Systemic Practice and Action Research, Vol. 12, No. 3, 1999
A Note on the Correspondence Between Complexity and Systems Theory Steven E. Phelan1 Received December 10, 1998 Complexity theory shares a common vocabulary with systems theory. Terms such as emergence, complexity, and adaptation appear in both traditions. Despite the similarities, complexity theory is not a misnomer for systems theory. Several points of departure exist in complexity's research agenda and methods. Moreover, while systems theory appears to have embraced interpretivist and critical philosophies, complexity theory remains firmly in the positivist camp, despite claims that it is a postmodern science. KEY WORDS: chaos; complexity; systems theory; positivism; realism.
1. INTRODUCTION To the adherents of the theories of chaos and complexity (hereafter "complexity theory"), the themes of emergence, nonlinear dynamics, and complexity are novel concepts closely associated with the work of the Santa Fe Institute. For systems theorists, many of the central concepts in complexity theory are simply old wine in new bottles, with an intellectual heritage that can be traced back to the pioneering work of von Bertalanffy, Ashby, and Boulding. Indeed, the wellknown phrase, "... The whole is greater than the sum of the parts," finds its earliest expression in Aristotle. As a complexity scientist, I was both surprised and embarrassed to find such an extensive body of literature virtually unacknowledged in the complexity literature. A common terminology suggests a high degree of commensurability between the two theories. However, on closer examination, although they share a common worldview, the two theories differ markedly in their research agenda and methodologies. An examination of the similarities and differences between the two theories occupies the first half of this paper. 1
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More recently, systems theory has been strongly influenced by social theories that problematize the neutral role of the observer in traditional systems theory. The notion that complexity theory provides support for a critical or interpretivist view of systems is examined in the second half of the paper. It is argued that complexity theory is strongly positivist and, as such, provides little to offer alternative worldviews despite evidence that complexity theory is often misconstrued to support these positions. The paper concludes with an examination of the possibility of a rapprochement between systems theorists and complexity theorists. 2. SYSTEMS THEORY AND COMPLEXITY A casual comparison of systems theory and complexity theory cannot fail to see a degree of conceptual overlap. Several terms carry virtually the same definition in both theories, including system, emergence, dynamic, nonlinear, adaptive, and hierarchy. Both theories also share a belief that there are universal principles underlying the behavior of all systems. This has led some commentators to suggest that the two fields are substantially similar, with complexity theory being a derivative (and possibly more advanced) version of systems theory (Zwick, 1997). There are, however, subtle points of departure. The first point of departure lies in the emphasis that systems theory gives to prediction and control and to "problem-solving" in general (Flood, 1990). In systems theory, the purpose of an analysis is to "improve" the system. Cohen and Cyert (1961, p. 317) argue that confirmatory analysis "... aims at understanding the operating characteristics of a total system when the behavior of the component parts is known with a high degree of accuracy." Exploratory modeling, on the other hand, aims "... to derive a set of component relations that will lead to a total system exhibiting the observed characteristics of behavior" (Cohen and Cyert, 1961, p. 317). Systems theory is predominantly focused on confirmatory analysis. It seeks to identify relationships between elements in a system and then to optimize some objective function. This approach is most obvious in the hard system methodologies used in operations research, engineering, and management science. Deciding how to optimize the layout of a factory to increase throughput is one example of this means-ends thinking. A problem-solving perspective is also present in much of the work in system dynamics and cybernetics. Cybernetic models, for instance, have been used to improve the efficiency of engines and the performance of gun predictors. However, the point is not limited to hard systems methodologies. Both soft system methodologies (Checkland, 1995) and critical systems thinking (Flood, 1990) are focused on improvement and "problem-solving." In the former case, the goal is to surface divergent models of the system in order to generate shared
