Chaos, Catastrophe, and Human Affairs

Applications of Nonlinear Dynamics To Work, Organizations, and Social Evolution

Stephen J. Guastello

• Publisher: Psychology Press
• Year: 2013
• ISBN:
  9781134787852
• Edition: 1
• Topic: Psychologie
• Subtopic: Geschichte & Theorie in der Psychologie

Whether talking about steering a wheelbarrow over rugged terrain or plotting the course of international relations, human performance systems involve change. Sometimes changes are subtle or evolutionary, sometimes they are catastrophic or revolutionary, and sometimes the changes are from periods of relative calm to periods of vibrant oscillations to periods of chaos. As a general rule, more complex systems are likely to produce more complex forms of change. Although social scientists have long acknowledged that change occurs and have considered ways to effect desirable change, the dynamical processes of change have been poorly understood in the past. This volume combines recent advances in mathematics and experimental design with the best available social science theories to produce a new, integrated, and compact theory of work, organizations, and social evolution. The domains of application extend from human decision-making processes to personnel selection and work motivation, work performance under conditions of stress, accident and health risk analysis, the development of social institutions and economic systems, creativity and innovation, organizational development and group dynamics, and political revolutions and war. Relative to other literature on nonlinear dynamical systems theory (NDS), this book is unique in that it integrates new developments in NDS with substantive psychological theory. It builds on many recent developments in organizational theory to show that nonlinear dynamics were often implicit in those works all along. The result is an entirely new way of viewing social events, understanding change processes, and asking questions about social systems. This book also contains much new empirical work and explains the newly developed methods for testing these new hypotheses.

1

An Invitation to Chaos

Look out the nearest window. Is there any straight line out there that wasn’t man-made? I’ve been asking the same question of student and professional groups for several years now, and the most common answer is a grin. Occasionally a philosophical person will comment that even the lines that look like straight lines are not straight lines if we look at them through a microscope. But even if we ignore that level of analysis, we are still stuck with the inevitable observation that natural structures are, at their core, nonlinear.

If the foregoing is true, why do social scientists insist on describing human events as if all the rules that make those events occur are based on straight lines? The probable answer is that straight lines are easier to work with. Perhaps it has been difficult enough to figure out the simple rules, and the tools to figure out the complicated rules have been in short supply.

How complicated is “complicated”? There are four characteristics that make straight lines different from each other: their slopes, intercepts, lengths (in which case we have a line segment), and where we put them in relationship to … well … other lines. Curves, on the other hand, can take on a myriad of shapes, ranging from gradual contours to convoluted balls of yarn. The essential challenge is to describe what all those hitherto ignored shapes, and the information implied by them, mean. If we are successful, the explanations should, in turn, have a serious impact on the way we look at the world and on the type of information we choose to seek out when we need to know something.

So how is the explanation of interesting events—such as a jury’s perception of evidence, the productivity of a manufacturing organization, an industrial accident or disease, urban renewal, fatigue and overwork, revolution and war—affected by the linear versus nonlinear issue? Nonlinearity impacts on our notions of cause and effect. If we have an event, or an effect, Y, that is caused by a condition X, and there is a linear relationship between X and Y, then what we are really saying is that any change in X, large or small, will have a proportional effect on Y. If there is a nonlinear relationship between X and Y, however, then a small change in X could produce a dramatic change in Y, or alternatively, a large change in X could produce no discernible change in Y. Furthermore, the amount of change in Y that we get from any change in X would have a lot to do with the initial values of X and Y that we started from.

Does any of this added complication actually get us anywhere? Are the differences between a nonlinear interpretation of an event and a linear one actually accomplishing anything? Let’s put it this way: Psychology does little better than to account for 50% of any phenomenon it tries to study, particularly outside of the laboratory. As for the other 50%, we glibly call it error. Sometimes we give the unknown a long name, such as “random measurement error,” or a similar concatenation of adjectives, as if all we had to do was to measure our constructs a little better and we’d have our problems licked. It couldn’t be that our explanations are missing a few important parts, could it?

Of course it could! In fact, the central objective of this book is to provide a “parts catalog,” some “tools” for installing those parts into theories, and an extensive array of applications where improvements are in evidence. At this early stage of the conceptual development, I will take matters a little further to say that, on the average so far, theories that are properly equipped with nonlinear and dynamic models account for twice as much of the phenomena they purport to describe compared to their linear counterparts (Guastello, 1992a). Sometimes the benefit is larger.

