Clinical Chaos

A Therapist's Guide To Non-Linear Dynamics And Therapeutic Change

Linda Chamberlain, Michael R. Butz, Linda Chamberlain, Michael R. Butz

• Publisher: Routledge
• Year: 2016
• Print ISBN: 9780876309261
• eBook ISBN: 9781317714767
• Topic: Psicologia
• Subtopic: Storia e teoria della psicologia

Psychology and the social sciences are in need of a new foundation, one that provides a better model for understanding complex behavior. Chaos theory and its newest permutation, complexity theory, offers an innovative, exciting and potentially revolutionary leap forward in the evolution of scientific thought. In Clinical Chaos, therapists and theoreticians from various areas in the social sciences will explore the relevance and implications for non-linear dynamics in observing, explaining, and understanding human behavior. At last, the scientific search can again encompass surprise, transformation, unpredictability, and pattern. This book is intended to introduce social scientists to chaos through paths that are already familiar. By linking chaos theory with existing psychological theories and established areas of clinical pursuit, Clinical Chaos emphasizes the relevance of this new science in providing a more flexible useful model for complexities of life.

Part One

An Introduction to Chaotic Systems

The journey begins in Part I (Chapters 1 and 2), by exploring the basics of chaos theory and non-linear dynamics. These two chapters will provide an overview of basic concepts and tenets of chaos theory that will resurface throughout this book. The terminology that is common in describing nonlinear phenomena and the dynamics of interactive, interdependent systems are the main attractions on this part of the path. Once the language and patterns of observation have been conquered, Chapter 2 examines the relevance of these concepts for clinicians.

Throughout this book, the authors have attempted to “be gentle” with those readers who may not have approached the territory of pure science for some time. As clinicians, we are aware that our training and work are often focused more on the theories and techniques unique to psychotherapy. Be assured—more familiar therapeutic themes and issues will be the focus of later chapters. We believe, however, that the scientific foundation that is being established by those studying nonlinear dynamics has clear implications for how we observe, understand, and interact with others as therapists. The perspective of chaos theory as it applies to human behavior and psychotherapy is still being developed. We hope that clinicians who read this book will allow themselves to question the foundations of the phenomena they observe and not push ahead too quickly to find applications. Stay with the theory for awhile and try the perspective it offers. For many of us, it has opened up incredible new territory.

Chapter 1

An Introduction to Chaos and Nonlinear Dynamics

Linda Chamberlain

“The future is disorder. A door like this has cracked open five or six times since we got up on our hind legs. It’s the best possible time to be alive, when almost everything you thought you knew is wrong”

Valentine (p. 48). In Arcadia by Tom Stoppard.

In case no one has told you yet, reality is shifting. The platform that social scientists have stood upon for more than a century is breaking apart. This pronouncement will strike terror in the souls of many readers, and joy or relief in the souls of others. It is the goal of this book to make the journey into the realm of chaotic dynamics as non-threatening and illuminating as the authors’ journeys have been. Welcome to chaos and complexity. Leave some assumptions behind for now; they will always be there for you if you want them back after your adventure. In this first chapter, you will be introduced to strange attractors, uncertainty, phase space, non-linearity, punctuated equilibria, butterfly effects, fractals, and other basic concepts that you will encounter in other chapters. As with any journey into new territory, learning the language will help prepare you for encounters with those who inhabit this chaotic landscape.

If you are like many in the social sciences, the realm of “hard” science and the demands of empiricism may be intimidating. Designing empirically solid experiments with human subjects has been daunting at best and often, simply impossible. Most of us have known throughout all of our training and careers that we were working with conditions and variables that (at best) loosely fit the criteria demanded by experimental models. As I once noted in conversation with a microbiologist, it is difficult in psychology to have finite, “clean” results since we generally can’t kill our subjects once we have completed the experiment and reached the outcome we want. The empirical goal of objectivity has been a major stumbling block. Human beings are simply too complex to fit the mold of traditional, “objective” experimentation. As Gregory Bateson (1972, p. 47) noted in a metalogue:

Daughter: What does “objective” mean?

Father: Well. It means that you look very hard at those things which you choose to look at.

D: That sounds right. But how do the objective people choose which things they will be objective about?

F: Well. They choose those things about which it is easy to be objective.

D: So it’s a subjective choice?

F: Oh yes. All experience is subjective.

D: But it’s human and subjective. They decide which bits of animal behavior to be objective about by consulting human subjective experience…

F: Yes—but they do try to be not human.

