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Chaos Theory Then and Now: The Continued Importance in Psychology
By: Matthijs Koopmans on: Mon 02 of Feb, 2009 [21:33 UTC] (445 reads)

This presentation intends to provide an appraisal of recent developments in the field of nonlinear dynamical systems (NDS) in psychology. While the study of dynamical and systemic processes is an old intellectual tradition, dramatic new developments have taken place during the last three decades, that include catastrophe theory (Thom, 1975), chaos theory (Prigogine & Stengers, 1984), complexity theory (e.g., Holland, 1995; Waldrop, 1992), fractal geometry (Mandelbrot, 1983) and agent-based modeling (Elliott & Kiel, 2004). Although these new developments all originate outside of psychology, psychologists have made extensive use of them in their research as well as psychological practice, as is attested to by an extensive list of edited books (e.g. Abraham & Gilgen, 1995; Masterpasqua & Perna, 1997; Orsucci, 1998; Robertson & Combs, 1995; Sulis & Combs 1996; Tschacher & Dauwalder, 1999) that examine the applicability of nonlinear dynamical systems in the various sub-disciplines of psychology.

photo from 2007 Winter Chaos conference, Puerto Rico

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It is interesting and revealing to look back to way nonlinear dynamical systems were introduced in psychology some fifteen years ago, and how its potential was described back then, and compare this to the way NDS is currently utilized in the field. There was a great deal of excitement at the time and the potential of chaos and other theories as the following quotes illustrate:
Chaos theory is not simply a set of novel procedures that can be imported into the existing establishment of scientific psychology; it in fact represents a fundamental revolution in viewpoint (Combs, 1995, p. 130)

Chaos theory has stuck such a deep chord in so many different disciplines that it is difficult to account for its attraction in terms of technological advances alone. One reason may be that chaos theory has taken us away from the algebraic and numerical abstraction into the geometry of pattern and form. But another even more compelling reason may be that chaos has an archetypical power of its own. (Peat, 1995, p. 371).
NDS scholars have now moved past preliminary assessments of the potential of these new dynamical models in the field to embark on the less glamorous but ultimately more important task of systematic model building and developing an empirical research agenda. The purpose of the proposed presentation is to provide an appraisal of the recent progress that has been made in these areas, and its significance of this progress for the field of psychology, as well as identify some of the challenges ahead. The presentation is based on a recently published book on the topic (Guastello, Koopmans & Pincus, 2009), which discusses recent theoretical developments in nonlinear dynamical systems psychology with separate chapters for social psychology, developmental psychology, neuropsychology, psychophysics, organizational psychology and cognitive psychology and other sub-disciplines within psychology. In a way, the book can be seen as a sequel to the titles referenced above.
Why is NDS important to psychologists? The papers included in the Guastello et al. volume indicate that NDS has many roles to play in the field, including at least the following: a) NDS clarifies the use of dynamical concepts in psychological research and theory, b) it provides a rich and well defined lexicon of change concepts, c) offers a new perspective on old questions in the field, d) offers an opportunity to revisit and refine old dynamical systems models based on recent developments in nonlinear dynamics, e) identifies prototypical examples of dynamical phenomena and build theories and an empirical research agenda around them, f) describes the inherently dynamical and systemic nature of many psychological phenomena, g) facilitates input from other disciplines about systemic behavior, h) reconciles seemingly controversial issues and integrates disparate areas of knowledge into a single theoretical framework, and i) target areas of weakness in traditional linear models. I will illustrate each of these roles briefly based on the work presented in the book.
What are some of the challenges ahead? There are many unresolved questions with regards to the measurement of chaotic processes in psychology. Both from within and outside of the community of dynamical systems scholars, doubts have been expressed about our ability to ever be able to measure chaos in any non-approximate way (see e.g., Kaplan & Glass, 1995; McSharry, 2005). Similarly, the identification of inherent predispositions toward change within system is an important aspect of understanding emergence (Goldstein, 2002), but fraught with measurement challenges that we have not even begun to address. While the translation of theoretical assumptions into sound measurement strategies is a central aspect of good science, it does not necessarily follow that we need to try to operationalize chaos and emergence as if they were psychological constructs. It is instructive to once again revisit Piaget’s theory of child development in this context (e.g., Piaget, 1967), because it is a clear example of a dynamical theory whose claims could be fruitfully investigated empirically, without there being a need to operationalize and measure concepts such as equilibrium and frame of reference. Perhaps chaos and other NDS concepts are also better understood as part of a larger comprehensive framework that, in its totality, provides guidance as to how we should define our priorities in research and practice and as a way to help generate hypotheses that express the relationship between nonlinear processes and their context, rather than viewing it as a simple one-to-one correspondence between NDS constructs and their associated measurement strategies.
References:
Abraham, F. D. & Gilgen, A. R. (1995). Chaos Theory in Psychology. Westport, CT: Praeger.
Combs, A. (1995). Psychology, chaos, and the process nature of unconsciousness. In F. D. Abraham & A. R. Gilgen (Eds.) Chaos theory in psychology (pp. 129-137). Westport, CT: Praeger.
Elliott, E. & Kiel, L. D. (2004). Agent-based modeling in the social and behavioral sciences. Nonlinear Dynamics, Psychology and Life Sciences, 8, 121-130.
Goldstein, J. (2002). The singular nature of emergent levels: Suggestions for a theory of emergence. Nonlinear Dynamics in Psychology and Life Sciences. 6, 293-309.
Guastello, S. J., Koopmans, M. & Pincus, D. (2009). Chaos and Complexity in Psychology: The Theory of Nonlinear Dynamical Systems. New York: Cambridge University Press.
Holland, J. H. (1995). Hidden order: How adaptation builds complexity. New York: Basic.
Kaplan, D. & Glass, L. (1995). Understanding Nonlinear Dynamics. New York: Springer.
Mandelbrot, B. B. (1983). The Fractal Geometry of Nature. New York: Freeman.
McSharry, P. (2005). The danger of wishing for chaos. Nonlinear Dynamics in Psychology and Life Sciences. 9, 375-397.
Orsucci, F. (1998) The complex matters of the mind. Singapore: World Scientific.
Peat, F. D. (1995). Chaos: The geometrization of thought. In R. Robertson & A. Combs (Eds.) Chaos Theory in Psychology and Life Sciences. Mahwah, NJ: Erlbaum. Pp. 359-372.
Perna, P. A. & Masterpasqua, F. (1997). The psychological meaning of chaos. Washington, DC: American Psychological Association.
Piaget, J. (1967). Six psychological studies. New York: Random House.
Prigogine, I. & Stengers, I. (1984). Order out of chaos: Man’s new dialogue with nature. New York: Bantam Books.
Robertson, R. & Combs, A. (1995). Chaos Theory in Psychology and Life Sciences. Mahwah, NJ: Erlbaum.
Sulis, W. & Combs, A. (1996). Nonlinear Dynamics in Human Behavior. Singapore: World Scientific.
Thom, R. (1975). Structural Stability and Morphogenesis. New York: Benjamon-Addison-Wesley.
Tschacher, W. & Dauwalder, J. P. (1999). Dynamics, Synergetics, Autonomous Agents. Singapore: World Scientific.
Waldrop, M. M. (1992). Complexity: The Emerging Science at the Edge of Order and Chaos. New York: Simon & Shuster.

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