’s ability and motivation to evolve IS further. In addition, we propose that it is time that IT systems included modelling capabilities, based on multi-agent representations of the organization and its context, to explore and support strategic thinking and decision making. Journal of Information Technology (2006) 21, 229–238. doi:10.1057/palgrave.jit.2000075 Keywords: complexity science; information systems; ontology; epistemology; axiology; agent-based modelling

Introduction rganizations are dynamic, evolving entities; they are interdependent, complex systems (Thompson, 1967). The application of complexity science, and so the theories that underpin complex systems, to organizations is not a novel approach (Simon, 2001). We show in this paper that complexity science has continuing relevance to organization science and in particular to the transformation of organizations and the information systems (IS) that underlie such transformations. We distinguish between the formal IT system and the IS of individuals that include this, and also other informal, personal and idiosyncratic contacts and connections. By taking an agent-based view of organizations, we consider that evolution is driven most strongly by individuals in the firm, whose IS co-evolves through their interaction with other agents. And this coevolution is driven by each agent’s value system, their

O

axiology. We discuss managerial implications for understanding the co-evolution of IS by reference to an axiological framework. The next section provides an introduction to complexity science in relation to organizations and their evolution. Complexity science The aim of this section is to define some of the major concepts in complexity science in relation to organizational evolution, and their relevance to the multi-layered, relational nature of organizations. Complex systems are open systems that interact with the environment and with other complex systems. They are the result of evolutionary processes during which successive levels of structure emerged (cells, organs, organisms,

A co-evolutionary perspective on IS

PM Allen and L Varga

230

groups, societies, etc.). They are always far from thermodynamic equilibrium because the emergence and maintenance of structure always requires the flow of energy and matter to sustain it. Such systems have a potential for further evolution and change in the context of their environment and will generally involve structural, organizational change over time. Traditional organizational theories, such as transaction cost economics or resourcebased theory, take a static, micro, transactional perspective. They either explain observed behaviour and structure as an inevitable consequence of a supposed optimal functionality or as a necessary expression of inner capabilities, which may yield results in the short term and in an unchanging world, but they are inadequate to describe or predict changing organizational affairs. Complexity science is the science of evolution, of changing organizational forms and structures and of emergent capabilities and features. However, complexity science views such organizational evolution as resulting from the interaction of individuals. This is not simply a reductionist view because the individuals are changed internally by their interactions, and therefore give rise to genuine emergent, collective effects. It is also a view in contrast to that in which organizations are viewed as functional from a top-down perspective. It cannot be assumed that the interaction of the individuals will necessarily lead to the successful functioning of the organization, or that there is any scientific principle that ensures the functioning will be optimal, or indeed even maintained. Instead, complexity science sees an organization that is operating as being the result of a historical pathway of local experimentation that has led to a system that works well enough to have survived until now. Its operation affords no simple guarantee about future survival. Complexity science is cognizant of the whole system in qualitative, holistic ways (McKelvey, 1999), recognizing that interactions in real systems exhibit non-linearity, and that this is the mechanism by which small causes and fluctuations at a lower level can generate disproportionate, structural evolution at a higher level above. It is sometimes called the butterfly effect (Lorenz, 1963), wherein a tiny effect of a butterfly flapping its wings could potentially change the emergent pattern of the large-scale atmospheric dynamics. However, in this example, the variables and phenomena are not changed qualitatively, while in general, complexity recognizes that structural evolution, emergent properties and capabilities are driven by the dialogue between the microscopic and the macroscopic levels of description of a system. In this way, complexity science also recognizes that history – luck, contingency, particular circumstances – really matters, since this evolutionary dialogue is path-dependent and is not reversible. The co-evolution of the agents within complex systems means that together their learning and adaptation trace a particular pathway into the future rather than a convergent one to some generic equilibrium structure. The system is sensitive to initial conditions as well as to events and circumstances along the way with an irreversibility expressing the ‘arrow of time’. These characteristics of organizations, as complex systems, need to be recognized in order for managers to recognize the constraints and opportunities to influence the evolution of their organizations.

A complex system does not exist in isolation. It both competes and collaborates with other organizations for resources and customers, at many layers within the organization, for example, individuals, teams and departments. Co-evolution occurs when the direct or indirect interaction of two or more evolving units produces an evolutionary response in each other (Van Valen, 1983). An organization, for example, may co-evolve with one of its suppliers in the development of a particular technology. The relevance of co-evolution is that regardless of the dimensions that each agent perceives at a given time, these dimensions will change over time, sometimes incrementally and sometimes suddenly as the consequence of a phase transition. Because of this, the variables, parameters and factors that we perceive in reality (ontology) change over time, and force some evolutionary change in our understanding and knowledge (epistemology), as well as the aims and goals (axiology) we have with respect to this domain. The niche in the economy occupied by any one organization is constantly changing depending on interactions. ‘Everything affects everything else in a web of connections’ (George Cowan in Waldrop (1992)). Biological metaphors (Morgan, 1997) and empathy with population ecology (Hannan and Freeman, 1977), also a macroperspective, are strongly evident in the focus on the whole in complexity science. New organizations and new organizational forms (types) emerge as others fade. ‘Emergence is above all a product of coupled, context-dependent interactions’ (Holland, 1998). One action may have varying effects on different parts of the complex systems, and may result in varying degrees of feedback, driving virtuous or vicious cycles. But this web of interactions occurs not just at the interorganizational level. If organizations can be construed as processes that are co-evolving with other processes (both in the organization and outside it), then change is an outcome of interaction in those co-evolving processes (Jantsch, 1975). Organizations are also parts of multiple systems of higher order complexity, such as markets, networks, societies and nations, each of which has its own set of distinctions and each of which co-evolves with the other. And the interaction occurs not just between units (e.g. processes, organizations, societies) at the same level. Evolutionary forces operate from microscopic processes via inter-woven systems to macro-structure (Capra, 1996). Via a series of interactions, the actions of one agent in an organization may affect an entire society. Complex systems adapt and evolve as their states are modified in ways to enhance its chances for success (Kauffman, 1995a; Capra, 1996). Such adaptive evolution in the classical Darwinian mould is thought to stem from random mutations that are then subject to natural selection (Coveney and Highfield, 1995; Holland, 1995; Hammerstein, 2001). This has been challenged by Kauffman (1995a), who shows how new order is generated via a process of selforganization. This conceptualization stresses the holistic view that the whole is greater than the sum of the parts and that order emerges from the complex interactions within the network of systemic relationships. Kauffman states that self-organization is the root of order. At each level, new emergent structures form and engage in new emergent behaviours. The nature of the component items of a system