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understanding and consensus on required actions to improve the system. The latter approach seeks to "emancipate" people from sources of domination in order to facilitate problem solving by placing all participants on a fair and equal footing. The emphasis on confirmatory analysis in systems theory contrasts markedly with the exploratory nature of work in complexity theory. Researchers in complexity theory typically start with a complex system, such as an economy, and proceed to posit relationships between agents or elements in the system that, in interaction, may explain the system's aggregate behavior. One of the basic premises of complexity theory is that much of the apparently complex aggregate behavior in any system arises from the relatively simple and localized activities of its agents. Systems theory, on the other hand, defines complexity as arising from a high number of parts (agents) and interactions (Yates, 1978). Complexity in complexity theory then is something of an illusion—it is something that emerges when several agents follow simple rules. The most oftquoted example of this principle is Craig Reynolds' "boids" program (Waldrop, 1992). The program is able to simulate a flock of birds on a computer screen. The simulated birds or "boids" are able to wheel in unison and avoid obstacles as if conducting highly complex, highly coordinated maneuvers. In fact, each "boid" in the simulation obeys only three simple rules: (1) maintain a minimum distance from other objects in the environment (including other boids), (2) match velocities with other boids in the neighborhood, and (3) move toward the perceived center of mass of boids in the neighborhood. The higher-level properties of the flock emerge from the interaction of agents following simple rules. Much of the research in complexity theory is involved in searching for the simple rules that purportedly explain the behavior of complex systems. In fact, the search for simple (i.e., analyzable and decomposable) interactions can be considered one of the defining characteristics of complexity research. This research is still in its infancy. It is possible that if or when these guiding rules are found, then the interest of researchers will turn toward predicting and controlling the systems of interest. It is probable that a dialectic process exists between confirmatory and exploratory analysis. Success in deriving explanations for system behavior creates the ability to use the model for prediction and failures in prediction spur further exploration (Morecroft, 1985). However, at the current time, complexity theorists are more interested in exploration and explanation than in "problem-solving." A second point of departure is the dependency of system theory on feedback (and feedforward) loops to characterize most of the relationships and interactions
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between system elements. System dynamics models, for example, are capable of generating extremely complex models (in the sense of having a large number of elements and relationships between elements). However, the building blocks or ontological categories at the disposal of the modeler are basically limited to stocks and flows, with flows representing mainly either positive or negative feedback to a given element in the model. It is something of an article of faith with systems theorists that a combination of positive or negative feedback (including self-referential behavior) is a useful way of characterizing interactions in a system. One of the weaknesses of the approach is that stocks and flows invariably refer to the quantity rather than to the quality (or any other characteristic) of an element (or its attributes). Soft systems theorists have tried to move away from the reliance on feedback mechanisms, although there is still a tendency to conceive social relationships in feedback, or circular, form (Flood and Carson, 1988). There is no doubt that one of the major drivers of complexity theory has been the widespread availability of artificial intelligence methods, such as neural nets and genetic algorithms (Goldberg, 1989; Holland et al., 1986). These techniques have enabled researchers to populate simulated worlds with multiple intelligent and idiosyncratic agents (McKelvey, 1997). Agents are idiosyncratic because they learn from their own localized experiences. Each agent thus evolves as a unique individual entity. Complexity theorists get excited when this diversity creates aggregate behavior that cannot be explained when agents are treated as homogenous entities. Vriend (1994), for instance, was able to build an economy that matched supply with demand while maintaining (as in the real world) a wide range of firm sizes. Traditional economic models had always assumed that firms were homogeneous in equilibrium and were not able to explain observed variations in firm size. Each of Vriend's firms started with the same endowment and learning strategy but a combination of chance conditions exploited by rapid learning led some firms to become more successful than others over time. The use of artificial intelligence (AI) substitutes symbolic reasoning for the numerical reasoning typically found in system dynamics (Drogoul and Ferber, 1994). Instead of differential equations linking (quantitative) inputs to (quantitative) outputs, AI techniques allow virtually any property of the system to be represented symbolically (Newell and Simon, 1972). Thus, agents in complexity theory are not limited to simply reacting in predetermined ways to inputs or environmental stimuli like elements in a systems model. Intelligent agents (with the right programming) can learn, make inferences, and plan. It would be very unusual to find a systems model of an aggregate economy being modeled at the level of the individual household and firm. More likely, high prices would stimulate production (in the aggregate) and suppress demand (in the aggregate). It is not difficult to use feedback loops to show that the econ-
