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ADDING CHAOS TO LIVING SYSTEMS THEORY

Lane Tracy
Ohio University
Athens, OH 45701 USA
Chaos theory and complexity theory grew from investigation of the behavior of nonlinear dynamical systems. These theories have been employed to explain the evolution of life (i.e., both autopoiesis and mutation), irregular patterns in life processes, consciousness, the behavior of crowds, economic patterns, war, and many other aspects of living systems. Integration of chaos and complexity theory into living systems theory (LST) would help to explicate certain processes that were not fully discussed by James G. Miller (1978) in the original presentation of the theory, and would highlight the dynamic nature of living systems. The purpose of this paper is to begin to integrate the theories of chaos and complexity into LST.

Keywords: chaos, complexity, living systems, evolution, autopoiesis

DYNAMIC NATURE OF LIVING SYSTEMS
Living systems theory (LST) has been criticized for being too equilibrium-oriented. It is true that LST, as developed by Miller (1978), treats many life processes in terms of maintenance of steady states. Yet living systems as a whole are seen as dynamic--growing, adapting, learning, moving, changing. Maintenance of steady states is only one of the metapurposes of living systems. Other metapurposes, actualization of the potential of the system and propagation of all or part of the system's template, conflict with maintenance. Processes such as growth and reproduction require that equilibrium be disturbed.

LST is also dynamic in another respect. Cells represent the lowest level of living systems, the level at which the basic characteristics of life emerge. The higher levels of living systems--organs, organisms, groups, organizations, communities, societies, and supranational systems--each evolved from the next lower level through a process which Miller calls fray-out (Miller & Miller 1990). Furthermore, the evolution from one level of living systems to a higher level involves an increase in complexity and the emergence of new characteristics.

LST was developed from an extensive study of the literature of a wide variety of disciplines in the biological and social sciences. The search was for parallels and common characteristics of systems at many levels. Structures and processes that could be found at all levels from cells to supranational systems would presumably constitute the essentials of life. They would be the characteristics that distinguish life from non-life.

Among the characteristics found at all levels of living systems are:

(a) They are open systems, with significant inputs, throughputs, and outputs of various sorts of matter-energy and information.
(b) They maintain a steady state of negentropy even though entropic changes occur in them....
(c) They have more than a certain minimum degree of complexity....
(d) They either contain genetic material composed of deoxyribonucleic acid (DNA)...or have a charter. One of these is the template...of their structure and process from the moment of their origin....
(e) They are largely composed of an aqueous suspension of macromolecules, proteins constructed from about 20 amino acids and other characteristic organic compounds, and may also include nonliving components.
(f) They have a decider, the essential critical subsystem which controls the entire system, causing its subsystems and components to interact....
(g) They also have certain other specific critical subsystems or they have symbiotic or parasitic relationships with other living or nonliving systems which carry out the processes of any such subsystem they lack.
(h) Their subsystems are integrated together to form actively self-regulating, developing, unitary systems with purposes and goals....
(i) They can exist only in a certain environment. Any change in their environment...outside a relatively narrow range which occurs on the surface of the earth, produces stresses to which they cannot adjust (Miller, 1987, 18).

Although these characteristics individually refer to steady states, structure, control, integration, self regulation, adjustment, and other aspects of equilibrium, collectively they describe systems that are highly complex, existing in a far-from-equilibrium state, constantly changing (albeit within limits), and strongly interactive both among their components and with their environment. Such systems are precisely the sorts that have been found to exhibit chaotic behavior.

CHAOS AND COMPLEXITY
Chaos theory is the study of regularity within irregularity, of predictability wrapped in unpredictability, of ordered disorder, of pattern arising from turbulence. It is also known as the theory of nonlinear dynamical systems, because nonlinear functions iterated over time produce results that appear at times to be chaotic and at other times to be stable.

