Systems Science and Systems Engineering White ... - Systems Praxis

Systems Science and Systems Engineering White Paper Version 1.1 September 26, 2011 Lead Author: Gary Metcalf Co-authors, Contributors, and Interested Parties: Jack Ring, Duane Hybertson, Bud Lawson, Jennifer Wilby, Len Troncale, Hillary Sillitto, David Ing

Introduction This paper is meant to be an ongoing work in progress. Its intent is to describe some of the commonalities and potential synergies between systems science and systems engineering. As human endeavors, though, both fields will continue to evolve, as will their potential connections to each other. Eventually, it should indicate future directions for both fields. There have been movements recently to more closely align systems science and systems engineering organizations, based upon a belief that each needs to change in response to impending challenges. This paper will provide a rationale for those beliefs, and outline a possible path forward. Section 1 of the paper describes specific organizations which represent significant members and practitioners of each area: the International Society for the Systems Sciences (ISSS: www.isss.org), the International Federation for Systems Research (IFSR: www.ifsr.org), and the International Council on Systems Engineering (INCOSE: www.incose.org). Section 2 addresses some of the common histories of systems science and systems engineering. Section 3 outlines current challenges for systems engineering, and Section 4 provides examples of changes to be considered. Section 5 explores some of the interdependencies between systems science and systems engineering, including ways in which science and engineering (i.e. understanding and acting) more generally might be brought closer together. Section 6 offers some very preliminary conclusions. 1.0 Systems Science and Systems Engineering Organizations The ISSS was established in 1956, originally as the Society for General Systems Research, an affiliate of the American Association for the Advancement of Science. It is often associated with the work of Ludwig von Bertalanffy and the efforts to develop a General System Theory, but its members and leaders have spanned a wide range of professional backgrounds and practice areas. The International Federation for Systems Research (IFSR: www.ifsr.org), founded in 1981, is a federation of systems organizations around the world (including the ISSS.) Those member organizations cover a spectrum of geographic regions and of specific systems orientations (e.g. cybernetics). INCOSE, founded in 1991, has a membership of nearly 8000 systems engineers, most prominently from aerospace and defense industries in the US, but also spanning many other countries.

In 2010, INCOSE began an internal working group focused on system science, whose charter is to “promote the advancement and understanding of Systems Science and its application to SE” (http:/ /www.incose.org/practice/techactivities/wg/syssciwg/). The stated objectives were to: “1) encourage advancement of systems science principles and concepts as they apply to systems engineering, 2) promote awareness of systems science as a foundation for systems engineering, and 3) highlight linkages between systems science theories and empirical practices of systems engineering.” In concert with this same intent, INCOSE and ISSS began formally inviting representatives as guests to each other’s meetings, in an exchange of ideas and interests. In 2011, ISSS and INCOSE formally signed memorandum of understanding, based on the following principles: 1. The ISSS and INCOSE agree to a relationship for mutual benefit, to be reconfirmed every three years. The purpose of relationship is to further the practices and knowledge jointly in systems sciences and systems engineering. 2. INCOSE members are interested in gaining foundational knowledge in systems science concepts, methods and tools that may be applied in the practice of systems engineering. 3. ISSS members are interested in seeing systems theories applied in practice, and further developing approaches on practical problems in systems engineering, based on the rich legacy of research in the systems sciences. Also in 2011, INCOSE applied for membership in the IFSR. 2.0 Historical Connections Between Systems Engineering and Systems Science According to Hybertson (2009), “in its modern form, SE [systems engineering] was developed in the 1950s at least in part as a more systematic approach than the ad hoc activities undertaken during World War II” (p. 3). Those efforts created what Hybertson refers to as TSE (traditional systems engineering), which dealt with mechanical systems, or all systems as if they were mechanistic. The expected outcomes of SE projects during that era were clear, and requirements (it was believed) could be stated explicitly. Like Hybertson (2009), the INCOSE Systems Engineering Handbook (2000) traces the formal development of SE to the 1950s, and to pressures for improved methods of development, management, and production coming out of World War II. According to the handbook, those who first acted in the role of systems engineers were typically project managers or chief engineers who organized the various professionals and subsystems involved in an operation. More specifically, “early Systems Engineers operated without any theory or science of Systems Engineering or any defined and consistently-applied processes or practices” (http://g2sebok.incose.org/documents/assets/MSS//Final/ sh%20hdbk%202.1.pdf). The development of SE followed disciplines such as operations research (OR), which often traces