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omy will settle into an equilibrium where supply equals demand. The key distinction is that a systems approach would find it difficult (a) to model diversity at the level of each household and firm and (b) to have difficulty getting inside the black box to explain the how production and consumption decisions were made. The use of artificial adaptive agents enables these issues to be studied in depth (Holland and Miller, 1991; Phelan, 1997). Of course, it is feasible that systems theorists would have incorporated the use of idiosyncratic agents into their models had the technology been available when the theory was developed. Quantitative feedback represented the practical limits of the technology in the 1950s and 1960s. It may be possible that systems theorists are not tightly committed to the older methodology and might actually welcome that extra flexibility gained by adopting agent-based modeling. In summary, we have noted at least three points of departure between complexity and systems theory. Complexity theory tends (1) to focus on exploratory analysis, (2) to use agent-based modeling, and (3) to maintain that complexity arises from the interaction of agents following simple rules. Systems theory favors confirmatory analysis or "problem-solving" using feedback (and feedforward)based models. For systems theorists, complexity is a function of the number of system components and the amount of interaction between them. 3. COMPLEXITY AND POSTPOSITIVE^ Systemic methods were first developed and applied in science and engineering. Systems ideas have found ready application in these disciplines, due in part to the ease in delineating the components, boundaries, and objectives of "hard" physical systems. Applying systems concepts to "soft" social systems has proved more of a problem. Analysts and participants working in soft systems often fail to reach agreement on systems definitions or on the problems to be solved (Checkland, 1972, 1981). Checkland has sought a radical explanation for these disagreements. Differences in perspectives, he argues, arise from different Weltanschauungen, or worldviews. These different worldviews arise, in turn, because we each need to make sense of our world by integrating new sensory perceptions with constructs formed from our past experiences (experiences which begin at birth). To the extent that our cumulative experiences diverge from each other, so do our interpretations of problems and events. Soft systems methodology (SSM) promotes a process that aims to encourage facilitators and system participants to learn more about the perspectives and constructs of others in the system (its clients, actors, and owners). These insights provide the possibility for shared understanding and consensual action (although the connection between consensus and change is not clearly satisfactorily explained for reasons given below). Critical systems theory (CST) draws heavily on the philosophy of Haber-
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mas in seeking to move beyond the insights of soft systems methodology. Flood (1990) has proposed that the influence of dominant ideological forces, or worldviews, can cause us to misinterpret situations, thus causing certain views to be privileged over others. This had the effect of creating a "false consciousness" among system participants. According to Flood, the oppressed need to be liberated from their oppressors by surfacing the hidden assumptions in the dominant worldview(s). Only when an 'ideal speech situation' has been created will system participants be able to freely participate in negotiating 'fairer' outcomes for all. Both SSM and CST move away from the positivist epistemology of "hard" systems science. Positivism maintains that our sense perceptions provide accurate knowledge of reality. The truth of a statement about reality can be determined only by careful observation. This admits notions of right and wrong. Concepts of progress also arise as careful observation, combined with induction and deduction (i.e., rationality), allows more of "reality" to be revealed. "Post positivist" systems theories, such as SSM and CST, contest the notion that observation reflects reality (McKelvey, 1997). At the simplest level, our biology and psychology (our hard wiring) biases our perceptions (von Foerster, 1984). Ontological reality exists but we can never know it (von Glasersfeld, 1991). The closest we can get to ontological reality is a shared agreement about experential reality, that is, a general consensus on terms that (we assume) arise from our shared perceptions with others. Thus, we can expect broad agreement on widely shared experiences—heat, light, and mass for instance. However, higher-level