231

cannot be used to predict emergent structures (Mihata, 1997). ‘Complexity is a science of emergence’ (Waldrop, 1992). Complexity tells us that the future is not predetermined. It is made by interaction. In a series of papers (Allen, 1976, 1994; Allen and McGlade, 1987), the essential driving force of evolution and of complex systems was shown to be the micro-diversity (heterogeneous and idiosyncratic individuals) that exists below any chosen level of description of real systems. It was shown that micro-diversity provides an internal pool of adaptive and creative behaviours that drives the evolution of the system as a whole, through successive structures and organizations, changing both the macro-structure and also the internal beliefs, criteria and aims that underlie individual behavioural responses. In this way, the internal beliefs and views held by agents of a given kind are shaped by their experiences that in turn result from the organization and structures that they inhabit. These are, in their turn, also formed by the behaviour and interactions of the individual agents. This is a circular system that either is self-reinforcing, marking a period of stability, or is not, marking the occurrence of an instability. The complex system that represents organizational evolution is therefore about periods of structural stability, when rational analysis and knowledge can exist, separated by instabilities, when new variables and aspects invade the system, and rational decision making is impossible. This can be understood by studying the assumptions and approximations made in arriving at a mechanical description or model of any evolved, and hence complex, part of reality. There are five assumptions: 1. that there is a system boundary, with the environment outside and the ‘system’ inside, 2. that we can define and classify the content of the system – the variables, mechanisms, processes, elements and their interactions – and we see that over time these have changed qualitatively, 3. that we can describe the system in terms of average types, 4. that we need consider only most probable (average) micro-events and 5. that the system has run to an equilibrium. This is illustrated in Figure 1 in which we see the different types of model and epistemology that arise as successively more constraining, simplifying assumptions are made. With no assumptions, reality is simply subjective experience, and survival would rely on intuitive, spontaneous responses. With the definition of a boundary, and with the ability to classify the elements and content of a situation, we would arrive at an evolutionary view, because over time almost all situations of interest are characterized by a qualitatively changing structure, with new entities and dimensions emerging and earlier ones disappearing. Considering the present however, the classification of the entities present, together with the probability of interactions between them, yields a description in terms of a probabilistic differential or difference equations, expressing the coupled behaviour of the system. Because of the usual presence of feedbacks and non-linearities, such a

Successive Simplifications Reality

Dictionary

Complexity

Noisy System Dynamics

Structure?

Freedom

x

y z

Evolutionary Model

Self-Organising Model Deterministic With “Noise” System Dynamics

X

Evolving Structure

Z

X

Y

Constraints

Mechanical Model Z

Study Attractor(s)

Equilibrium Model

Quantity

Y

Simplicity

Assumptions 1

2

3

4

Price

5

Boundary – Classification – average types – average events – equilibrium

Figure 1 Successive assumptions are used to reduce reality to a simple, causal mechanical description, with models that can ‘run’ but not evolve.

system would normally possess multiple possible dynamic attractors, and the probabilistic interactions would allow the system to move between different possible attractor basins under the effects of noise and fluctuations. For this reason, it is referred to as ‘self-organizing’ since the system could spontaneously jump from one configuration (attractor basin) to another. This kind of model could be used to test the spread of outcomes possible under various ‘what if’ scenarios. In this way, they would constitute an excellent basis for contingency planning, risk analysis and for testing the resilience of an organization. However, this all assumes that the organization and its environment does not evolve qualitatively, rendering the model incorrect. Making a further assumption that only the most probable events actually occur, we arrive at a system dynamical description of the system. This corresponds to an apparently ‘causal’ description of the behaviour of the system, one that supports rational decision making, and with which participants feel comfortable. The behaviour of the system is apparently predictable, and can be used for ‘what if’ scenario testing – and an exogenous ‘sensitivity analysis’ can replace partially the effects of fluctuations and noise that the ‘self-organizing’ model would treat more correctly. One further assumption that is often made is that of equilibrium. In particular, such methods as cost/benefit analysis of a possible decision rely on comparing the initial (equilibrium) situation with the final one. This is appealingly simple, but does ignore the probable fact that the system and its environment are never at equilibrium, and hence the calculation is false. This cascade of assumptions concerns the degree of understanding that we have of a situation. As we make successive simplifications, we are increasingly clear in our description of the situation, despite the fact that our assumptions may be false. We can only claim to really understand something if we can state exactly what it is made of, and how the different parts interact. But, in complex systems, and in organizations, ‘what agents are’ may be changing as they learn new things and formulate new preferences, and the way that the different agents interact may also be changing if new connections are formed, or if communication produces new knowledge, or beliefs. In short, the right-hand side of Figure 1 is where we