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its roots to the British Military in WWII. Notably, though, Leonardo da Vinci had articulated a key systems engineering tenet, “Start with the end in mind” circa mid-1400’s. According to Visco (2011), the use of engineers and scientists for military applications traces back at least to Alexander the Great, around 330 BCE (http://www.incose.org/orlando/Attach/201103/ Origins_of_Operations_Research_21March2011.ppt). The Code of Hammarubi, stating rules for making things and penalties for errors dates by to 1700 BCE. Differences in the era of WWII included both the degree of development, and reliance on, scientific and technical discoveries. The most notable, of course, was the use of physics in the development of the atomic bomb. Airplanes and submarines created distinct advantages, which were countered by the development of radar and sonar. Arguably more important were the developments in mechanisms of information and control. Antiaircraft guns, for instance, were enhanced with servomechanisms (feedback systems), greatly increasing their accuracy. Research in servomechanisms formed the basis for Whirlwind, the first computer which operated in real time and used a video display for its output, as well as providing the theoretical foundation for System Dynamics modeling. It is reasonable to suppose that the “intellectual climate” at the close of World War II was especially receptive to the ideas developed in the context of game theory, that is, ideas related to the rational conduct of conflict. Already in World War I, the power of science as a fountainhead of technology replaced the traditional martial virtues (valor, elan, selfless loyalty, etc.) as the main contributor to victory. The unprecedented contributions of scientific thought to war technology during World War II added further to the prestige of intellectual endeavor. The theory of games appeared as a powerful tool for developing a sophisticated science of strategy, not only in war but in many other pursuits where success depended on effective conduct of conflict, for example in business competition or in competitive politics (Rapaport, 1996, p. 26). Or, as Axelrod (1997) offered: Lanchester’s Law equating the relative size of opposing forces to the rates of respective losses was an early game model presaging by 50 years the obverse, Metcalf’s Law equating the worth of participating in a network to the number of active nodes in the network. WWII and the decade of the 1950s also spurred many developments in systems science. Even before the ISSS was formed, a series of meetings now known as the Macy Conferences (sponsored by the Josiah Macy, Jr. Foundation) was held, which focused on issues of cognition and communication. Out of these came the foundation for the field of cybernetics (as described most notably by Norbert Wiener in his book of the same name.)

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A number of notable systems scientists came out backgrounds in operations research, including C. West Churchman and Russ Ackoff in the U.S., and Peter Checkland in the U.K. Stafford Beer’s work in Viable Systems also began with his experiences in the British Military in WWII. Early on, though, systems scientists recognized the limitations of using industrial models and methods when applied to human realms. Mathematical models provided specificity, but often at the cost of accuracy. A model, for instance, might well describe the factors which it entailed, but at the risk of omitting factors critical to the actual functioning of the system. Just as importantly – in some cases, more so – models are built on assumptions which are often unstated, and frequently even unrecognized. Prof. George Box’s observation that, “All models are wrong, some are useful,” Captured this succinctly.. Out of these kinds of dilemmas came the development of “next-generation” systems ideas. Cyberneticists such as Heinz von Foerester described second-order cybernetics, in order to include the role of humans in the system. Churchman strongly emphasized the role of ethics in systems design. And Checkland introduced his Soft Systems Methodology to highlight, among other things, the notion of an intervention system that affects other systems and thereby changes itself. Out of other engineering backgrounds came theorists such as John Warfield, who sought to incorporate Sir Jeffrey Vickers’ notions of human factors into systems engineering. There was also Rudolph Starkermann, who redefined the model of multiloop servomechanisms in terms of conscious and unconscious human foibles to explain how groups of humans inevitably evolve themselves while affecting others. In a 1979 article titled “The Future of Operational Research is Past,” Ackoff stated his opinion that “American Operations Research is dead even though it has yet to be buried” (p. 93). His prediction would appear to have been premature given that the U.S. Department of Labor reported over 60,000 people employed as operations research analysts in 2009 (http://www.bls.gov/oes/current/ oes152031.htm). Similarly, what Hybertson (2009) refers to as TSE has often been called “hard systems” thinking or practice by most of the systems scientists noted above. That general approach, though, has remained dominant in terms of its prevalence in systems engineering practice. While the rationale for change has been present for decades, it has yet to have any dramatic impact in the field. Or as described by Sillitto (2011): There has long been a divide between systems practitioners concerned with “hard” systems – often involving software and complex technologies – and “soft systems”, concerned with social systems and human understanding of systems and human response to complex situations. Both sets of practice seek an underpinning theory or science of systems. However the relationship of systems science to systems thinking and systems engineering is uncertain, or at least not widely agreed (p. 2) A current definition of systems engineering is provided on the INCOSE website as:

