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Complexity in Non-adaptive Organizations

Chaotic systems can reach a state of stable order through three mechanisms: positive feedback loops, negative feedback loops, and mixed feedback loops leading to complex, yet patterned, outcomes. This last state of order is typified by an inability to model such outcomes using standard mathematical approaches. Instead, simulation, rather than prediction, is used in order to deconstruct general trends of complex systems (Scott, 2003, p. 93). Such has been the case for general scientific research methodologies for several hundred years. Philosophers such as Rene Descartes (1596-1650), John Locke (1632-1704), George Berkeley (1685-1753), and David Hume (1711-1776) helped form the backbone of what is currently the scientific method (Moser & Vander Nat, 2003).

It has only been since the introduction of computer systems however that chaotic systems have been able to be modeled due to the complex nature of the calculations. As Lorenz (1963) discovered, small changes in initial conditions can lead to large differences in final results when linear dynamic systems are modeled using computer simulations. Open systems however are not represented by linear systems (Melin & Castillo, 2002).

Since Lorenz’s initial weather simulations, research into non-linear dynamic systems has produced some surprising results as they apply to adaptation. Outcomes in chaotic open systems have been since shown to generate stable ordered patterns which show hallmarks of being adaptive (Packard, 1988). More specifically, order appears spontaneously at the periphery between systems governed by chaotic and non-chaotic rule sets (Packard, 1988; Kauffman, 1995; Marion, 1999). This area, which Kauffman describes as providing order for free, has two very unique characteristics of adaptation – fluidity and memory – fluidity being the ability to communicate information at a distance and memory being the ability to store information (Waldrop, 1992).

These findings have two very real consequences for organizational survivability. The first is that adaptation within organizations must exist at the edge of chaotic environments (non-linear open systems) or else rely on exclusively positive or negative feedback mechanisms (i.e. linear closed systems) for establishing order. The second is that in the absence of ordered feedback mechanisms, an organization must address issues of knowledge emergence to remain sustainable (Nonaka & Nishiguchi, 2001). These ideas are supported by Marion’s (1999) work on complex systems which suggested that an inability to adapt either to environmental factors or shifts in paradigms are the primary causes of organizational extinction.

The non-adapting organization will therefore suffer from a number of serious faults in the organization’s learning capability. These will include:

  1. An inability to break with past practices (Govindarajan & Trimble, 2004),
  2. An inappropriate sense of future expectations (Weick & Sutcliffe, 2001),
  3. An inability to detect and plan for crises (Mitroff, 2004),
  4. An inability to reframe thinking to match the situation (Bolman & Deal, 2003),
  5. An inability to effectively communicate through political action (Bourdieu, 2003),
  6. Ineffective strategies for knowledge emergence (Nonaka & Nishiguchi, 2001),
  7. Taking too long to adopt new paradigms (Kuhn, 1996),
  8. Not establishing an effective logos around task activities (Frankl, 1984),
  9. Not understanding the organization’s resource capabilities (Barney, 1996), and
  10. An unwillingness to explore adaptation strategies (March, 2003).

In addition, Barrett and Peterson (2000) noted that there is a large difference between the adaptive learning organization and the generative learning organization. Barrett and Peterson observed that adaptation toward problems fundamentally fragments an organization. Adaptation assumes control over factors of production. That is, it establishes either positive or negative feedback mechanisms which may be argued to drive a complex organization away from a narrow zone in which patterned stability arises. Generative learning, in using a more appreciative approach, is more highly focused on fluidity and memory mechanism rather than control As Barrett and Peterson noted,

Appreciative inquiry takes seriously the notion that how we live our life is a function of where we put our collective attention. Stewards of appreciative learning processes pose a provocative question: What happens if we turn our attention to what is most valuable, to what is most vibrant in a human system? (pp. 13-14)

This line of questioning is very similar to the line of argumentative reasoning used by Kauffman (1995). Kauffman observed that autocatalysis in chaotic systems was not based on controlling mechanisms but rather generative mechanisms (p. 62). This may be due to the fact that controlling mechanisms are focused on using the smallest number of resources to gain a desired outcome whereas autocatalysis doesn’t depend on economies of scale. Rather, order is generated out of phase transitions irrespective of economies. That is, once a sufficiently large enough critical mass is built up, order spontaneous generates. In the case of the non-adaptive organization, this implies the organization either must adapt to its new environment or, if it is unable to leverage controlled feedback mechanisms, inevitably becomes extinct.

Kevin Feenan
Managing Director
Knomaze Corporation

References

Barney, J. B. (1996). Gaining and sustaining competitive advantage. Reading, MA: Addison-Wesley Publishing Company, Inc.

Barrett, F. J. & Peterson, R. (2000). Appreciative learning cultures: Developing competencies for global organizing. Organization Development Journal, 18(2), pp. 10-21.

Bolman, L. G., & Deal, T. E. (2003). Reframing organizations: Artistry, choice, and leadership. 3rd edition. San Francisco: Jossey-Bass.

Bourdieu, P. (2003). Language & symbolic power (7th ed.). Cambridge, MA: Harvard University Press.

Frankl, V. E. (1984). Man’s search for meaning: An introduction to logotherapy. New York: Simon & Schuster.

Govindarajan, V. & Trimble, C. (2004). Strategic innovation and the science of learning. MIT Sloan Management Review, 45(2), 67.

Kauffman, S. (1995). At home in the universe; The search for the laws of self-organization and complexity. New York: Oxford University Press.

Kuhn, T. S. (1996). The structure of scientific revolutions (3rd ed.). Chicago: University of Chicago Press.

Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of Atmospheric Sciences, 20, pp. 130—141.

March, J. G. (2003). Organizational adaptation. In Mintzberg, H., Lampel, J., Quinn, J. B., & Ghoshal, S. (Eds) The Strategy Process – Concepts, Contexts, Cases (pp. 468-471). Upper Saddle River, NJ: Prentice Hall.

Marion, R. (1999). The edge of organization; Chaos and complexity theories of formal social systems. London: Sage Publications.

Melin, P., and Castillo, O. (2002). Modelling, simulation, and control on non-linear dynamical systems. An intelligent approach using soft computing and fractal theory. New York: Taylor & Francis Inc.

Mitroff, I. (2004). Crisis leadership: Planning for the unthinkable. New York: John Wiley & Sons.

Moser, P. K., & Vander Nat, A. (2003). Human Knowledge: Classical and contemporary approaches (3rd ed.). New York: Oxford University Press.

Nonaka, I., & Nishiguchi, T. (Eds.). (2001). Knowledge emergence: Social, technical, and evolutionary dimensions of knowledge creation. Oxford: Oxford University Press.

Packard, N. H. (1988). Adaptation toward the edge of chaos. Urbana IL: University of Illinois at Urbana-Champaign, Center for Complex Systems Research.

Scott, W. R. (2003). Organizations: Rational, natural, and open systems (5th ed.). Upper Saddle River, NJ: Prentice Hall.

Waldrop, M. M. (1992). Complexity: The emerging science at the edge of order and chaos. New York: Simon & Schuster.

Weick, K. E., & Sutcliffe, K. M. (2001). Managing the unexpected. San Francisco: Jossey-Bass.

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