232

feel we understand what is going on and how the system works, while reality remains firmly on the left, and forces us to re-form our thinking when the dissonance between left and right become too great. Our understanding of reality is in terms of a set of interacting components that cannot evolve of themselves and the changes and innovations that occur in reality are merely taken account of by making a new, revised model of that reality. The mechanical, system dynamic view of the ‘functioning’ of the system at a given time is necessarily incomplete in that it does not include the micro-diversity within the agents that leads to new ideas and to learning, that will in fact change and modify things. Figure 1 tells us therefore that the key assumption (3) in which we assume a description of the current situation in terms of the average types, or homogeneous elements, currently present, is the critical one in which the ‘evolutionary potential’ is lost. In the real, complex system, there is internal heterogeneity, multiple different perspectives and constructs, and differing aims and goals, and it is the interaction of these things over time that will lead to evolutionary, structural qualitative change. As we shall discuss later, if the system under discussion is an organization of people, then the information technology may only allow flows permitting (even optimizing) the functioning at this particular moment, according to its current functional structure. But in reality this may be entirely inadequate to allow the agents within the organization to evolve and change enough for the organization to survive in its environment. Our understanding of reality is shaped by the IS we use. And our motivations are both personal and shaped by our perception of the organization’s culture and its values. For this reason, organizational change and evolution can only be understood successfully on the basis of a multi-agent view. In the next section, we describe the various bases for knowledge and show how complexity theorists span the spectrum from objectivist through subjectivist perceptions of reality. Evolving organizations and IS In our deconstruction of complexity, we see that organizations clearly correspond to the ‘evolutionary’ domain when two assumptions have been made: that a boundary can be defined (inside and outside the organization) and that we can classify the functional elements of the organization at any given time, The view accepts the fact that organizations evolve, changing the working practices, functional divisions, departmental structures and logistic flows. The models involving additional assumptions correspond to the existing, current structure, and are therefore systems models of the current operational system. When the assumptions of average types is used, we have a probabilistic systems model that can be used to study the behaviour of the organizations under fluctuating conditions – in other words, with uncertain demand, or supply, or disturbances of various kinds in its running. This kind of model can study the resilience and the ability of a real system to deal with contingencies of various kinds. The deterministic model that requires the assumption of only average, or most probable events, can be used to study the average behaviour of the organization, under smooth conditions.

Useful as these two systems models can be however, it still should not hide the reality that over time the organization, the agents that interact within it and their practices, aims and goals all co-evolve, in a longer term process of structural, qualitative evolution. Somehow, the design of the IT system of the organization must not hinder this evolution, and the IT system itself must evolve with the changing needs and patterns of interaction that will be required. Organization science has its roots in the functionalist paradigm (Burrell and Morgan, 1979) in which objects are perceived as real and as existing independently of the observer. Researchers taking a ‘normal science’ view of organizations (Curd and Cover, 1998; McKelvey, 2002) perceive that objective information exists in organizations and may be accessed for analysis. Organizations have been shown to function as information processing systems (Tushman and Nadler, 1978), with increasing uncertainty demanding greater information processing needs (Galbraith, 1977). Organizations and their members need to be adaptive systems and the analysis of the organization, its parts, its environment and its interactions makes the organization better able to adapt. Such analysis is usually quantitative and supported by modelling, which can explore alternative choices. Interpretivists, who take a subjective view of reality, frame constructs as a way to describe objects in tandem with the observer’s understanding, for example, by abstraction or by the use of metaphor. Objects are perceived as human constructions and methods seek to enable better understanding of these constructs; they are recognized as fallible and not a basis for prediction. Interpretivists generally subsume ontology within epistemology. Complexity theorists look at organizations according to the following: (1) agents and interactions underlie systems’ behaviour, which not only makes it unpredictable but also non-transparent in that it is not explained by some topdown functionality; (2) that non-average actions and events, feedback loops and non-linearity drive evolution and (3) that emergent structures are not necessarily optimal – existence is not an indication of sustainability. The methods used by complexity theorists are likely to vary depending upon the paradigm they pursue. Functionalists may experiment using dynamic, formal models such as agent-based computations simulations and experiments encapsulating rules (behaviours). These models have been developed in many ways to account for complex behaviour. Interpretivists may favour narrative, dialectic, phenomenological or hermeneutic methods with which to examine constructs. In using complexity science as a meta- theory, we also recognize its limitations, particularly issues concerning definitions and measures (Johnson and Burton, 1994; Cohen, 1999; Allen, 2001a). It follows that complexity science concerns both the nature of the system and the nature of its interpretation. It is about both ontology and epistemology. In summary, items, be they objects or shared constructs, that are capable of definition, measurement and analysis are placed in the ontological dimension. On the epistemological dimension, we suggest that individuals develop new items, by interaction with other agents, through reasoning



233

or imagination driven by individual value systems. At any one time, individuals may be cognizant of: 1. emergent items, which describe new ideas but are not well-defined, 2. maturing items, for which consensus is gaining from a growing number of individuals, 3. re-defining items, which are superseding existing items, 4. dying items, which have failed to achieve critical mass and 5. latent items, which may emerge but which require stimulation or interaction to gain development. Items may be multiple, insofar as they may describe classifications or sub-sets of other items. The key point is that agents need interaction with other agents in order to evolve items. They need to transmit ideas and other agents need to be receptive to these. Items in the ontological dimension that are codifiable and nearly tangible for many individuals with a nearhomogeneous understanding (e.g. orders, items shipped, etc.) are candidates for inclusion in IT systems. They are data items of types such as nominal or ordinal with predetermined value ranges or codes. Alternatively, items such as market, competition and innovation are very difficult to define and to obtain consensus as to meaning and measures. These are the types of item that are heterogeneous and unlikely to be part of an IT system. They may exist with specific interpretations in an IT system, for example, number of patents as a proxy for innovation. Items in the epistemological dimension are those perceived through the eyes of the individual. Individuals make sense and interpret the organizations and systems they touch. Analysis is qualitative and largely provisional due to the nature of what constructivists believe is knowable. Evolving agents and IS Let us now consider ontology, epistemology and axiology at an agent level. We will show how each contributes to the agent’s worldview. The agent’s ontology consists of the items in the agent’s reality. The agent’s epistemology acts as the information processor and creator of new ontological items. The agent’s axiology, that is their set of values and goals, however, is the basis for the agent deciding what is good, what matters and what the agent is going to pursue. In other words, agents’ knowledge will change because of the experience of new pieces of reality, but it will also be driven in directions that are of interest to the agent. None of ontology, epistemology and axiology is static; they coevolve as learning and knowledge develop by the agent as a consequence of interaction. So, what does this mean for IT systems in an organization? It follows that IT systems represent a consolidated sub-set of agents’ ontologies. IT systems consist of those items that organizational agents agree exist in reality and are necessary for the completion of organizational tasks. IT systems represent emergence at the organizational, macrolevel. As agents’ ontologies evolve, upgrades and revisions of IT systems follow. This is an example of the need to process and retain information by organizations, that is, the development of the organization’s memory (Walsh and Ungson, 1991; Walsh, 1995). IT systems reflect what is