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…an interdisciplinary approach and means to enable the realization of successful systems. It focuses on defining customer needs and required functionality early in the development cycle, documenting requirements, then proceeding with design synthesis and system validation while considering the complete problem… Systems Engineering integrates all the disciplines and specialty groups into a team effort forming a structured development process that proceeds from concept to production to operation. Systems Engineering considers both the business and the technical needs of all customers with the goal of providing a quality product that meets the user needs (http://www.incose.org/practice/whatissystemseng.aspx). The concepts of both systems and systems engineering are further refined by the INCOSE Fellows: A system is a construct or collection of different elements that together produce results not obtainable by the elements alone. The elements, or parts, can include people, hardware, software, facilities, policies, and documents; that is, all things required to produce systemslevel results. The results include system level qualities, properties, characteristics, functions, behavior and performance. The value added by the system as a whole, beyond that contributed independently by the parts, is primarily created by the relationship among the parts; that is, how they are interconnected. Systems Engineering is an engineering discipline whose responsibility is creating and executing an interdisciplinary process to ensure that the customer and stakeholder's needs are satisfied in a high quality, trustworthy, cost efficient and schedule compliant manner throughout a system's entire life cycle. This process is usually comprised of the following seven tasks: State the problem, Investigate alternatives, Model the system, Integrate, Launch the system, Assess performance, and Re-evaluate. These functions can be summarized with the acronym SIMILAR: State, Investigate, Model, Integrate, Launch, Assess and Re-evaluate…It is important to note that the Systems Engineering Process is not sequential. The functions are performed in a parallel and iterative manner (http://www.incose.org/practice/fellowsconsensus.aspx). Meanwhile a major shift was taking place in engineering in general and in software engineering in particular. It featured an alternative way of looking at a problem and articulating a solution. This was the shift from the functional view (and LaPlace transforms) to the time based, state-space view. It was prompted by the notion of frames in the Artificial Intelligence field and by the Simula 1968 method of modeling. It was fueled by the interest in designing software as data flow machines and by the cybernetic need to cope with differential game theory, the minimum entropy problem, and the finite horizon control problem, among others (Stoorvogel 1992). State-space approaches to systems evolved in popularity during the next three decades. Perhaps the state-space approach to systemics will be key to the next era of systems engineering innovation and productivity. Similarly, systems engineering personnel began to encounter customer situations that changed faster than intervention systems could be devised. The systems engineering methods now called TSE (Hybertson, 2009) presumed the problem definition was a clear, persistent target. TSE education presumed state-determined systems with linear relationships among inputs and outputs. However, other customer situations exhibited stochastic behavior, continually changing but at least within 5

known or knowable bounds, i.e., ergodic. In response, systems scientists devised Monte Carlo and Bayesian techniques to describe and predict such systems. Then system engineering personnel put these technologies to work by devising rule-based adaptive loops in the systems they designed. However, a third kind of problematic situation was always lurking, non-deterministic situations. Noticed in India 2500 years ago and exemplified by the game of chess, non-deterministic systems have now come to the fore in societal demands on systems engineering. Today, autonomous systems are proliferating, demonstrating that we know how to create them. However, our ability to qualify them as fit for purpose and to guarantee safety of operation is not yet sufficient. It is important to note as well that all systems of more than trivial scope produce information, products and services. Classifying a system as an information system or a product system or a services system may mask its wholeness or worse, preclude ensuring it wholeness as fit for purpose.

3.0 Challenges for SE Since its early development, the challenges presented to SE have expanded significantly. Even describing the differences, though, has proven to be difficult. For some SE professionals, the challenges have to do with the size and scope of projects, and the abilities to meet requirements of time and budget. For others, it is the dynamic nature of the systems involved. Many also cite the human aspects which are inherently involved in projects aimed at systems of communication, energy, healthcare, transportation, and so on. The primary area of change is the expansion to qualitatively more complex systems. For example, building a power plant is a complex engineering activity involving both technical and social elements but the behavior of a power grid across a region is substantially more complex and less predictable. The SE community is not only beginning to expand from power plants to power grids, but is also beginning to tackle problems even further beyond its traditional horizons. Representative problems include avoiding disease pandemics, creating a more viable healthcare system model, anticipating the effects of alternative energy policies, improving the effectiveness of the criminal justice system, and preventing or dealing with insurgency and terrorism (Hybertson, 2009, p. 1) The challenges now facing SE are described by Hybertson (2009) in this way: The field of systems engineering (SE) has achieved a degree of maturity but is currently experiencing a significant expansion of scope beyond its comfort zone. The expansion is predominantly in the area of complex systems (CS); that is, systems that tend to include people or other autonomous agents, cross organization boundaries, change continually, and be less predictable, less deterministic, more chaotic, less centrally controlled, and more self-organizing and adaptive than is true of what we consider traditional systems… Most systems that SE will engineer in the future are combinations of machines and people, not strictly one or the other. In addition, many of them exist at multiple scales, ranging from the nano-level to the macro 6

system-of-systems (SoS) level… Indeed, there is not yet a general understanding of what engineering a CS means, or a consensus that it is even possible (p. xix). Hipel, Jamshidi, Tien, and White (2007) list a host of “application domains” which represent pressing problems for systems engineering, including “human factors, sensors and robotics, marketing systems, homeland security, health systems, and medical mechatronics” (p. 726). They arrive, though, at proposed four domains which they believe would dominate development in systems, man, and cybernetic (SMC) efforts for the coming decade: (1) service systems, (2) infrastructure and transportation systems, (3) environmental and energy systems, and (4) defense and space systems. As they note, each of these domains is becoming more complex, and more human-centered.

