Managing Emergent Phenomena: Nonlinear Dynamics in Work Organizations

Managing Emergent Phenomena: Nonlinear Dynamics in Work Organizations

Managing Emergent Phenomena: Nonlinear Dynamics in Work Organizations

Managing Emergent Phenomena: Nonlinear Dynamics in Work Organizations


Chaos, catastrophe, self-organization, and complexity theories (nonlinear dynamics) now have practical and measurable roles in the functioning of work organizations. Managing Emergent Phenomena begins by describing how the concept of an organization has changed from a bureaucracy, to a humanistic and organic system, to a complex adaptive system. The dynamics concepts are then explained along with the most recent research methods for analyzing real data. Applications include: work motivation, personnel selection and turnover, creative thinking by individuals and groups, the development of social networks, coordination in work groups, the emergence of leaders, work performance in organizational hierarchies, economic problems that are relevant to organizations, techniques for predicting the future, and emergency management. Each application begins with a tight summary of standard thinking on a subject, followed by the new insights that are afforded by nonlinear dynamics and the empirical data supporting those ideas. Unusual concepts are also encountered, such as the organizational unconscious, collective intelligence, and the revolt of the slaved variables. The net results are a new perspective on what is really important in organizational life, original insights on familiar experiences, and some clear signposts for the next generation of nonlinear social scientists.


The year 2001 marks the 20th anniversary of the first journal article on nonlinear dynamics in organizational behavior. The topic was a butterfly catastrophe model of equity in organizations, which was intended as a theoretical integration of some basic psychology, work behaviors, and nonlinear dynamical elements. The first plausible statistical methods for testing any such theory came soon afterward.

Two important developments took place 10 years later. First, I was able to show that a growing range of catastrophe models for discontinuous change, each theoretically grounded in their own way, could account for a substantial amount of behavior variance that could not be accounted for by common linear and static approaches to the same problem. Second, there was a suddenly growing interest within the organizational behavior community in a wider range of nonlinear dynamics that could be applied to a still wider range of organizational phenomena. The hunt for chaos was on; social scientists searched for it in the economy, in the city, in the workplace, in the mind, and in the living room sofa.

Unfortunately, the mathematicians and physicists who gave us chaos only gave us simulations and algorithms that required many thousands of noise-free observations. We needed statistical methods that were suitable to psychological and organizational data. Although the schism in the knowledge base could give some graduate students a nightmare or two, it gave me one of those deja vu moments. By the time the moment had passed, I had harnessed nonlinear regression to evaluate the possibility of chaos and other dynamics in real data.

Thus, in 1995 I published Chaos, Catastrophe, and Human Affairs: Applications of Nonlinear Dynamics to Work, Organizations, and Social Evolution. The book compiled the best substantiated pieces of nonlinear theory and research in the psychology of work behavior (or industrial/organizational psychology and some of us prefer)—plus some new speculations and temptations. The statistical methods for catastrophes, chaos, and so forth were explained and illustrated in the course of the exposition. In the cases in which nonlinear dynamics models were accepted as better explanations of phenomena, the nonlinear models were more accurate by an average of 2:1 in terms of statistical variance accounted for compared to the best linear counterpart.

Meanwhile, the interest in chaos mutated to an interest in complexity. When “complexity” is not being used as a management buzzword, it refers to processes . . .

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