CHAOS BY ANY OTHER NAME

Many seemingly random events are actually more predictable than people have usually thought. The tools of prediction are differential equations, and there is a relatively small set of general models that we can apply to events that we observe changing over time. In this book we have no need to agonize over the basic theory of differential equations, advanced topology, and other mathematical contributions. We use the products of those mathematics instead.

Chaos theory is the shortest common name of the theory that we unfold and elaborate here. It is actually a charming misnomer. There is indeed a phenomenon known as “deterministic chaos,” and it is an important part of chaos theory. But the underlying theory that gives the chaos part of the story meaning is much broader in scope. The repertoire of nonlinear change processes that we now have available ranges in complexity from single-point dynamics, to waves and cycles, to the seemingly random world of chaotic events. Furthermore, there are explanations for why change processes are sometimes simple, sometimes complex, and sometimes change from simple to complex or vice versa.

Nonlinear dynamical systems theory is, therefore, perhaps the most accurate label for the subject matter at hand. It’s a handful of words to be processed, so the acronym NDS is adopted. Complex systems theory, sometimes called complexity theory, is closely related in meaning to NDS, but the shift is away from the mathematical tools of analysis and description to the qualitative understanding of the systems to which the new concepts are applied, of the complexity of those systems, and of how nonlinear dynamic processes could be aggregated within a complex system. Catastrophe theory is a special topic within the broader domain of NDS that pertains to sudden, discontinuous changes of events. In other words, catastrophe theory is all about the proverbial “straw that broke the camel’s back.”

The next objective for this chapter is to delineate eight central propositions of NDS or the ideas that developed from it.

1. Many seemingly random events are actually predictable with a set of nonlinear differential equations.

2. The so-called chaotic processes that produce apparent randomness and uncertainty vary in complexity and level of turbulence.

3. All discontinuous changes of events can be modeled by one of seven elementary topological forms, which are hierarchically ordered by level of complexity.

4. Complexities of systems are indicated by the dimensionality of their behaviors.

5. Conventional notions of four-dimensional space–time are inadequate to explain many phenomena; dimensionalities greater than four and fractional dimensionalities are common in natural systems.

6. The classical notions of cause and effect are replaced by concepts involving control (in the engineering sense), bifurcation, energy, and turbulence.

7. Within the discipline of psychology, and within several of its subdisciplines, many of the rudiments of chaos and catastrophe theories can be observed in research reports as far back as the turn of the last century.

8. Where there has been an opportunity to test a well-developed application of NDS against a conventional linear alternative, NDS described phenomena twice as well as its linear competitor.

The tool kit of NDS consists of attractor forces and repeller forces, stabilities and instabilities, bifurcation and self-organization, fractal geometry, the distinction between evolutionary and revolutionary change, and catastrophes and discontinuous change. Chapter 2 is devoted to the expansion on the central concepts. A new theory inevitably brings with it some new analytic techniques centered around the question, “How do we know what we know?” How can we run tests on data to determine whether one or another process is taking place, assess the level of complexity, and so forth? Chapter 2 concludes with an overview of analytic strategies that have become common in NDS research (oxymoron not intended!), and Chapter 3 is devoted to the structural equations approach to data analysis that has become most influential to applications contained in this book.

Having stated the overarching premises of NDS, this chapter continues with the concept of a general systems theory, and why the word system is used so often! This chapter concludes with an overview of the applications themselves, and the interrelationship among them before they were confronted with NDS. Chapter 2 discusses the basic NDS theory with frequent mention of where the applications would involve a NDS concept, whereas chapters 4 through 13 reverse the priorities and tell their stories from the point of view of the application, therein detailing how NDS concepts made improvements on specific theories and how we knew we were successful.

GENERAL SYSTEMS THEORY

A general systems theory is an interdisciplinary theory. In other words, it contains rules and propositions that extend beyond their initial application to at least one other field of study. A theory that successfully solves a problem in political economics with principles of evolutionary biology would qualify as a general systems theory. As one might anticipate, some general systems theories are more intensive and extensive than others. A theory that is intensive explains a lot about a particular phenomenon. A theory that is extensive applies to a wide variety of situations, where the more disparate and apparently unrelated the applications are, the better.