4.0 What Kind of Change is Needed? A critical question is the magnitude of difference which a change makes in practice. A growing consensus seems to be that addressing many of the current challenges will require more than basic adjustments to existing models or processes. The traditional starting point of beginning projects with a clear set of requirements, for instance, is not adequate to meet the complexity of the environments with which many systems engineers are faced. It is not just a matter of mission or scope “creep,” or the indecision of customers or stakeholders. In dynamic environments, change is a constant element. In the past, systems were designed simply to withstand such changes, e.g. to maintain their structures and functioning under the pressures of temperature change or forces of weather, etc. In more fluid environments systems and environments affect each other, like an artificial organ being transplanted into a body. In truth, though, most systems have been more dynamic than they have been seen to be. While roads and bridges, for instance, are relatively static structures, as part of transportation systems within even greater urban systems, their implementation creates changes which affect their adequacy. (The common parlance has been that such projects were outdated before they were completed.) Domain Variety It is useful to consider the application domains proposed by Hipel, et al. (2007) as examples. Interestingly, at least three of the four (service systems, infrastructure and transportation, and environmental and energy) have all been targets of work by IBM, which has significant investment in SE. Service science Hipel, et al. (2007) cite what are becoming familiar statistics about the growth of service as a percentage of the economies of the world. As they note, “services employment in the U.S. is at 82.1%, while the remaining four economic sectors (i.e., manufacturing, agriculture, construction, and mining), which together can be considered to be the ‘goods’ sector, employ the remaining 17.9%” (p. 726). Even defining a distinct realm of service is no small task, though. Economics divides sectors between

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goods and services, as noted above. There are service industries, which may involve the production and use of physical infrastructure, but whose primary output is not goods, per se. In labor, the distinction has typically been manufacturing versus service jobs. Politically, manufacturing has been equated with higher-paying, unionized work including rich benefits packages, and services with low-paying, low-skilled jobs, such as fast-food restaurants or call centers. Hipel, et al. (2007) use the definition of service sector from a 1987 article by Quinn, Baruch, and Paquette, as “all economic activities whose output is not a physical product or construction, is generally consumed at the time it is produced, and provides added value in forms…that are essentially intangible” (p. 727). As proposed by Vargo and Lusch (2004), though, and as incorporated by Spohrer and Maglio (2006), and others at IBM, the shift to service systems is potentially much more dramatic than an increase in the service sector. Vargo and Lusch suggest service-dominant logic as an alternative to the goods-dominant logic which has pervaded the industrial era. According to them, there is no inherent value to a product, per se. The value is only in what is experienced by the customer or user. Moreover, value is always “co-created” between the parties, rather than delivered from one to the other. Effectively, all economic exchange is based on the creation of value, and products are only one form of service. Hipel, et al. (2007) draw heavily on other works Tien and various co-authors in their description of service. Tien and Berg (2003) introduce the concept of co-production in service, as well as emphasizing the fact that many technology jobs are knowledge-intensive and at the high end of the pay scale. As Hipel, et al. explain, “while traditional services – like traditional manufacturing – are based on economies of scale and standardized approach, electronic services – like electronic manufacturing – emphasize economies of expertise or knowledge and an adaptive approach” (p. 728). Hipel, et al. (2007) point out similarities and interdependencies between services and manufacturing, and innovations in manufacturing which they believe could be applied to services. They suggest that processes such as total quality management, six sigma, quality circles, just-in-time manufacturing, flexible or agile manufacturing, and so on, could “be recast in service-related terms” (p. 728). They also note that the shift from mass production to mass customization seems to create more overlap between service and manufacturing, since mass customization virtually requires some aspect of coproduction. Hipel, et al. (2007) identify specific differences between services and manufacturing as well, including the idea that manufactured goods tend to deteriorate with time while assets that come from service are often reusable, and may actually increase in value with reuse through interactions between the coproducing parties. Drawing from Tien and Cahn (1981), it is suggested that service processes tend to be more subjective than those in manufacturing, and service performance and satisfaction might be best addressed through the management of expectations. IBM’s formal entry into the development of service science seems to have begun in May of 2004, with a two-day summit of over 100 university professors, IBM scientists and consultants. The incentive for the