perceived as a consolidated reality of organizational requirements. The IS of each agent is idiosyncratic and homogeneous only to the extent that agents interact and agree upon meaning. An IS is a property of an agent. Some of an agent’s IS is work-in progress, ill-defined and may never truly emerge; some will be ontological, but not included in any IT system because of reasons of cost, timing, frequency of use, etc. Ontology and epistemology co-evolve; the items that an agent holds as real form the building blocks for new items. We learn from experience, but this may either be passively encountered or may result from the experiments that we choose to do. Our axiology will, however, shape and motivate the experiments that we choose to undertake and therefore will affect the changes that occur in our epistemology. Our axiological notions are congruent with those of interpretive schemes (sets of values and beliefs) (Ranson et al., 1980; Daft and Weick, 1984) and archetypes (Greenwood and Hinings, 1988). And the relationship between organizational values and organizational structures for the purposes of successful change management (Hinings et al., 1996) is significant. The driver for a single organizational culture is consistent with recruiting and retaining agents with similar axiologies and value systems. Schein (1992) defines culture as ‘shared meaning and behavioury something that people have in association with others y stands for things that are important to people, that they value and is acted out in the things that people do’. Harmony or at least congruence in organization values, or by Schein’s definition, culture, will be reflected in a less diverse set of axiological items. Schein’s model of organization has three levels: the underlying level of implicit assumptions about how the world works, why we are here, etc.: the culture (aims and goals) and the behaviours and artefacts that we see. We see that complexity goes deeper than this, and includes an epistemological level of ‘how we think the system works’ (the axiological framework presented in this paper). Our IS are really an embodiment of this. As such they must coevolve with our constantly reformulating aims, and the changing reality we experience. New agents with an axiology similar to the dominant organizational values will be socialized more quickly and will find empathy with agents in the organization that share similar value sets. They will move the organization forward along a more congruent path than agents with quite different axiologies. The evolution of epistemology is motivated by axiology. An agent with no interest in a particular area will not contribute to its evolution. But as not all agents will have identical axiologies, there are likely to be multiple, interacting individual motivations. The harnessing of these different, conflicting and cooperating potential interests, partly through the operation of one or more IT systems, can lead to successful emergent behaviour of the organization. And importantly, as the organization is a complex system, the emergent outcomes could not have been predicted. Success may not arise from the imposition of a single idea about what knowledge is required, what it means and how the operation and outcomes should be judged. It may arise instead from a messy concatenation of different views about these things that lead to multiple explorations, and to the amplification of successful



234

responses. Clarity, transparency and rationality may only be possible as post hoc analyses of the past. A successful future may perhaps only be attained through much more obscure routes. In reality, the interaction of two or more agents exploring their IS will lead to the development or elimination of new knowledge as epistemological items are explored, resolved, eliminated or combined in a process of self-transcendence (Jantsch, 1983). Forces such as planning and controlling, which are largely supported by IT systems, push the organization towards stability and order. Forces of innovation and experimentation, that which remains uncodifiable (because new concepts are being invented and not yet described), largely abstract and not diffused, are forces of instability and disorder. Entrepreneurial organizations (Burgelman, 1983; Bygrave, 1989) especially give organizational members adequate liberty to experiment, learn and explore, enabling the organization to develop a repertoire of responses to various as yet unknown challenges from competition in the environment. Nonaka (1988) states that freedom to generate creative conflicts between parts of the organization is necessary for organization evolution. More generally, self-renewing and self-organizing organizations are consistent with organizational learning. Learning is a willingness to experiment and search for new ways of doing things, Senge (1990). Knowledge, once put into practice becomes a source of learning. Consistent with this, Argyris and Schon (1978) see potential for change within the many instabilities and incoherences that accompany planned activities. More and more comprehensive IT systems are developed and implemented, but instead of reaching the intended facilitation and making the business more efficient, the complexity has become so high that users sometimes experience increased difficulty and reduced efficiency (Lind and Lind, 2005). This increased complexity relates not only to the computer-based IT system, but to the user perception of the nature of the information and ultimately their behaviour in managing the information for the benefit of the organization. An IT system represents a snapshot in time. It reflects what was needed (and this may include some compromise if different communities share an IT system), affordable and understood at the time of commissioning. Even with a short delay between commissioning and implementation, the IT system may not be adequate to meet the information processing needs of organizational members. An IT system is always dated and there is always a problem moving it on, but it does need to move on. Constructs in the real world rapidly overtake IT systems. The IT system can only ever contain what is known. IT systems can clearly contribute most to the organizational IS when the business processes are well defined and stable. This would be true for the part of the organization that is primarily concerned with current production or delivery of existing output the current ‘cash cow’. An agent’s IS needs to be able to both exploit the operational cash cow and to enable innovation and exploration of new organizational requirements. Each agent may use a variety of IT systems, such as e-mail, e-procurement, etc. in order to deliver services to customers. These IT systems, and their fit to business processes, enable the delivery of services. It is

the complete set of IT and non-IT systems and processes that an agent uses that we class as their IS. Each agent may therefore have different IS. And each information system automatically leads on to further questions. It raises issues for the agent, which may require new classifications not included in the information. It calls for greater distinctions in the information. This is a consequence of the atomistic nature of reality. Any one IT system is unlikely to include every variable that every organizational agent needs to manipulate. In practice, IT systems provide the functionality that was assessed as mandatory and a priority when the IT system was evaluated. Co-design of business and IT systems are inter-related (Liu et al., 2002). Even by the time the IT system is procured, new mandatory functionality may have arisen. And, for some agents, IT systems provide for only a fraction of their needs. As information needs evolve, IT systems meet less and less of agent needs. Agents always work with IT systems that represent some fraction of their needs at some time in the past. The IT system is limiting. If the IT system included everything employed in the IS of any one agent, then the IT system itself would be their reality. Because the IT system does not evolve, except by dint of upgrades, new releases, etc, that is, new variables being imagined by agents that are codified and well-defined, then we know IT systems do not represent reality. Reality is always ahead of IT systems. The complex system that the IS of a firm therefore itself constitutes, sits within a market and a business environment that is also an evolving complex system. The two co-evolve indeed, as the improved (or worsening) performance of an organization impacts on its potential customers, and its competitors. So, IS is both a limiting factor on the performance of a firm in a changing world, and also a potential competitive advantage. What is required is that the agents within the firm, the ‘nodes’ of the IS, are able to interpret what is happening, and also to imagine and experiment on how this might be enhanced. Summarizing this we are saying that individual schemas and experimentation are vital in defining the ontology, which is why there is a co-evolution of axiology, epistemology and ontology. This leads to the problem of IT systems always being behind reality (i.e. the gap between IT systems’ provision based on out-of-date ontological representations and the agent’s evolving ontology). This leads us to the question of how this gap might be bridged, allowing IT systems to better support agents in the real world, and in the next section we will suggest that modelling may be the means by which the gap is addressed. Organizational IT systems and modelling On the whole, organizational models, describing possible developments, exploring (seriously) the possible outcomes of different strategies are not present in the current IT systems of organizations. Yet, models are not new in organization science. McKelvey (2002) states that modelling is a valid approach for conducting organizational science. Simulation modelling, considering productivity improvement and improved decision making, are used in assessing



