Complexity and Communication

Sir Isaac Newton argued that the relationships between all physical elements can be measured, predicted, and controlled. Complexity theory, as developed in the 20th century by scientists such as the Nobel laureate chemist Ilya Prigogine, says otherwise, holding that the relationships of many physical elements and organisms are nonlinear. Some theorists in the social sciences have begun to recognize the adaptability of complexity theory to human systems. For example, one of its classic ideas—that “initial conditions make a difference”—which was originally discovered in meteorology simulations, is transferable to human relationships. Meeting an attractive person, say, or beginning an important project is affected by the precise context of when such an event happens in a sequence of other possibilities.

Inevitably, complexity theory became an umbrella concept that covers a wide span of influences in the hard and soft sciences—from chemistry and physics to communication and social psychology. It has this breadth because the more a focus on any given system, be it social or physical, is widened to include its supersystem (the layer of influences outside it, such as the weather, external politics, or technological change) and its subsystem (the layer below, such as subordinates, commodities, or outsourced functions), the more complex that system becomes. For example, SEMATECH, the computer research consortium developed in the mid-1980s to regain U.S. market share from Japan, has as its supersystem the computer industry; as its subsystem, it has its various suppliers of products and services. Complexity theory would say that it is impossible to understand SEMATECH as an organization without understanding the industry and its suppliers. And, in fact, a team of researchers documenting the SEMATECH story in the 1990s showed that the consortium's leader, Robert Noyce, found it necessary for SEMATECH to invest in the supplier subsystem in order to improve chip-manufacturing performance.

This entry describes two kinds of complexity and then discusses the kinds of communication to be used in response to them. First is structural complexity, a set of conditions characterized by numerosity (e.g., the greater the number, the greater the complexity), diversity, and interdependence of system parts. When combined, these three conditions increase the amount of uncertainty that one finds or must deal with in a given system. Structural complexity, then, describes the conditions inherent in that system. Dynamical complexity, on the other hand, is the actual process of things changing nonlinearly in a structurally complex system. Say, for example, 100 leaders (numerosity) from 100 sovereign nations (diversity), whose fates are connected, must reach a decision concerning the allocation of scarce resources (interdependence of system parts). The process of their decision making is inevitably complex, and the result equally uncertain, which is to say, unpredictable. While traditional Newtonian science may identify main effects and principal features of a system, complexity theorists would argue that [Page 153]total control of elements within that system is impossible because combined effects that are in simultaneous interplay act—and interact—in unpredictable ways, making systems dynamical. When structural and dynamical complexity are applied to human systems, we are reminded anew that people are only occasionally logical and rational. Because they possess imperfect information, they tend to act on the basis of ideology, chance, and perceived individual payoff.

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