Relevance and Implications of Chaos Theory for Strategy (Levy)


 
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Relevance and Implications of Chaos Theory for Strategy (Levy)
Stefka Nenkova, Student (University), Netherlands

Industries are complex, dynamic and nonlinear systems, in which interdependence occurs between firms, as well as between firms and other actors in the same environment such as consumers, the government, and financial institutions (Levy, 1994). Taking into account this view, Levy suggests several really important implications of chaos theory for strategic planning / strategy:
  • LONG-TERM PLANNING IS VERY DIFFICULT – the main problem is “trying to use finite measurements in an infinite world”. According to chaos theory, there is little use in trying to plan for the future based on the past since history does not repeat itself. On the contrary, it suggests that instead of putting valuable resources into forecasting, strategic planners should consider a number of possible scenarios, which would allow for more flexibility when facing change. In this train of thought, joint ventures and acquisitions could be seen as an effort to keep a foothold in different potential scenarios in order to cope with uncertainty and change.
  • INDUSTRIES DO NOT REACH A STABLE EQUILIBRIUM – chaos theory implies that “systems can never pass through the same exact state more than once” since if that was true they would be caught in an infinite cycle, driven by deterministic relationships. From that assumption it can be concluded that stability is an illusion, which would not continue for long. Furthermore, chaos theory suggests that “chaotic systems can spontaneously self-organize into a more complex structure” (Allen, 1988)
  • DRAMATIC CHANGE CAN OCCUR UNEXPECTEDLY – one of the assumptions made in traditional economic and strategic paradigms is that small changes in the parameters correspond to small changes in the equilibrium outcome. Quite contrary, chaos theory follows the assumption that small endogenous as well as exogenous disturbances may lead to large changes. This implication is often underestimated by managers. What is more, “the size of the fluctuations from one period to the next in chaotic systems have a non-normal probability distribution”, which opposed to normal distribution suggests more frequent occurrence of large fluctuations.
  • SHORT-TERM FORECASTS AND PREDICTIONS OF PATTERNS CAN BE MADE – in contrast to its name, chaos also contains a certain degree of order. This allows useful short-term forecasts to be made, but only when there are accurately specified starting conditions. Another implication of the existence of order in chaos is that fluctuations have boundaries determined by the structure of the system and its parameters. Moreover, useful information could be found in the repetitive patterns that chaotic systems trace, even though the circumstances and results differ. These patterns are independent of scale, meaning that “similar patterns are traced by a system whatever horizon is used to view it” (that can be observed in economic time series, stock prices, etc) suggesting the generation of fractals (images of patterns within patterns).
  • GUIDELINES ARE NEEDED TO COPE WITH COMPLEXITY AND UNCERTAINTY – strategy by definition is understood as “guidelines that influence decisions and behavior”. General guidelines are necessary because it is impossible to identify the optimal course of action for every possible scenario. The need for broad strategies is evident when the complexity of the industry systems is taken into account. However, chaos theory takes this one step further, implying that strategies need to adapt in order to cope with the dynamic nature of the chaotic systems. The problem is that “there is no simple way of deriving optimal strategies for a given system”. Simulation models might help find the most effective way to achieve a goal.
Sources:
Levy, D. (1994) “Chaos Theory and Strategy: Theory, Application, and Managerial Implications”, Strategic Management Journal, Vol. 15, Special Issue: Strategy: Search for New Paradigms (Summer, 1994), pp. 167-178
Allen, P. M. (1988) “Dynamics Models of Evolving Systems”, System Dynamics Review, 4, Summer, pp. 109-130.
 

 
 

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