235

the effects of potential technology investments (King and Burgess, 2006). The use of dynamic simulation models that incorporate the effect of IS on existing business processes can help with decision making and in achieving maximum fit between organizational needs and system capabilities (Giaglis et al., 2005). Bruderer and Singh (1996) built a model of organization creation, learning and death, using a genetic algorithm. The model assumes the three Darwinian processes of variation, adaptation and selection. They show that environmental selection influences adaptation, which in turn influences selection. They conclude that there are three distinct phases of organizational evolution: experimentation, revolution and incremental improvement. Experimentation exemplifies new organizations. Revolution triggers the emergence of a dominant design. Paul et al.’s (1996) genetic algorithm models organizational adaptation and resource allocation. The adaptive capability affects performance more than the initial arrangement of organizational members, that is, the organizational structure. These kinds of model show us the potential capacity of IT systems to support organizational evolution, helping to articulate the evolving values and goals of the organization and the importance that this has in a changing reality, for the needs and requirements of the IT system. Agent-based models try to simulate features of complex systems by simulating self-organization, order creation and emergence of structural attractors (form and content of naturally sympathetic elements). The fitness (or performance) (see Hannan and Freeman, 1977) of particular structural attractors can be measured. Learning and adaptation may also be simulated (Kauffman, 1995b). A Cellular Automata model used by March (1991) had agents learning and modifying their beliefs based on organization norms. The model demonstrates advantages of having a mix of fast and slow learners to balance exploration and exploitation dynamics. Agent-based modelling (ABM), and the contribution of the Santa Fe Institute to complexity science via computational ABM (Casti, 1997), is still relatively new to organization studies. Although very promising, ABMs require internal elements to represent real-life phenomena more closely (Carley, 2002); agents need to ‘learn’ and have some measure of cognition; the relationship between emergence and agent activation and capabilities and other organizational contingencies. Kauffman’s NKC model is a co-evolutionary cellular automata model that has been used to develop organization theory. The NKC model simulates organizational fitness (performance) landscapes. It has shown that taking a local/ incremental perspective is disadvantageous (Kauffman and Macready, 1995). An organization can become trapped in a local performance peak and may miss alternative higher performance peaks. In Figure 1, we have shown the different types of organizational model that can be developed, according to the number of assumptions made concerning its operation. However, the system dynamics models should not be dismissed too rapidly because they do not have an evolutionary capability. The development of such a model can certainly allow the modeller to explore different parameters, decision thresholds and possible information

flows. Similarly, the ‘noisy’ system dynamic model can also be exceedingly useful in exploring parameters, thresholds and information flows in the presence of realistic fluctuations. In other words, systems models of this kind can also be seen as multi-agent models, and can simulate different possible structures of distributed intelligence. Such simulations can be of real use for organizations that cannot afford to conduct multiple ‘experiments’ on-line. The model can be run millions of times and can allow the modeller to discover much more effective parameters, decision structures, information flows and decision thresholds (Datta et al., 2006). Such models are really of operational interest, but can really help improve business performance under conditions of somewhat unpredictable demand – a situation that is of enormous generality. Such models can make systems both more resilient to perturbations and also more efficient. For the more ambitious, evolutionary models of Figure 1, those involving evolutionary, structural change over time, despite examples being given for many years, these have failed to be picked up generally, with the result that most IT systems have no ‘intelligence’ or sophisticated decision support that would allow strategic ‘what ifs’ to be explored. In fact, complex systems models of this kind can be of great potential use, for example, in establishing the organizational features that underlie the ability to adapt and evolve (Allen, 1994), in exploring how distribution or supply systems could self-organize over time (Allen and Strathern, 2004) and in making decisions about organizational innovation and the introduction of new working practices and techniques (Baldwin et al., 2003; Allen and Strathern, 2004; Allen et al., 2006). Similarly, there are now complex systems models of new product development (McCarthy et al., 2006) and of the discovery process in the pharmaceutical domain (Van Dyck and Allen, 2006), and so clearly, these represent important additions that will greatly improve IT systems, in some ways allowing them to feed the IS that people have, particularly in regard of imagining possible futures, and possible strategic transformations. Evolutionary models of markets have also been developed, which can allow a firm to explore the potential returns on different pricing and quality strategies for their products (Allen, 2001b, 2004; Allen and Strathern, 2004). These kinds of models could be incorporated in IT systems to support strategic assessments of organizational structure, of possible product and market strategies. Such models allow us to explore the possible evolution of the system based on the axiologies of the actors involved, which change more slowly than their behaviour. These models are a natural and necessary improvement on the current situation in which more often than not, making changes in spreadsheets are the only way that ‘what if’ scenarios are explored. But simple spreadsheet models correspond to the far right-hand side of Figure 1, since they can only take a stationary situation and consider the stationary outcome resulting from some change. In addition, spreadsheets cannot really deal with dynamic situations in which there is systemic feedback, some of which is circular. Simulation models, whether mechanistic, noisy or evolutionary, can and should therefore become additional features of IT systems, allowing people to explore their degree of ignorance concerning a situation, as well as test out their ideas and