Chaos was perhaps first noticed as a scientific phenomenon by Poincaré, who attempted to calculate the orbits of three or more interacting celestial objects and discovered that, except in special cases, it could not be done. The equations describing the motions of the objects were nonlinear and could not be solved directly. In some cases approximate solutions were possible through iteration of the calculations, but there was no general or exact solution. Thus Poincaré worried that the solar system was unstable. Nature was capable of creating chaos!

Another source of concern about chaos in nature was the weather. Attempting to create a computerized model of weather systems, Lorenz (1966) discovered the so-called "butterfly effect" or sensitivity to initial conditions, namely that very small differences in starting conditions can lead to very large differences in outcomes. Although Lorenz initially used a set of linear equations, he later found that a much smaller set of three nonlinear equations produced a better approximation of real weather conditions (Sparrow 1982). That such a small set of equations could generate a never-ending variety of complex outcomes was a revelation.

Rapid development of computer technology spurred investigations of the behavior of systems of nonlinear equations. By permitting huge numbers of iterations and improved means of plotting and displaying results, computers facilitated the study of oscillators, population biology, economic systems, heartbeat irregularities and a host of other problems that had resisted linear analysis. Meanwhile, fractal patterns such as the Mandelbrot set emerged to generate popular interest (Gleick 1987).

Chaos theorists have developed a number of important concepts for understanding and measuring complex systems. Attractors are mathematical structures that describe particular patterns in plotted results or in the motions of objects in space. Attractors represent one kind of order that is generated in nonlinear dynamical systems. Bifurcation occurs when results diverge into two or more pathways simultaneously or when there is a change from one attractor to another. Bifurcation may lead to chaos, but it may also lead to a return of order. Catastrophe theory studies discontinuous change from chaos to stability and back again. Catastrophe theory uses topology to examine the boundary conditions.

New kinds of measurements have emerged from the study of chaos. The number or frequency of iterations is important. Bifurcations often occur with increasing frequency or shorter periods. Fractional dimensionality is a measurement concept that has emerged from the study of geometric structures that are generated by nonlinear functions. Fractional dimensions measure the complexity of attractors.

Systems at the edge of chaos, systems that are far-from-equilibrium, exhibit self-organization. It is this self-organizing behavior that is the particular focus of complexity theory. While chaos theory analyzes the local interactions of positive and negative feedback that can produce both chaotic and stable outcomes, complexity theory is interested in the emergent global structure resulting from a large number of local interactions (Lewin 1992).

Complexity theory investigates the behavior of systems when they are far-from-equilibrium. Life is intrinsically a far-from-equilibrium phenomenon. As noted earlier, a defining characteristic of living systems is that "they maintain a steady state of negentropy" in a world in which entropy rules (Miller, 1978, 18). Complete equilibrium for a living system is death. Thus, there must be something in living systems that creates and maintains far-from-equilibrium states. That something is complexity. Through a complex, interactive flow of negative and positive feedback, often from nonlinear variables, living systems create and maintain a variety of relatively stable processes based on far-from-equilibrium values.

FITTING CHAOS INTO LIVING SYSTEMS THEORY
At the time that LST was being developed, chaos theory was in its gestation period. It had not yet coalesced into a coherent theory. Thus, LST was organized as a subset of general systems theory but without benefit of the concepts of chaos, complexity, and self-organization that are now prevalent.

Evolution And The Fray-Out Process

Theories of Evolution

The dominant theory of evolution of living systems was Darwin’s (1859) principle of natural selection and its extensions into the area of social systems (Spencer 1895; Gerard, Kluckhohn, & Rapaport 1956). Thus, Miller (1978) cites mutation and natural selection as the basic mechanisms by which living systems evolve within each level. Miller is aware that these mechanisms may not be sufficient. He notes Jacobson's (1955) calculation that in historical time these processes alone could not account for the level of complexity that exists in the human organism, but that this restriction could be circumvented through a process of parallel development of building blocks, a notion elaborated by Simon (1962). A prime example of this process would be the widely accepted theory that modern nucleated cell structure resulted from the symbiotic mating of cyanobacteria and oxygen-breathing bacteria (Margulis 1981).

