Theory of Complex Adaptive Systems

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Complex adaptive systems (CAS) is the term defining complex systems that can adapt to a continuously changing environment. Such systems have certain peculiar characteristics such as the ability to self-regulation, i.e. the possibility to find the best possible ways to evolve with preserving the highly effective rates of functioning, openness that is interaction with the environment and external influences, unpredictability caused by the complexity (Grus et al, 2010, pp. 443-445). It is of significant importance to highlight that complexity in the case of a complex adaptive system does not refer to complicatedness. Instead, it means that the system constitutes a large number of elements that interrelate effectively with each other, and that is why can easily govern themselves and adapt to the changes in the environment. The only problem with the complexity is that because of it the development of the systems cannot be controlled and, so, it is unpredictable.

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The theory of complex adaptive systems has been formulated as the division of the chaos theory of the development, thus giving a rise to the complexity theory. The theory of complex adaptive systems and the complexity theory have become popular in many areas of investigation such as sociology, biology, psychology, healthcare, economics, etc. Speaking of business and economics, there are two primary assumptions here. First of all, the economy is a complex adaptive system that best functions in the states far from equilibrium, and equilibrium is not a natural state of the economy. Second, the economy being a complex adaptive system feels better in the case of self-regulation and self-organization and may suffer from a collapse if strictly controlled (Lansing, 2003, p. 193).

The development of the theory of complexity started in the middle of the preceding century. As it was mentioned, it derived from the chaos theory. As noted by Simon (1962), what leads to complexity is not chaos but the structure of the system and the interchangeability of its elements (p. 467). Moreover, he believes that complexity is characterized by the hierarchy that, to his mind, is “a system of interrelated subsystems, each of the latter being, in turn, hierarchic in structure until we reach some lowest level of elementary subsystem” (Simon, 1962, p. 468). The author explains it by the fact that hierarchies are the only form of the systems that may evolve and adapt to the changes in the environment as the smallest subsystems learn how to behave in response to changes. Together with that, Simon (1962) draws attention to near-decomposability and comprehensibility of the complex system pointing out that interactions among the subsystems are in most cases stronger than within them (p. 477) that making it possible to detect the elements of the system.

Simon introducing his hierarchical approach to systems set the base for the further development of the complexity theory. Simon’s approach became the groundwork of the theory of another author, Philip Anderson, who believes that Simon is right in his assumption that complex systems are open because they interrelate with the environment during interchanging resources with it and that they are systems because they comprise of a certain number of elements working together that can be detected (Anderson, 1999, p. 216). Nevertheless, Anderson (1999) borrowed Simon’s ideas he went further in the development of the theory and pointed out that there are three dimensions of interaction between the subsystems: vertical, horizontal, and spatial. He believed that the vertical dimension is the amount of the elements in the system, vertical is the number of positions they hold, and spatial is the environment in which they operate (p. 216). The author as well believes that complex adapting systems are self-organizing and adapt to the changes in the environments as they learn how to behave in the case of the system evolution.

Maguire et. al (2006) contribute to the further development of the complexity theory. The authors are the first to raise the subject of the limitations of the complexity theory. Thus, Maguire et. al (2006) believe that there are difficulties in defining methods for relevant and accurate decomposition of the complex system into subsystems, measuring the changes occurring in the system in response to shifts in the environment, and the overall deficit of sufficient data on the subsystems and systems when it comes to modeling (p. 177). In general, the authors in their study focus on computing the models of the complex theories and the interrelations between the complexity theory and postmodern approaches to defining the system. As of computation of the complexity sciences models, the authors state that such models usually do not have enough theoretical grounding, are not sufficiently complex, lack logical consistency, are difficult to understand by the people using the various environment for constructing simulation models, etc. (p. 195) thus stating that complexity sciences have a high potential for further development.

Stacey (2011) focuses on the historical background of the development of the complex theories and their division from the chaos theory and its evolution in different areas of investigation from chemistry to economics and the study of industries. The author focuses on the complexity of industry through a prism of chaos theory and believes that the ability of the system’s components to interrelate, self-organize, and evolve in response to the changes in the environment derives from the chaos (Stacey, 2011, p. 229).

So, the authors of the scholarly and practitioner-oriented studies are very much alike in the visible assumptions as they all focus on the characteristics of the complex adaptive systems. They all believe that the complexity of the system is achieved by the existence of subsystems that interact with each other on a hierarchical basis. What is more, these components can learn how to behave in response to the changes in the environment and easily adapt to the newly established conditions. That, in turn, entails the impossibility to predict the further development of the whole system as the change in the behavior of the smallest component may initiate changes on every level.

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Such similarity in assumptions, however, leaves a gap in the knowledge of the complexity. It is important to state that some of the authors, namely Macguire et. al, went further in investigation and except for focusing on the historical development of the theory tried to highlight its limitations and study the simulation models thus setting a background for the further studies of the complex theories and proving that it has a promising future.

References

Anderson, P. (1999). Complexity theory and organization science. Organization Science, 10(3), 216-232.

Grus, L., Grompvoets, A., & Bregt, A. K. (2010). Spatial data infrastructures as complex adaptive systems. International Journal of Geographical Information Science, 24(3), 439-463.

Lansing, S. J. (2003). Complex adaptive systems. Annual Review of Anthropology, 32(), 183-204.

Maguire, S., McKelvey, B., Mirabeau, L., & Oztas, N. (2006). Complexity science and organization studies: Mapping the field. In Clegg, S.R., Hardy, C., Lawrence, T.B. & Nord, W.R. (eds.) The SAGE handbook of organization studies (pp. 166-214). London, England: SAGE Publications.

Simon, H.A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467-482.

Stacey, R.D. (2011). Strategic management and organizational dynamics: The challenge of complexity. (6th ed.). Harlow, England: Pearson.

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