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meeting came in part from the recognition that computing power and connectivity had moved to plane, where it was able not just to reduce costs or improve productivity, but become a “full-fledged partner in the remaking of companies” (IBM Research, n.d., p. 5). More specifically, The development of new business models, processes, strategies and workforce management methods can itself be viewed as a series of services. Hence IBM describes this new area collectively as business performance transformation services (BPTS), and views it as at least a $500 billion dollar potential market -- in effect, a chance to use technology to wrest greater efficiencies from the world’s economic structure (IBM Research, n.d., p. 7). By 2005, IBM’s initiative had been expanded to Service Science, Management, and Engineering (SSME) – later also to include Design (SSMED). In a 2005 keynote address to the International Society for the Systems Sciences (ISSS), Spohrer explicitly noted his view of a necessary connection between SSME and systems science. The shift towards service inside IBM had apparently been taking place for some time. According to Spohrer and Maglio (2006) “in the last 20 years the proportion of revenue from services has grown dramatically --- in 2007, of $99B in total revenue, $54B came from services” (p. 3). By December of 2008, IBM also announced that it was selling its PC manufacturing operations to Lenovo, leaving the greatest part of its workforce in consulting services. One of the challenges was simply that IBM’s research sites were staffed with engineers who developed computer hardware and software. There was no specific capacity for conducting research into service, where the greatest part of the economy had long been. From this came more initiatives, one of which reflected back to work in the 1960s. At that time, IBM needed employees for the burgeoning fields of computer development. The foundations had been laid by scientists with backgrounds in mathematics, engineering, physics, and so on. There was no specialty in computers, though, so IBM encouraged and aided the development of the field of computer science, in cooperation with universities around the world. A similar process took place in relation to systems science. According to a 2009 press release (http:/ /www-03.ibm.com/press/us/en/pressrelease/27201.wss), IBM was “collaborating with more than 250 universities in 50 countries that are offering courses or degree programs in Service Science, Management and Engineering (SSME).” IBM was also instrumental in helping found the Service Science and Innovation Institute (SRII), a consortium of major hardware and software corporations, universities, and professional associations (including IEEE). Hipel, et al. (2007) cite Tien and Berg (2003) in a “call for viewing services as an SoS [system of systems] that require integration with other systems and processes” (p. 727). In 2010, IBM published a report summarizing its view of the global system of systems, and noting challenges in which it saw opportunities for addressing dramatic inefficiencies (see http://www.ibm.com/ibm/files/

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Y067208R89372O94/11The_worlds_4_trillion_dollar_challenge-Executive_Report_1_3MB.pdf.) IBM’s (2010) SoS report was actually part of its Smarter Planet initiative. In the 2009 press release noted above, though (http://www-03.ibm.com/press/us/en/pressrelease/27201.wss), Smarter Planet was mentioned in concert with the SSME university relationships. As the press release states it: As the world becomes more instrumented, interconnected, and intelligent, customers are being served through complex systems that require new problem solving and communication skills in the global workforce. SSME educates people about these complex systems and networks that serve customers better, creating what IBM calls a smarter planet -- building a smarter healthcare system, smart grids and smarter cities. Beyond teaching technology and businesses courses with factory floor examples, SSME programs focus on many complex service systems, such as healthcare and transportation networks (para. 2) Infrastructure and transportation systems According to Hipel, et al. (2007): The U.S. spends roughly 45% of the cost of transportation on the movement of goods. Roughly $1 trillion or 9% of the nation’s GDP is generated by the freight or logistics industry… Approximately 27% of the nation’s GDP is created by the movement of goods across the nation’s international borders (pp. 729-730). (The infrastructure they note is limited to the physical and information systems which support transportation.) Two of the major systems noted in IBM’s (2010) SoS report are also infrastructure and transportation. In that report, infrastructure accounts for $22.54 trillion of global GDP, and transportation for $6.95 trillion. The report estimates that in the U.S. alone, 2.3 billion barrels of oil are wasted each year on unnecessary street traffic, and that “one quarter of the electricity generated each year is never consumed” (p. 4.) The report also appropriately notes the complexity of solving global, interdependent problems. It is not simply a matter of applying traditional principles of efficiency. As the report puts it, “These systems are part of a larger, interconnected system. And because of the high degree of interdependence among systems, this inefficiency lies not only within individual systems, but also within their interrelationships” (p. 5). Environmental and energy systems As succinctly stated by Hipel, et al. (2007), “All societal systems…are completely reliant upon access to energy systems, without which they would cease to operate or exist” (p. 732). In addition to the inefficiencies in the use of resources already noted, though, every societal system also produces unwanted by-products and waste. Learning to design efficient, effective, and sustainable large-scale

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systems, accounting for both human and ecological factors, presents enormous challenges to systems engineers. IBM’s (2010) report estimated a total of $15 trillion per year in wasted or lost resources around the world. For example, “More than 50 percent of the world’s food supply never makes it to consumers,” and, “nearly 35 percent of all the water used each year is frivoled away by poor agricultural water management” (p. 1). The report points out that the root problem is not a lack or failure in technology, but most importantly an issue of mindset by decision-makers. Large-scale problems tend to be presented and approached as sets of contradictions. We can either have jobs, or we can have a clean, natural environment – but not both. Competition can fuel economic growth, leaving some better off than others, or there can be equity. Progress will bring risk, or there will be stability and stagnation. According to scientific estimates, Earth is about 4.5 billion years old, and the first living cells appeared around 3.8 billion years ago. Since that time, living entities have managed to evolve, and to maintain a sustainable balance which allowed life to continue. The real anomaly would be if humans “progressed” to the point of stopping that process. Traditional, mechanistic models and approaches have taken us in narrow direction, which seems obviously not to be sustainable. It is this domain of environmental and energy systems in which the need for new approaches may be most obvious and imminent. (Anecdotally, based on contact with systems engineering students at meetings and conferences, it appears that environmental challenges are being included now as a regular part of their curricula. That would seem to be a hopeful sign with respect to future development.) Defense and space systems Given the membership of INCOSE, this may be the most important domain for audiences of this paper. Changes in the nature of human conflicts mirror many of the advances in technology. Increases in international travel and global communications have brought previously separate cultures into direct contact, and often competition. Access to, and control of, resources of all kinds are now a matter of constant global trade, be those food, water, energy sources, minerals, finished products, and so on. Terrorism by one ethnic or religious entity on another has almost supplanted traditional wars between recognized states. Attacks on information systems are now as threatening as physical attacks by bombs and missiles, in terms of potential damage. Advances in space exploration often paralleled defense research. While there have been important contributions to civilian technology, much of the emphasis by governments who supported the programs was at least equally in the development of weapons or defense technologies. As noted by Hipel, et al. (2007), military campaigns of the last century often followed “life cycle” plans, including not just military success, but also the rebuilding and stabilizing of social, political, and

