ABSTRACT
Chapter 10 introduced the thought that there are individual differences inmanagers’ ability to predict and control chaos and other dynamical events that occur within an organization. Chapter 11 introduced several situations in which decisions must be made concerning dynamical events in the external economic environment. Collective decisions by individuals and institutional policymakers contribute to economic dynamics as much as they respond to the dynamics. It is believed that many of the same forecasting skills are at work in both the internal and external arenas. This chapter describes some methods for predicting the future and explores
some of the psychology that is allegedly involved. The first methods date back centuries before the scientific era. Conventional psychology has made some notable contributions also, most of which are better known for other reasons. NDS concepts play progressively larger roles aswemove toward present-day approaches to futurism. Subsequent sections of the chapter highlight various means of predicting and controlling chaos, catastrophes, and self-organization explicitly. This chapter perhaps contains the greatest amount of speculative material, but a good deal of it is empirically grounded. On the other hand, it is not possible to ride off into the sunset without a sunset. Decision theories and computer programs for decision-support techniques are
designed to assist decision-makers to maximize their outcomes and minimize
decision errors. Decision support programs may be ineffective when the management decisions involve information that is incomplete, inaccurate, and changing over time. Decision-makers are likely to pay for certainty by selecting options that involve less risk, or by accessing the missing information sources that would serve to reduce uncertainty. Uncertainty in decision making takes several forms: (a) The decision maker
knowswhat the possible outcomeswould be and knows the probabilities associated with each outcome, but is compelled to guess which of the outcomes will actually take place. (b)Thedecision-maker knowswhat the possible outcomeswouldbe, but does not know the probabilities associated with each outcome. (c) The decisionmaker has an incomplete knowledge of the possible outcomes, and hence, the probabilities associated with any of the known options are speculative. (d) The decision-maker has incorrectly identified the problem, and as a result identified all the wrong options. Decision aids may take the form of single simple equations, such as a multiple
linear regression equation, or a complex computer program such as an “expert systems.”The connection between complexity theory and the control of uncertainty dates back to the late 1940s. The landmark contributions are chronicled here in four epochs: artificial life and artificial intelligence, control of chaos from the mathematical point of view, controlling catastrophes, and control of dynamical events from the point of view of social processes. Attention is given to both the general procedures and the specific variables that have surfaced in psychological or economics research. At the broadest level of analysis, the control of dynamics systems requires a
perspective that allows change and fluctuation as normal, rather than a perspective that insists on fixed-point equilibrium control. Sensitivity to initial conditions and synchronization play important roles in system control; system control may focus on the mathematical control of engineering systems, or the social expression of control in organizations (Ditto&Munakata, 1995;Kaas, 1998;Stacey, 1992, 1993).
People often see advantages in knowing something about the future. Questions might pertain to the fate of a single business transaction or a global trend, such as energy consumption patterns, population and environment shifts, health care utilization, economic flow and distribution of wealth, and relationships among institutions (e.g., businesses, universities, and other educational environments; governments; scientific enterprises; labor representatives; etc.). This section of the chapter describes some landmarks in the technology of forecasting, some current ideas and issues, and the type of thinking that takes place. Later sections of this chapter expand on several points concerning the cognitive
processes that are involved specifically in the prediction of chaos and other unstable
events: artificial intelligence and artificial life programs, cognitive processes in the prediction of chaotic number series, use of chaotic controllers in a system, manipulation of control parameters that are known to a situation, and techniques that facilitate the self-organization of a system. The foregoing techniques assume a certain level of knowledge of the system, the nonlinear dynamics that are involved, and the control parameters that are included. Several other cognitive processes relevant to futurism were identified in
Chapters 5 and 6: There is expectancy theory whereby the rat guesses where the cheese will be, based on prior experience. There is game theory, whereby individuals choose strategies based on utilities to themselves and others; each player in a game tries to second-guess the other players’ responses. There is divergent thinking of the type inherent in the Consequences test, which compels people to think of a complex system and its possible outcomes. More generally there is divergent thinking and cognitive style. A certain amount of skill, however, needs to be developed whereby the vizier
can form a clear picture of the current state of a system, capture its underlying complexity, and envision possible future scenarios of what might happen if certain other events are introduced. Note here the distinction between the monolithic visions of the visionary leader and themultiplicity of vision that occurs in futurism. Of interest, there are research institutes all over the world dedicated to futuristic thinking and producing meaningful results.