236

explore possible changes. These models can represent both the axiologies and epistemologies of different agents, and can explore the possible outcomes of their interaction. It could even allow us to anticipate the way that the agents may change their axiologies and epistemologies as a result of the changing ontology. They extend the agent’s field and extent of vision (i.e. by providing access corporate wide and to external data) and coupling these with the modelling capacity would provide a powerful means of doing ‘what ifs’ and other experimentation. Conclusions The idea that the successful evolution of organizations as complex systems requires the expression of individual diversity of ideas, beliefs and aims, is very important. Clearly, it goes against many of the more usual views about efficiency, leanness and quality control that are currently held. In many ways, it comes down to the attitude to risk and the ability of individuals within an organization to experiment with their own ideas about what they should be doing and how. Obviously, experimentation on the production line of a pharmaceutical company is not something to encourage, and yet learning can only be achieved by experiments and feedback from the outcomes. Testing of ideas would need to be tried out to see what really happened. And this is where we see that management is itself an experimental process, and should be judged according to the feedback from its outcomes. The IS (IT systems and other connections) of an organization are really the infrastructure of its ‘brain’, and the acquisition of information, the sense-making of these readings and the implications and response to this are determined by the connectivity and the contents of the nodes in the system, and the structure of power to act on the emergent knowledge that allows evolution and learning. Complexity science provides a framework that allows new concepts to be developed and promotes formal modelling (Morel and Ramanujam, 1999). Both of these enable new insights into organizational phenomena. In particular, it takes a systems view that is already a sound foundation in organization studies. Complexity science is the science of evolution. Computational models enable the study of emergence triggered by interdependent, heterogeneous actors (individuals). Complexity science also encompasses multi-paradigm perspectives, quantitative and qualitative methods, both ontology and epistemology. Our contribution here places IS clearly in the epistemological domain. IS are the basis upon which organizational members understand what is going on. We make the role of axiology, that is, the role of value systems and motivations, more clear, and we show how an individual’s (an agent’s or actor’s) internalization of what he perceives as reality, how he interprets reality and what motivates him/her to select different choices, are closely inter-related. A major concern of IS analysis has been the perceived success of IS versus their actual cost. The ‘productivity paradox’ of IS is that despite enormous improvements in the underlying technology, the benefits of Information Systems’ spending have not been found in aggregate output statistics. There are explanations: (1) the exclusion of quality or

variety in such statistics; (2) costs associated with restructuring and cost-cutting that are often necessary to realize potential benefits of IS have only recently been undertaken by firms (Brynjolfsson and Hitt, 1996); (3) high-complexity technology is associated with higher risk of failure: multiple competencies are required together, with coordination of many dissimilar components, drawn from different knowledge bases, so structures will be organizationally complex as differently organized sub-units will need to be closely related; and (4) requisite variety is required to achieve this degree of coordination (Singh, 1997). The recognition of IS as evolving bases of knowledge, driven by individual value systems, improves the nature of enquiry into IS success. The link between IS design and complexity is the need to endow an IS with the capacity to play a full role in such a process, and not to be a bottleneck or failure point that stops new ideas being conceived and tried out. Such an IS must allow for reconfiguration of its connections, for the inclusion of new nodes and the exclusion of old, and for quite different information to be carried along the links. In a changing environment, the capabilities of the element may need to change over time, and therefore that the connections made, and the content of the exchanges must co-evolve with the system in which it is embedded. Ultimately, the capabilities of the overall system will result from the connected capabilities of the participating elements – but all of these things need to be discovered by an active learning process in which the internal content of nodes and the connections that it makes all co-evolve so as to deliver an effective performance in the overall environment. So, an IS system within an organization should be seen as a matrix of possible connections – not only of those that are known to be necessary now but also others that we do not yet have a reason to make use of. Here our IS reflects our current epistemology (model of what is going on) and so must co-evolve with our changing aims and goals and our experiences of reality. Taking this agent-based complex systems perspective of IS leads us to suggest that IS are themselves complex systems, which display structural evolution over time, and furthermore that they are themselves part of a larger complex system of a business co-evolving with its environment. In this way, an IS is part of the internal mechanisms through which a business adapts and changes in its dealings with its environment, and one that expresses both a changing epistemology (internal model and variables about the environment) as a response to a changing ontology (changing technological and business environment). New variables emerge over time, and may either supersede existing ones or bring additional considerations into play. It is also probable that the IS will initially bring in a new variable or factor, and will evolve to some kind of trade-off structure for the best use of the variables currently considered important within the epistemology/axiology of that period. An initial variable is identified, and the intuitive value of taking it into account is accepted, and the IS is duly adapted to enable this to happen. However, with experience in using the new variable in decision making, other aspects emerge that are affected or affect the outcome, so that either the positive gains imagined before do not actually happen, or it is perceived how they can be



237

further improved by paying attention to a broader set of linked issues. The IS changes over time as it is modified, used and then re-modified as the experience demonstrates issues and linkages, barriers and opportunities that were not imagined before it was used. In this way, it is no different from any other artefact, and any design process. There are always emergent attributes and issues that follow some change, and these need to be dealt with in a chain or cascade of successive adjustments and adaptations based on experiential learning. The evolution and the expansion of IS within businesses both characterizes a complex system within the business, and also plays a role in the complex system of a wider economic and social reality in which it is embedded. The changes reflect the feedback between a changing reality and the changing internal aims, goals and interpretive frameworks of the interacting agents. What makes IS complex is that it can never be considered to be complete, closed or correct (Mark Strathern, private communication). However, it often is considered to be all of these and this leads eventually to a crisis that is resolved by some qualitative, radical re-structuring of the system, and the internal and external reality that it is in interaction with. Clearly, here we make the case for the role that modelling could play in facilitating and permitting the necessary ongoing learning process, with models embodying the epistemology of the individuals and the organization. It seems time for IT systems to incorporate such modelling capabilities, bringing evolutionary intelligence to the help of individuals and of the organization. In this paper, we have indicated how an IS is both an expression of a complex system itself, and also is embedded and co-evolving within a complex system. The performance of the organization needs to be reviewed and updated continually if it is to survive in a changing world, and this requires that the IS should evolve and change qualitatively over time in order to allow this. This is because the IS is in some ways the infrastructure of the epistemology of the firm. It allows the information to flow to and between the different agents within the organization so that they can know what is happening, and have the information necessary for their criteria of decision. These criteria will be based on their place in the system, their perceived role and function and how they believe this can be achieved. Over time, they will question their performance and consider events and experiences that have occurred and choose to update the variables and parameters that they need to know in order to perform. Obviously, what matters then is whether agents can use the IS to understand the effect that changes in their behaviour, or responses has on the overall outcome of the firm. If this is possible, then the agents can learn how to improve the behaviour of the firm through experiments with their own behaviour. This in fact takes us back to questions of the decomposability of the functioning of the firm, since a modular organization may not perform quite as well as a fully integral system, but may allow a much easier reading of feedback – the stuff of learning. Our axiological framework implies that we may be able to define the mechanisms of agent-based IS that are necessary for successful co-evolution, and if this can be done, we may be able to improve the performance of organizations and at least explain failure in scientific terms.