There are two traditions in general systems theory that are pertinent to NDS. One is the mathematical approach, and the other is the living systems approach. The mathematical approach, usually attributed to von Bertalanffy (1968) and Wymore (1967), uses mathematical relationships as the central set of premises. Applications would utilize the mathematical formula to solve particular problems or to model particular relationships. A good working set of mathematical models would carry with it qualitative relationships by which an application might be recognized. An application is thus an example of the generally stated prototype system.

Living systems theory (Miller, 1978) is the second tradition relevant to NDS and consists of three central premises. First, every living system contains 20 subsystems that can be divided into three groups: those that process both matter and information, those that process matter and energy, and those that process information (Miller & Miller, 1990; Miller & Miller, 1992, 1993a, 1993b). Particular subsystems are elaborated in subsequent chapters within the context of the specific applications of NDS.

The second key point of living systems theory that affects the NDS applications is level of system complexity. The levels are the cell, organ, organism (sometimes differentiated between human and other), group (with the same possible subdivision), organization, community, society, and supranational system. The great preponderance of applications covered in this book involve the human organism, group, and organization, and some involve relationships between humans and groups and organizations. There are, nonetheless, a few excursions into community, societal, and supranational levels of system organization. The third key point of living systems theory is the principle of fray-out (Miller & Miller, 1990). As systems grow in complexity from cells to supranational systems, the systems and subsystems become more differentiated and complex.

General systems theories can be further thought of as metatheories, methodologies, and possibly a whole way of viewing the world. A metatheory is a theory that organizes concepts, objects, or relationships inherent in several local (or specific) theories. Thus specific objects can be interchanged from one application to another, but the relationships among those objects could remain approximately the same. Another approach might show interchangeability of objects and relationships, but the “blueprint” that defines the metatheory would tell the scientist where to look for objects and relationships that could be useful.

When viewed as a methodology, a general systems approach would proceed to analyze and describe phenomena, beginning with the tenets of a working general systems theory. The next step would be to create a model of a phenomenon, which would compile a representation of a system using the tools of the general theory plus additional information that is specific to the application. The model-making process typically draws on the past successes and failures encountered with applications of the general theory. If a general theory is new in the sense that working applications are few and far between, the best thing that could happen to such a theory is the discovery of an application that utilizes as much of the general theory as possible with a minimum of situational alterations. Such an application would serve as a prototype for new applications.

If a general systems theory is truly meritorious, the knowledge gained from a successful application would increase the knowledge about the core principles of theory, and thus facilitate the hunt for further applications. One good application thus serves as a metaphor for another. Limitations to applicability will inevitably be encountered, and may suggest ways to improve the general theory with, hopefully, a minimum of new propositions. A general systems theory will eventually reach its limits of generalizability, and we would hope that it would in fact do so. A theory that is so general that it explains everything explains nothing. The trick is to build a theory that explains a lot and that has tentacles linking it to other general theories, where one domain begins and another ends, and to local theories whose main purpose is to describe a particular class of phenomena typically encountered within a particular discipline.

Another aspect to the idea of general systems theory as methodology concerns how one might go about proving the truth of the general principles. Although considerable effort goes into developing a cogent metaphor for a new system based on a known system, the validity of the metaphor requires empirical testing. New theories often involve special propositions that distinguish the theory in question from alternative explanations. Unique ideas often result in the need for unique equipment, experimental designs, and protocols of data analysis. New methodologies often evolve as new questions are asked, and generate additional new questions.

The last point to make is about how general systems theory could be viewed as an entire way to view the world, as Bahg (1990) observed. No doubt, if one has a general systems theory that reliably solves a wide range of problems, using it to assimilate new information and experience becomes a habit; in that sense it is a world view. If a theory is new, contains many novel ideas, and is powerful in application, that theory would eventually become a matter of philosophical discussion. One does not need to buy into any particular philosophy (of science or of anything else), however, in order to comprehend the theory, to interpret the result of its applications, or to use it successfully for further work. Philosophy could flow from the scientific work, but NDS is not predicated on a philosophy.

AN OVERVIEW OF INDUSTRIAL PSYCHOLOGY

Industrial psychology is concerned with applying any viable idea drawn from theoretical psychology to answer questions or solve problems concerning people at work. The theoretical content is complemented by field research where the research questions are defined by the needs of real people doing real work. The topic areas are typically broken into three broad groups: personnel selection, training, job analysis, and other individual issues; organizational psychology and group dynamics; and human factors engineering and ergonomics. There is a small amount of overlap across the three topic groups. The first ideological movement emerged a...