constructs such as truth, justice, and efficacy are more likely to be the products of heterogeneous experience because they depend on one's social context. This provides a ready explanation for Checkland's observed differences in analyzing physical and social systems. The two theories also lie at the limits of modernity. Both SSM and CST are optimistic in assuming that, even though we differ in our interpretations of ontological reality, there is still room for consensus (Tsoukas, 1992). For SSM, consensus arises from developing various conceptual models of the system of interest. For CST, consensus can arise only when an "ideal speech situation" has been created. Paradoxically, Checkland (1981) appears uncomfortable with openly advocating this position. He favors focusing on learning rather than problem-solving. He also appears to advocate that participants take action to change their system after consensus is reached but declines to provide any heuristics for recognizing when consensus has been reached or any guidelines for how to move from consensus to change. Presumably, prescriptive actions can be determined only in a specific context and cannot be generalized. One gets the feeling that consensus seeking could take a very long time. A defining characteristic of postmodernism is its rejection of consensus (Lyotard, 1983). All perspectives (particularly those developed across the bound-
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aries of time, culture, and gender) are so fundamentally incommensurate as to render reconciliation between competing worldviews impossible. As such, consensus will never, and can never, be reached. Furthermore, the unknowability of reality renders it impossible to judge the truth, value, or worth of different perspectives, rendering all perspectives equally valid (or invalid). For Foucault, realization of this situation is a special sort of liberation releasing the observer from any dependency or servitude to the worldviews of others (Brocklesby and Cummings, 1996). Recent articles in the field indicate an interest in applying postmodern theories to systems theory (Brocklesby and Cummings, 1996; Tsoukas, 1992). 3.1. Is Complexity a Postpositivist Theory? Scientists (including those at the Sante Fe Institute) view complexity as a positivist theory (Price, 1997; Sokal and Bricmont, 1998). The theory postulates that rules and equations can be discovered that are capable of explaining the observed complexity of the "real" world/universe. Furthermore, the theory maintains that these laws have the potential to predict and control the behavior of real-world systems. Thus, complexity would seem to be following a positivist research agenda. However, for many postmodernists (and postpositivists), chaos theory represents an "attack from within" on the privileged position held by science in a dominant ideology controlled by white European males. Like Einstein's relativity, Godel's theorem, and Heisenberg's uncertainty principle, chaos theory is said to delimit the boundaries of determinism and rationality, fatally wounding the cherished notion that science can predict and control all aspects of the "real" world (Gross and Levitt, 1994; Sokal and Bricmont, 1998). The authority most often cited in justifying this conclusion is Lorenz's (1963) butterfly effect. Chaotic systems show sensitivity to initial conditions such that small errors in specifying initial conditions amplify into large errors over time. Thus, a butterfly flapping its wings over the Pacific may cause a tornado in Texas. Clearly any errors in estimating parameters will lead to unacceptable forecasting error. For the postmodernists, a loss of ability to predict destroys the "clockwork" universe that Newton established. Establishing the invalidity of science apparently strengthens the validity of other ways of knowing about the world. One problem is that most commentators assume that almost any system that exhibits significant complexity (business, society, international relations, ecology) must be a chaotic system (Stacey, 1991). However, testing for chaos in high-dimensionality (many-variable) systems requires large amounts of data (thousands of observations) that are typically not available to those in the social sciences. The result is that many more systems are assumed to be chaotic than
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shown to be chaotic. Second, complexity theory assumes that most complex systems do not stay in a state of chaos for long, rather gravitating toward the edge of chaos where prediction is possible to some degree (Phelan, 1995). Third, as Mikulecky (1998) points out, the nonlinear dynamic equations used in chaos theory are accepted as an analogue of reality only because their output mimics some aspect of the real world (e.g., the weather) over a given range of parameters. For another set of parameters, the equations might yield a behavior we call the "butterfly effect." The implication is that we may never observe chaotic parameters in the real world, although the formal equations are capable of producing chaotic behavior. Finally, rather than revealing the futility of trying to predict the future, chaos theory has actually