The basis for scientific inquiry first began to shift when Albert Einstein (1954) delivered evidence which challenged the Newtonian view of a mechanistic universe. The Newtonian goal of discovering comprehensive, immutable laws that would unfailingly govern all phenomena and would allow us to predict and modify the course of natural processes began to crumble under the weight of relativity. Although still largely deterministic in nature (hence, Einstein’s fervent wish that God not play dice with the Universe), relativity set the stage for profound changes in scientific tradition. Perhaps the most important shift was that the observer was introduced back into the system. Objectivity as a part of the scientific process was challenged. Even the most rigorous of scientific endevours, physics, could no longer remove the observer from the data.

The second blow to traditional science came from quantum theory. Quantum science certainly buried even deeper the notion of objectivity, and it sounded an even louder death knell for the world as machine. A major tenet of quantum theory explains that an irreducible degree of randomness is a fundamental feature of nature. Therefore, experimental data and results will fluctuate to an unavoidable extent. “All elementary events occur at random, governed only by statistical laws” (Hebert, 1985, p. xii). Quantum processes are inherently unpredictable such that it is impossible to determine from moment to moment how a system or an element in a system will behave. If that is true for the behavior of such a fundamental element as light, how can that not be true for human behavior? Quantum physics began to weave together the dynamics of chance and predictability in a context-dependent tapestry. It became clear that data gathered out of context was incomplete, particularly if the context did not include the effect of the observer. The idea of spontaneous, unpredictable events was embraced by quantum theory. It began to appear that God did indeed play dice with the Universe.

These challenges to traditional, empirically-based scientific paradigms, coupled with the advent of the computer and the ability to “crunch” vast amounts of data, led to the birth of chaos theory. Chaos is an epistemology based on a concept of reality which, instead of being intrinsically orderly, stable, and equilibrial, is seething with spontaneous change, irregularity, disorder, and chance.

The interest in chaos has generated a myriad of definitions and descriptions that attempt to capture the nature of this beast. As Ian Percival writes, “The science of chaos is like a river that has been fed from many streams. Its sources come from every discipline—mathematics, physics, chemistry, engineering, medicine, and biology …” (Hall, 1991, p. 16).

In retrospect, it is apparent that the interest in chaotic dynamics arose simultaneously in almost every field of scientific study. Pieces of the theory that will be outlined came from meteorologists, paleontologists, biologists, physicists, mathematicians, computer engineers, economists, astronomers, and chemists. Chaos theory is the combination of ideas and research from many diverse scientists. As Robin Robertson notes (Robertson & Combs, 1995), “Chaos theory has begun to emerge as any true symbol emerges, from all directions at once, from the ‘most complex and differentiated minds’ of our age” (p. 14).

So what exactly is chaos and chaos theory?

“Chaos theory is the qualitative study of unstable aperiodic behavior in deterministic nonlinear dynamical systems” (Kellert, 1993, p. 2).

“Chaos is persistent instability” (Percival, 1991, p. 12).

“… chaos is a science of process rather than state, of becoming rather than being” (Gleick, 1987, p. 5).

“… complex behavior that seems random but actually has some hidden order” (Freeman, 1991, p. 78).

“… complex behavior, produced by simple, deterministic rules, is called chaos” (Cohen and Stewart, 1994, p. 190).

Each of the contributors to this book will offer further definitions regarding the nature of chaos and chaos theory. It should, however, be clear from the definitions already given that chaos theory seeks to investigate areas that have previously been avoided or ignored by science. Traditionally, physics and the other sciences have focused on, “the very big and the very small. The universe: the elementary particle. The ordinary-sized stuff which is our lives, the things people write poetry about—clouds-daffodils-waterfalls—and what happens in a cup of coffee when the cream goes in—these things are full of mystery …” (Stoppard, p. 48). Newtonian or classical science is based on simplification, and its effectiveness lies in dealing only with areas in which phenomena can be reduced to fragments amenable to the methods employed to analyze data. As Appleyard notes, “The entire scientific edifice, for all its hermetic inaccessibility to the uninitiated, is a vast monument to simplification” (1992, p. 141). Chaos theory is a human-sized science, a paradigm that deals with the behavior of complex, interactive systems without relying on the reductionistic principles previously employed by empiricism.

The question of whether psychology can be a “true” science has been debated almost since its formation as a separate discipline. Perhaps the dilemma is not so much whether psychology is a science or not, but whether psychology has found or created an appropriate model from which to pursue, gather, and process information. The vantage point of empiricism, from which psychologists have attempted to observe human phenomena, may have constricted our ability to develop a more comprehensive understanding of behavior and the process of change in human beings and human systems.