Another approach to explaining rapid development of living systems is the concept of coevolution. Living systems do not evolve in isolation; they evolve through interaction with each other. A simple example would be the codevelopment of crops, domesticated animals, and societies. The evolution of maize into corn, of feral pigs into domesticated pigs, and of a hunter-gatherer band into an agricultural community occurred through interaction of the three lines of development. None of these changes would likely have happened without the others (Jantsch 1980). Once again we have order emerging as the result of interaction of complex, nonlinear systems.

Minimum Complexity

With respect to the requirement that a living system must "have more than a certain minimum degree of complexity," the question arises as to how a living system is able to generate the necessary degree of complexity. Miller's (1978) explanation is that there is a trend toward greater complexity through natural selection. Mutations may produce systems of greater, lesser, or equal complexity, but those of greater complexity will be more likely to survive because they have enhanced adjustment processes and can adjust better to stresses in their environment. McShea (1991) has shown, however, that complexity in biology is a very slippery concept and that it is very difficult to demonstrate any evolutionary trend toward increased complexity.

Fray-out and Evolution to Higher Levels

Although Miller provides little detail about the fray-out process by which new levels of living systems emerge, presumably the same mechanisms apply. The structure and process existing at a lower level are mapped onto a higher level of systems, but there must also be adaptation and elaboration to meet the needs of a new environment, greater size, and increased complexity. Are mutation and natural selection sufficient to account for the development of higher levels of living systems such as organizations and societies?

Chaos theory may provide a more satisfactory explanation of the rapid development of complex systems, namely through autopoiesis. That is, the potential for certain kinds of order or structure exists in the set of materials and interrelationships of a complex system. Mutations within such a system are not random, but are guided by nonlinear dynamical processes that permit order to emerge spontaneously. Such self-organization is particularly likely to occur when the system is far-from-equilibrium and at the edge of chaos (Kauffman 1993). Under such conditions of instability controls are relaxed and whatever can happen does happen. Furthermore, selection is accelerated. In a sense the potential within the system generates itself, but the potential stems from the chaos created by complex linkages of feedback from nonlinear variables.

What may be occurring in the evolution from one level of living systems to a higher level is a shift from one kind of an attractor to another. The principle of organization that holds together a cell may be different from that which governs the behavior of an organism or an organization. A shift from one attractor to another may occur at the edge of chaos, where complex systems can reorganize themselves.

Conversely, similar evolutionary developments may occur repeatedly because of a single attractor. For instance, various organisms may have developed eyes independently because of such a morphogenetic attractor (Goodwin 1992). In social systems an attractor may have caused language to develop independently in different populations. Goertzel (1995) has speculated that belief systems are attractors. A belief system tends to attract confirmatory data that help to stabilize the system.

Chaos theory has been applied to analysis of processes at every level of living systems. Through autopoiesis cells arose from the maelstrom of chemical materials available in the early history of earth. Organs such as the eye evolved around attractors. Organisms coevolved through chaotic interaction with each other and with their common environment. These levels have already been mentioned. Richards (1996) has applied chaos theory to an analysis of how groups emerge from a collection of individuals. Guastello (1995) has described a variety of applications at the levels of organizations, societies, and supranational systems. If Living Systems were being written today, it could hardly avoid making repeated reference to chaos and complexity.

Chaos In The Template And Decider

Templates and Charters

At first glance it would seem that the template or charter of a living system is the solid rock on which all else in the system is built. Genes contain the instructions for developing and governing the basic structure and process of a biological system. Statements of mission, principles, rules, and regulations perform a similar function for social systems. Yet recent developments suggest that genes do not act in a deterministic way. Rather, instructions may arise from the complex interaction of many genes. Thus, mapping of the human genome will not result in an ability to control human processes until we learn how genes interact. To the extent that the interaction is nonlinear or chaotic, it may not be possible to predict and control genetic processes completely.