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economic systems. (There have been many noted exceptions, as well.) According a report for the National Research Council (Zacharias, MacMillan, & Van Hemel, 2008), it would seem that changes in the needs for military systems are likely to be as dramatic as any in the civilian realm. As the report explains: The Air Force and the other military services are increasingly interested in using models of the behavior of humans, as individuals and in groups of various kinds and sizes, to support the development of doctrine, strategies, and tactics for dealing with state and nonstate adversaries, for use in analysis of the current political and military situation, for planning future operations, for training and mission rehearsal, and even for the acquisition of new systems (p. 1). As the report further elaborates: Today’s military missions have shifted away from force-on-force warfare – fighting nationstates using conventional weapons – towards combating insurgents and terrorist networks in a battlespace in which the attitudes and behaviors of civilian noncombatants may be the primary effects of military actions. These new missions call for agile, indigenously sensitive forces capable of switching quickly and effectively from conventional combat to humanitarian assistance and able to defuse tense situations without, if possible, the use of force. IOS [individual, organizational, and societal] models are greatly needed for planning, supporting, and training for these forces and for evaluating the technology with which they fight. Models of human behavior in social units – teams, organizations, cultural and ethnic groups, and societies – are needed to understand, predict, and influence the behavior of these social units (p. 2) Discipline Variety The discipline-oriented challenges contrast with the domain-oriented challenges described above. Current problematic situations from which society wants relief are of a cybernetic order that will not be suppressed by intervention systems that are devised by current thinking and practices. Warfield (2002) warns that a key challenge for systems engineering occurs when problematic situations become so large and complex as to cause cognitive overload which inevitably results in underconceptualization of the suppression system, resulting in massive unintended consequences. In other words, the engineering challenge shifts from a) engineering --- cogently selecting and organizing technologies that solve a defined problem to b) sufficing --- responsively arranging and rearranging the competencies and practices of the systems engineering cadre as needed. Warfield (2002) recommends the Work Program of Complexity and provides a System Complexity Index metric that indicates the scope and depth of the system challenge at any stage of systems engineering activity. Also, Friedman (2005) advises that the main output of SE is a model of the intended system and that the efficacy of the model is a key issue. Ring (2009b) describes eight categories of systems and recommends the viewpoint that problematic

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situations come in three kinds, state-determined, stochastic and non-deterministic. As noted previously, TSE is tuned to cope with state-determined systems. Several practitioners in ISSS and INCOSE are dealing with stochastic systems. The principles, methods, techniques and tools for dealing with non-deterministic systems (Ring 2004) are being prototyped and evaluated. This poses a significant collaboration and co-learning agenda for system science and system engineering practitioners. 5.0 The Interdependencies of Systems Engineering and Systems Science This paper has thus far focused on a number of commonalities between systems engineering and systems science. But do these different professions really share any interdependence, i.e. do they need each other? C. P. Snow was a British scientist-turned-writer. In 1959 he published a small monograph titled “The two cultures and the scientific revolution,” which described the division between scientists and literary intellectuals of his time. It was roughly equivalent to what remains as the separation in universities between the sciences and the humanities. As Snow explained, though, it was not just a difference between professions or university departments. It represented a fundamental difference in orientation between people. One of the simplest examples Snow (1959) used was in questions he would ask each side. For scientists, he tended to probe about literature they had read (e.g. Dickens or Shakespeare). The responses were almost universally “no,” or only reports of vague, past attempts. The literary intellectuals were asked for a brief explanation of The Second Law of Thermodynamics, or even a basic meaning of mass or acceleration, which ended just as vaguely or defensively. Snow’s conclusion was that these differences in orientation created not only difficulties in communication, but also deep emotional conflicts and distrust, making collaboration often impossible. His recommended solution was in the reform of education – which has gone unheeded for the last halfcentury. In current terms, this divide is probably best described by students’ proclivity to mathematics. Either students understand and are drawn to mathematical forms of representation, or they tend to get past whatever requirements are imposed upon them however they can. Schön (1983) traces the emphasis on the scientific perspective to eighteenth century writers such as Bacon and Hobbes, which formed the basis for Positivism in the nineteenth century. Further: The professions had come to be seen as vehicles for the application of the new sciences to the achievement of human progress. The engineers, closely tied to the development of industrial technology, became a model of technical practice for the other professions (p. 31). As universities began to flourish in the US in the late nineteenth and early twentieth centuries, they were heavily influenced by the tradition of the German universities as multidisciplinary research