Chaos theory and the study of non-linear systems offers an alternative model for observation and understanding in psychology. Since chaos theory provides a broader perspective for many fields of science, it “offers unique possibilities for unifying psychology” (Robertson & Combs, 1995, p. 3). Natural and human systems contain essential elements of uniqueness, randomness, and irreversibility. Part of what differentiates theorists in chaos science is that they “jump from the paradigm of things to the paradigm of pattern” (Keeney, 1983, p. 95). As Robertson (1995, pp. 12–13) outlines, chaos theory has a direct relevance for clinical psychology, particularly in three of the basic principles it proposes. First, change isn’t always linear. “A” doesn’t always or only lead to “B”. In addition, small fluctuations in a behavior or sequence can have large effects. This will be further expanded in the description of the “butterfly effect” or sensitive dependence on initial conditions. Second, determinism and predictability are not the same. When there is feedback in a system, deterministic equations can lead to unpredictable results or chaos. Finally, in systems that experience chaotic or “far-from-equilibrium” periods (essentially, all natural, biologically based systems), change is not necessarily related to external causes; these systems can “self organize” at a higher level of organization.

In order to look further at these ideas and others that are encompassed in the theory of non-linear dynamics, it may be useful at this point to learn the language that can guide you through the territory. Chaos theory is rich in symbolism and metaphor. Perhaps this is true of chaos more so than other scientific paradigms because science is finally looking at the human level of experience, where language and story are important tools in communicating. Certainly, there are exquisite, complex mathematics that have been generated by the study of chaos which may interest many readers. It is the purpose of this book, however, to simplify those concepts in order to make them more available to clinicians who may not have an extensive mathematical background. So, welcome to the incredible stories that comprise the non-linear, chaotic view of the world.

THE LANGUAGE OF CHAOS

Before wandering too much further down the chaos path, it may be helpful to acquaint the reader with some basic terms and concepts that will be encountered in other chapters of the book. Chaos and complexity theory are certainly works in progress, and the different aspects of the general theory described here are not exhaustive. This section should provide a beginning point of reference for further examination and a basis for understanding some of the ideas that form the core of non-linear systems dynamics.

“The Butterfly Effect”, or Sensitive Dependence on Initial Conditions

It seems likely that an idea has truly captured the public attention when it is used as the basis for a car commercial. The ad begins with a butterfly flapping its wings in the jungle which adds turbulence to a storm system that is brewing which then generates rain clouds over a wide area of the hemisphere including the stretch of road on which the relieved driver of a new, special traction, better visibility car is making her way home. The idea is that even very tiny, distant events or effects can profoundly impact circumstances in our lives.

In much of the literature on chaos, sensitive dependence is referred to as the “butterfly effect.” The term “butterfly effect” came from a description of a possible chain of linked events described by a meteorologist. In 1960, Edward Lorenz, a meteorologist at the Massachusetts Institute of Technology, was working on a computer generated model weather program. His goal was a common goal of most meteorologists at that time: to clarify the natural laws that were at work in creating weather patterns so that the weather would be more predictable. Although the repetitions of patterns were never quite exact, there was clearly a recognizable structure to the model his computer generated and he was able to see familiar patterns arise over time. He was able to graph many of the different aspects of the weather (i.e. winds, temperatures) and see order in the patterns, even though there was no exact repetition.

In 1961, however, he wanted to replicate a segment of the program in order to examine the data more carefully. Instead of starting the whole program again, he began at a mid-point near the phenomena he wanted to examine. In the initial program, he had used six decimal places, the number:.506127. In his input for the segment replication, he used only the first three decimal places:.506. He had assumed that the difference—one part in a thousand—would be inconsequential. This assumption proved to be incorrect. The two patterns began the same but at one point began to clearly diverge and become two very different patterns.

Newtonian science would have predicted that the small change in the initial conditions would have had some effect, but that the effect would stay minimal and the patterns would remain largely similar. In linear scientific theory, a small change in a standard pattern should produce only an equivocally small fluctuation. Lorenz, however, realized he had not simply produced some type of computer error that could be overlooked as accidental. He recognized that the “butterfly effect” was no accident. The butterfly effect—the theory that a butterfly stirring the air today in Peking can transform storm systems next month in New York—became known technically as “sensitive dependence on initial conditions.” The understanding that even minute differences in input can quickly become overwhelming differences in output is a cornerstone of chaos theory. Lorenz also recognized that “crisis points,” where a fluctuation could occur, exist everywhere in natural systems.

Strange Attractors

Strange attractors began their existence as mathematical terms that helped explain why...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Acknowledgments
• PART 1: AN INTRODUCTION TO CHAOTIC SYSTEMS
• PART 2: CHAOS THEORY AND CLINICAL ORIENTATIONS
• PART 3: CHAOS THEORY APPLICATION IN CLINICAL RESEARCH AND PRACTICE
• PART 4: SPECIAL CLINICAL ISSUES AND CHAOS THEORY
• Biographies of Authors
• Author Index
• Subject Index