revealed a hidden level of order in seemingly random systems. The realization that many chaotic systems follow a predictable path in state space allows chaos to be controlled. For instance, the irregular beating of a human heart (a low-dimensional chaotic system) can be corrected by stimulating the heart in just the right place to throw it back into a healthy rhythm (Garfinkel et al., 1992). It would appear that the positivist project is not yet dead. 4. RAPPROCHEMENT Can the differences between the two theories be reconciled? I have argued that complexity theory differs from systems theory in its agenda (exploratory rather than confirmatory), techniques (agent-based models rather than circular flows), and epistemology (positivist rather than postpositivist). Clearly, agentbased techniques will find their way into the toolkit of systems scientists, as there is no a priori reason for system theorists to prefer circular flow models to agentbased models. Both approaches capture some of the essence of the conceptual categories of complexity and emergence. Choice of method may well come down to the relative efficacy of each method in a given problem context. The question remains whether complexity theory is just a sophisticated form of reductionism. Complexity theory starts from the assumption that much of the observed complexity in the world can be explained by relatively simple interactions among components of the system of interest. There is a reluctance to embrace radical holism, that is, to maintain that the whole can be understood only in its totality and that all interactions are important. In general, systems theory is more sympathetic to radical holism. This will be a continuing source of friction between the two theories. No doubt the confirmatory agenda of complexity theory will continue to expand. As the knowledge of complexity increases, so will the opportunities to use that knowledge to predict or control systems rather than simply seeking to understand their behavior. The work using chaos theory to control cardiac fibrillation and brain seizures is an example of the move in this direction. Nev-
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ertheless, much of the early work in complexity theory has focused on "complex adaptive systems" in biology, economics, and sociology. The self-referent nature of these systems makes it inherently more difficult (if not impossible) to identify general laws or regularities. Consequently, confirmatory insights will take longer to develop than in the physical sciences and will be more tentative and contingent when they do emerge. Ultimately, the continued popularity of "the sciences of complexity" will rest on their ability to generate a progressive research program (Lakatos and Musgrave, 1970) based, in part, on an ability to keep generating confirmatory insights. Ironically, it was the added complexity of social systems that spurred the development of postpositivist methods, such as second-order cybernetics and soft systems methodology, in systems theory. While complexity theory maintains a strongly positivistic stance, there is some evidence that a constructivist awareness may be just starting to emerge (Rocha, 1997). One of the strengths of agent-based modeling is its ability to model heterogeneous behavior among agents. It is conceivable that a model could be developed to allow agents to have different perceptions of an underlying ontological reality. These differences in perception would lead to divergent learning experiences and an inevitable variation in preferences and actions among agents. Agent based methods may thus go some way toward operationalizing the constructivist worldview. Whether this development will lead to any new insights remains to be seen. REFERENCES Brocklesby, J., and Cummings, S. (1996). Foucault plays Habermas: An alternative philosophical underpinning for critical systems thinking. J. Operat. Res. Soc. 47, 741-754. Checkland, P. (1995). Model validation in soft systems practice. Syst. Res. 12, 47-54. Checkland, P. B. (1972). Towards a systems-based methodology for real-world problem-solving. J. Syst. Eng. 3, 87-116. Checkland, P. B. (1981). Systems Thinking, Systems Practice, Wiley, Chichester. Cohen, K. J., and Cyert, R. M. (1961). Computer models in dynamic economics. Q. J. Econ. 75, 112-127. Drogoul, A., and Ferber, J. (1994). Multi-agent simulation as a tool for studying emergent processes in societies. In Gilbert N., and Doran, J. (eds.), Simulating Societies: The Computer Simulation of Social Phenomena, UCL Press, London, pp. 127-142. Flood, R. L. (1990). Liberating systems theory: Toward critical systems thinking. Hum. Relat. 43, 49-75. Flood, R. L., and Carson, E. R. (1988). Dealing with Complexity: An Introduction to the Theory and Application of Systems Science, Plenum Press, New York. Garfinkel, A., Spano, M. L., Ditto, W. L., and Weiss, J. (1992). Issues in controlling cardiac chaos. Science 257, 1230. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York. Gross, P. R., and Levitt, N. (1994). Higher Superstition: The Academic Left and Its Quarrels with Science, Johns Hopkins University Press, Baltimore.
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