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institutions. According to Schön (1983), Johns Hopkins University epitomized this tradition, and became the model around which other US universities were built. This framework created a distinction between higher schools of science, and lower schools of professional practice. The relationship was one in which the professions were expected to provide practical problems to the universities, who were in turn to provide the professions with new scientific knowledge which could be applied in practice. This divide was not maintained, of course, but the price for the professions was the need to adopt a Positivist epistemology in order to be included in the university. It obviously did not mend the divide between research and practice, either. The influence of Positivism on engineering and other professions created what Schön (1983) refers to as Technical Rationality, which takes us back again to World War II. As Schön explains, that war caused technologists to rely on science as never before. Following the war, the US government began a massive increase in funding for scientific research, with the belief that scientific progress would create societal good. Ultimately, though, this created a crisis of legitimacy. Scientific research alone was not able to live up to the expectations that it had set. Technical Rationality created a professional perspective focused on problem solving. What this misses, Schön (1983) calls problem setting. As he explains: [Professionals] are coming to recognize that although problem setting is a necessary condition for technical problem solving, it is not itself a technical problem. When we set the problem, we select what we will treat as the “things” of the situation, we set the boundaries of our attention to it, and we impose upon it a coherence which allows us to say what is wrong and in what directions the situation needs to be changed. Problem setting is a process in which, interactively, we name the things to which we will attend and frame the context in which we will attend to them (p. 40) The dilemma which all of this leaves for practitioners is what Schön (1983) describes as rigor or relevance. They may choose the arena of clear, research-based theory, which would include the use of mathematical spaces and perfectly coherent models, or they may work in the swampy lowlands where the messes of the real world, and most people, exist.

Lawson (2011) explains some of the distinctions between science and engineering: In Science the major task is to examine behaviors and to explain these behaviors by identifying fundamental structures that cause the behavior. The expression of the structures can be in the form of notation (mathematical, chemical, etc.), models or even natural language text. In Engineering the goal is to identify fundamental structures (building blocks) that can be, via design and development, integrated into a system that when instantiated and operated delivers desired behaviors (p. 3)

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In Figure 2, he graphically describes the relationship.

Figure 2. Ways in which scientific and engineering disciplines relate to the fundamental properties of structure and behavior. What Lawson (2011) describes is a critical connection between science and engineering – between ways of thinking or understanding, and acting. As noted above, these got separated historically in our educational systems. In practice, they are intimately interconnected. The connection between understanding and action may also represent an awareness of the need for both rigor and relevance. Action should be informed by understanding, and ideally this should occur in a pattern of ongoing learning, as Lawson’s diagram shows in Figure 2. This presents implications for both education and practice. Systems education has been the topic of meetings, conferences, debates, and conversations for decades. By the 1960s, there was a sense that systems science was firmly entrenched in universities. Its demise came from the fact that it did not fall under a traditional school or discipline, and therefore had no base of power from which to draw resources within the university systems. It also did not fit the paradigm of research which garnered funding from private or government institutions (at least not in the US), and therefore did not fare well politically, either.

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With respect to education in engineering, there have been discussions for some years now about the need for “T-shaped” workers; those with depth in a particular area of study, but also breadth of understanding across other domains. Kijima (2007) proposed the development of a “new liberal arts” in order to foster that breadth of development. A paper by Niewoehner (2006) proposes another consideration. Drawing on work by Paul (2004) regarding critical thinking in higher education, Niewoehner proposes that critical thinking is a necessary but missing element in engineering education. Using Paul’s framework he proposes intellectual standards which are to be applied to elements of thought in order to develop specific intellectual traits / virtues (i.e. Intellectual Humility, Intellectual Perseverance, Intellectual Autonomy, Confidence in Reason, Intellectual Integrity, Intellectual Empathy, Intellectual Courage and Fairmindedness). The emphasis of much of Paul’s (2004) work is that even though university faculties purport to value critical thinking, and students learning how to learn, very few can articulate anything about critical thinking as a concept. The connection for this paper is the potential that critical thinking might be a skill which could help both systems scientists and systems engineers in problem solving and problem setting, as Schön (1983) would state it. The challenges outlined by Niewoehner (2006) are not unlike those on which John Warfield worked for decades. Warfield (2006) summarized his list of bad practices to be overcome, in his final book: • • • • • • • • •

Underscoping the systems domains Unimaginative workspaces Mismatched media Linguistic pollution Premature quantification Insensitivity to discovered behavioral pathologies Inadequacy of comparisons of alternatives Blindness to history Monotonous bifurcation

Warfield (2006) spent a great deal of his life working on solutions to these kinds of problems. In the end, though, the problems were fundamentally ones of human limitations. (While technology might be useful, they are not technological problems.) Changes in educational systems, and the outcomes of potential changes, are both long-term prospects. The more pressing question may be what can be done in practice for both systems engineers and systems scientists in the near-term. From thinking to acting The reality appears to be that science and engineering are already converging in new ways in practice,