Acknowledgements This work was supported by ESRC Grant No.: RES-000-23-0845, ‘Modelling the Evolution of the Aerospace Supply Chain’. We thank the reviewers, and particularly the editors for their helpful comments and suggestions.

References Allen, P.M. (1976). Evolution, Population Dynamics and Stability, Proc Natl Acad Sci USA 73(3): 665–668. Allen, P.M. (2004). The Complexity of Structure, Strategy and Decision Making, in J. Foster and J.S. Metcalfe (eds.) Evolution and Economic Complexity, Cheltenham: Edward Elgar, pp. 85–107. Allen, P.M. and McGlade, J.M. (1987). Evolutionary Drive: The effect of microscopic diversity, error making & noise, Foundation of Physics 17(7): 723–728. Allen, P.M. and Strathern, M. (2004). Evolution, Emergence and Learning in Complex Systems, Emergence 5(4): 8–33. Allen, P.M., Strathern, M. and Baldwin, J.S. (2006). Evolutionary Drive: New understanding of change in socio-economic systems, Emergence Complexity & Organization 8(2): 2–19. Allen, P.M. (1994). Coherence, Chaos and Evolution in the Social Context, Futures 26(6): 583–597. Allen, P.M. (2001a). A Complex Systems Approach to Learning, Adaptive Networks, International Journal of Innovation Management 5(2): 149–180. Allen, P.M. (2001b). What Is Complexity Science? Knowledge of the Limits to Knowledge, Emergence 3(1): 24–42. Argyris, C. and Schon, D.A. (1978). Organizational Learning: A Theory of Action Perspective, Reading, MA: Addison-Wesley. Baldwin, J.S., Allen, P.M., Winder, B. and Ridgway, K. (2003). Simulating the Cladistic Evolution of Manufacturing, Journal of Innovation Management, Policy and Practice 5(3): 144–156. Bruderer, E. and Singh, J.V. (1996). Organization Evolution, Learning and Selection: A Genetic-Algorithm Based Model, Academy Management Journal 39(5): 1322–1349. Brynjolfsson, E. and Hitt, L. (1996). Paradox Lost? Firm-level evidence on the returns to information systems spending, Management Science 42(4): 541–558. Burgelman, R.A. (1983). Corporate Entrepreneurship and Strategic Management: Insights from a process study, Management Science 29(12): 1349–1364. Burrell, G. and Morgan, G. (1979). Sociological Paradigms and Organizational Analysis, Ashgate Publishing, Aldershot: UK. Bygrave, W.D. (1989). The Entrepreneurship Paradigm (II): Chaos and catastrophes among quantum jumps, Entrepreneurship: Theory and Practice 14(2): 7–30. Capra, F. (1996). The Web of Life: A New Scientific Understanding of Living Systems, New York: Anchor Books. Carley, K.M. (2002). Intra-Organizational Complexity and Computation, in J.A.C. Baum (ed.) The Blackwell Companion to Organizations, Oxford: Blackwell, pp. 208–232. Casti, J.L. (1997). Would-Be Worlds: How Simulation Is Changing the Frontiers of Science, New York: John Wiley and Sons. Cohen, M. (1999). Commentary on the Organizational Science Special Issue on Complexity, Organization Science 10(3): 373–376. Coveney, P. and Highfield, R. (1995). Frontiers of Complexity: The Search for Order in a Chaotic World, New York, NY: Fawcett Columbine. Curd, M. and Cover, J.A. (1998). Philosophy of Science: The Central Issues, New York: Norton. Daft, R.L. and Weick, K.E. (1984). Toward a Model of Organizations as Interpretation Systems, Academy of Management Review 9(2): 284–295. Datta, P., Christopher, M. and Allen, P.M. (2006). Proceedings of the EIASM Conference, Oxford: June. Galbraith, J.R. (1977). Organizational Design, Reading, MA: Addison-Wesley. Giaglis, G.M., Hlupic, V., de Vreede, G.-J. and Verbraeck, A. (2005). Synchronous Design of Business Processes and Information Systems Using Dynamic Process Modelling, Business Process Management Journal 11(5): 488–500. Greenwood, R. and Hinings, C.R. (1988). Organizational Design Types, Tracks and the Dynamics of Strategic Change, Organization Studies 9(3): 293–316.