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and at a rapid pace. Systems biology, for example, is an emerging field. As described on the website of the Institute for Systems Biology (http://www.systemsbiology.org/Intro_to_Systems_Biology): Systems biology is the science of discovering, modeling, understanding and ultimately engineering at the molecular level the dynamic relationships between the biological molecules that define living organisms. (Leroy Hood, Ph.D., M.D., President, Institute for Systems Biology) Traditionally, engineering focused on projects in which design was preceded by requirements. Success was determined by the degree to which outcomes met the given specifications. The Hoover Dam, for instance, was a massive undertaking in terms of design and construction, and “has been rated by the American Society of Civil Engineers as one of America's Seven Modern Civil Engineering Wonders” (http://www.usbr.gov/lc/hooverdam/History/storymain.html). The Three Gorges Dam in China, while also a massive achievement in engineering and construction, proved to be much more controversial – not because of the engineering per se, but because of many other implications involved. Building the dam as it was designed, for instance, initially displaced 1.3 million people from their homes, and covered archaeological and historical sites. While it may well be argued that these are not engineering issues, it then raises questions about the boundaries for engineering practice. As the scope and impact of projects increase, so do the implications for unforeseen outcomes and questions about decision-making. As noted earlier, systems engineering students seem to be regularly presented now with projects involving environmental systems. Practices such as biological engineering, whether for genetically modified crops, animal cloning, or possibly systems biology applications, raise tremendous ethical questions, well beyond the concerns noted above. Lawson (2011) has suggested a model for bringing together aspects of science and engineering. He has done so by combing two existing models, the OODA (orient, observe, decide and act) with the PDCA (plan, do, check, act), as shown in Figure 3.

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Figure 3. OODA and PDCA Loops. As Lawson explains, the OODA loop …is directly in line with the principles of systems thinking (science side) and in enterprises is typically executed continuously in leadership and management functions. Certainly all scientists consciously or implicitly utilize this loop in establishing what is to be studied based upon situations (natural, man-made or mixed) that they encounter and the need for understanding (p. 8). The PDCA loop came initially from Statistical Quality Control, and was popularized by W. Edwards Deming as one of the principles for Total Quality Management. As Lawson elaborates: There is a strong relationship between the PDCA activities presented here and the engineering treatment of structures and behaviors portrayed in [Figure 2], especially when the project has the goal of designing and developing new structures, or altering existing structures. In general, the PDCA thinking is applied to all projects, for example, projects related to conceptualization, development, production, maintenance, or retirement of systems. The OODA is always continuous, while the PDCA loop is usually discrete, that is, tied to specific goals and time frames. When coupled, the loops provide a process for learning and acting.

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Changing the nature of practice, especially for long-tenured professionals working to meet daily demands, is an immense challenge. At the very least, a starting point may be moving away from the assumption that we can isolate, define, measure, and affect factors in dynamic world, without having to consider the context. The question can no longer just be, “does it work?” without considering “how does it fit?” 6.0 Initial Conclusions Theories and practice tend to evolve with the challenges they attempt to address. They also reflect the prevailing mindsets of professions, institutions, and leading authorities of their time. In some ways they represent the greatest advancements that have been made; in others, they mirror the practicality of efficiently and/or effectively accomplishing what needs to be done. Organizations of all sizes and types are confronted with the need to adapt as the environments within which they exist evolve. While it may be apparent that changes are occurring, though, their direction and magnitude are rarely clear or linear. Despite the data and tools available, new directions are often a judgment call by those in charge. Those vested in systems engineering are at a cusp brought on by three drivers. First, the impending retirement of their ‘baby boom’ cadre at a rate far higher than the replacement rate demonstrated by colleges and commercial services. Second, the need to deal with whole systems, also called system of systems, composed of heterogeneous, ambiguous components. Third, the need to estimate “fit for purpose” of a large, heterogeneous system throughout its life cycle. Regarding the whole systems challenge, a study initiated as a panel at INCOSE IS2007 and reported in Ring (2009) indicates that system engineering personnel must increase their productivity and innovation ten-fold by approximately 2015. It is likely that a key enabler will be a better understanding of systemics through a dialogue with system scientists. The clear implication is that SE, the activity, must be seen as a system, a human activity system though augmented by symbol manipulation methods and tools, and as a nondeterministic system. Another implication of these three drivers may be that the activity often referred to as SE may be viewed as two distinctly different activities. One serves to discover and precis' the problematic situation and underlying problem system while the other serves to devise and qualify a problem suppression system. The latter is currently called engineering of systems by many academic institutions and is clearly a branch of engineering. The former may not be as much a branch of engineering as a branch of social science/semiotics. At the level of a profession, the drive for stability is often far greater than at that of an individual organization, and the implications of change larger. Occasionally, new ideas or technological advances create new possibilities. More often, change is preceded by an inability to effectively solve new problems. In some cases, it is a catastrophic failure which drives change. In the absence of change, both organizations and professions often just fade into obscurity, and then disappear.

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