238

Hammerstein, P. (2001). Evolutionary Adaptation and the Economic Concept of Bounded Rationality – A dialogue, in G. Gigerenzer and R. Selten (eds.) Bounded Rationality: The Adaptive Toolbox, Cambridge, MA: The MIT Press, pp. 71–82. Hannan, M.T. and Freeman, J. (1977). The Population Ecology of Organizations, American Journal of Sociology 82: 929–964. Hinings, C.R., Thibault, L., Slack, T. and Kikulis, L.M. (1996). Values and Organizational Structure, Human Relations 49(7): 885–916. Holland, J. (1995). Hidden Order: How Adaptation Builds Complexity, Cambridge, MA: Perseus Books. Holland, J. (1998). Emergence: From Chaos to Order, Cambridge, MA: Perseus Books. Jantsch, E. (1975). Design for Evolution: Self-Organization and Planning in the Life of Human Systems, New York: Braziller. Jantsch, E. (1983). The Self-Organizing Universe, 1st edn Oxford, UK: Pergamon Press. Johnson, J.L. and Burton, B.K. (1994). Chaos and Complexity Theory for Management; Caveat Emptor, Journal of Management Inquiry 3(4): 320–328. Kauffman, S.A. (1995a). At Home in the Universe, Oxford: Oxford University Press. Kauffman, S.A. (1995b). Escaping the Red Queen Effect, The McKinsey Quarterly 1: 119–129. Kauffman, S.A. and Macready, W. (1995). Technological Evolution and Adaptive Organizations, Complexity 1(2): 26–43. King, S.F. and Burgess, T.F. (2006). Beyond Critical Success Factors: A dynamic model of enterprise system innovation, International Journal of Information Management 26: 59–69. Lind, A. and Lind, B. (2005). Systems Research and Behaviour Science, Systems Research and Behavioural Science 22: 453–464. Liu, K., Sun, L. and Bennett, K. (2002). Co-Design of Business and IT Systems – Introduction by Guest Editors, Information Systems Frontiers 4(3): 251–256. Lorenz, E.N. (1963). Deterministic Nonperiodic Flow, Journal of the Atmospheric Sciences 20(1): 30–141. March, J.G. (1991). Exploration and Exploitation in Organizational Learning, Organization Science 2(1): 71–87. McCarthy, I.P., Tsinopoulos, C., Allen, P.M. and Rose-Anderssen, C. (2006). New Product Development as a Complex Adaptive System of Decisions, Journal of Product Innovation Management 23: 437–456. McKelvey, B. (1999). Toward a Campbellian Realist Organization Science, in J.A.C. Baum and B. McKelvey (eds.) Variations in Organization Science: In Honor of Donald T, Thousand Oaks: Campbell, Sage Publications Inc, pp. 383–411. McKelvey, B. (2002). Model-Centred Organization Science Epistemology, in J.A.C. Baum (ed.) The Blackwell Companion to Organizations, Oxford: Blackwell, pp. 752–780. Mihata, K. (1997). The Persistence of Emergence, in Eve, R.A., Horsfall, S. and Lee, M.E. (eds.) Chaos, Complexity and Sociology: Myths, Models & Theories, Thousand Oaks, CA: Sage Publications Inc, pp. 30–38. Morel, B. and Ramanujam, R. (1999). Through the Looking Glass of Complexity: The dynamics of organizations as adaptive and evolving systems, Organization Science 10(3): 278–293. Morgan, G. (1997). Images of Organization, Thousand Oaks, CA: Sage Publications Inc. Nonaka, L. (1988). Creating Organizational Order Out of Chaos: Self-renewal in Japanese firms, California Management Review 30(3): 57–73. Paul, D.L., Butler, J.C., Pearlson, K.E. and Whinston, A.B. (1996). Computationally Modeling Organizational Learning and Adaptability As Resource Allocation: An artificial adaptive systems approach, Computational and Mathematical Organization Theory 2(4): 301–324. Ranson, S., Hinings, B. and Greenwood, R. (1980). The Structuring of Organizational Structures, Administrative Science Quarterly 25(1): 1–17. Schein, E.H. (1992). Organizational Culture and Leadership, 2nd edn. San Francisco: Jossey-Bass Publications. Senge, P.M. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization, 1st edn. New York: Doubleday. Simon, H.A. (2001). Complex Systems: The interplay of organizations and markets in contemporary society, Computational and Mathematical Organization Theory 7(2): 79.

Singh, K. (1997). The Impact of Technological Complexity and Interfirm Cooperation on Business Survival, Academy of Management Journal 40(2): 339–365. Thompson, J.D. (1967). Organizations in Action: Social Science Bases of Administrative Theory, New York: McGraw-Hill. Tushman, M.L. and Nadler, D.A. (1978). Information Processing as an Integrating Concept in Organizational Design, Academy of Management Review 3: 613–624. Van Dyck, W. and Allen, P.M. (2006). Pharmaceutical Discovery as a Complex System of Decisions: Front Loaded Experimentation, Emergence: Complexity & Organization 8(3): 40–56. Van Valen, L. (1983). How Pervasive Is Coevolution? in M. Nitecki (ed.) Coevolution, Chicago, IL: University of Chicago Press. Waldrop, M.M. (1992). Complexity: The Emerging Science at the Edge of Order and Chaos, New York, NY: Simon & Schuster. Walsh, J.P. (1995). Managerial and Organizational Cognition: Notes from a trip down memory lane, Organization Science 6(3): 280–321. Walsh, J.P. and Ungson, G.R. (1991). Organizational Memory, Academy of Management Review 16(1): 57–91.

About the authors Peter Allen is Professor of Evolutionary Complex Systems and Head of the Complex Systems Management Centre, which is involved in a wide range of research projects including working with the DTI – Foresight Project on Intelligent Infrastructure Systems; the Asian Development Bank, and EABIS. Professor Allen has also coordinated the ESRC priority network ‘NEXSUS’ over the past 5 years. NEXSUS is continuing with a collaborating project from a NEXSUS partner – an ESRC-funded joint project with the University of Sheffield ‘Modelling the Evolution of the Aerospace Supply Chain’. He has a Ph.D. in Theoretical Physics, was a Royal Society European Research Fellow 1969–1971 and a Senior Research Fellow at the Universite Libre de Bruxelles from 1972 to 1987, where he worked with Nobel Prizewinner, Ilya Prigogine. Since 1987 he has run two Research Centres at Cranfield University. For almost 30 years, Professor Allen has been working on the mathematical modelling of change and innovation in social, economic, financial and ecological systems, and the development of integrated systems models linking the physical, ecological and socio-economic aspects of complex systems as a basis for improved decision support systems. Professor Allen has written and edited several books and published well over 200 articles in a range of fields including ecology, social science, urban and regional science, economics, systems theory and physics. He has been a consultant to the Canadian Fishing Industry, Elf Aquitaine, BT, GlaxoSmithKline, DERA, DSTL, the United Nations University and the European Commission. He is an Editor-in-Chief of the Journal Emergence: Complexity and Organization (E:CO) and a Director and Co-Founder of The Complexity Society. Liz Varga is working as a Research Officer in the Complex Systems Management Centre, a part of the School of Management, Cranfield University, where her research focus is in the evolution of aerospace supply chains. She is pursuing her doctoral studies with research interests in complexity and organization evolution, organizational forms, philosophical paradigms and paradox. She is a consultant to a number of UK public sector organizations.