Charters have similarly been viewed as deterministic. The development of bureaucracy was an attempt to stabilize organizations by means of a complex set of rules and regulations (Weber 1947). But the spontaneous eruption of self-rule among the members of bureaucratic organizations tended to defeat the purpose. More recently it has been found that looser controls coupled with commitment to the organization's mission are often more effective in maintaining stable processes. Effective organizations harness the power of chaos to make themselves more responsive and adaptive to their environment (Peters 1987).

Since its inception the field of organizational development has focused on changing the culture of an organization and making its decider subsystems more fluid and receptive to change. Networking has become a hallmark of modern organizations. All of these changes are moves away from stability and toward greater interaction among the component systems of the organization. The change process itself involves unfreezing current patterns (i.e., moving the organization toward the edge of chaos) and creating points of bifurcation (Guastello 1995).

Decider Subsystem and Consciousness

Miller (1978) views decision making as data processing. He assumes that a properly functioning decider subsystem is relatively stable, well organized, linear, and rational. Instability and irrationality are treated as pathologies. The concept of consciousness is not even mentioned. Yet consciousness and self-awareness are clearly implicit in Miller's discussion of decision making in human social systems.

The lack of a good theory of consciousness may have inhibited Miller from employing this concept, but complexity theory has brought us to a somewhat better understanding. Humphrey (1992), for instance, envisions consciousness as the sum total of sensations and their feedback loops, a view that would make the concept applicable to all living systems. Dennett (1991), on the other hand, sees consciousness as a higher-order phenomenon stemming from massive parallel processing in the brain. Parallel processing produces multiple drafts of reality, which are filtered into a single stream of consciousness. Others, such as Penrose (1989) and Churchland and Sejnowski (1992), see a need to go beyond the computational model and look for order emerging from the chaotic interactions of billions of firing neurons. Combs (1995) believes that autopoiesis plays a part in the emergence of consciousness. Various states of consciousness are attractors, which generate activities that tend to stabilize the current state.

The emerging view of the decider process is that it is chaotic, resulting from an extremely large number of interactions occurring in a nonlinear dynamical system. Order forms around attractors. Yet too much order results in pathologies such as schizophrenia and epilepsy (Briggs & Peat 1989) or groupthink (Janis 1982). Adding noise to the decider subsystem increases its capacity to respond to a complex environment (Breeden, Dinkelacker, & Hubler 1990).

Chaos In The Critical Subsystems And Components

The decider is not the only critical subsystem to which chaos theory has been applied. Catastrophe models have been formed to analyze variables such as stress, channel capacity, learning curves, and color perception in the information-processing subsystems (Guastello 1995). Physical stress on the matter-energy processing subsystems has likewise been subjected to catastrophe analysis. Catastrophe models have also been applied to immune processes in organisms and security processes in organizations, both found in the boundary subsystem (Guastello 1988). The memory process has been found to be distributed throughout the brain and seems to reside in the network of interconnections (Edelman 1981).

Specific components also exhibit chaotic behavior. A simple nonlinear mathematical model during periods when it becomes too chaotic can model the erratic eye movements of a schizophrenic. The normal heartbeat is a very complex set of interacting rhythms. Evidently an attractor is at work, coordinating the rhythms of various heart muscles. Pathologies such as tachycardia and fibrillation occur when the heart shifts to a new attractor or moves into a region of chaos (Winfree 1983).

People are the basic components of groups and organizations. Catastrophe theory has been used to model personnel selection, learning, work motivation, absenteeism, and turnover (Guastello 1995). Bifurcation and nonlinear models have been employed to understand the behavior of nations as components in supranational systems.

SUMMARY
If the literature search that led to living systems theory were being carried out today, some aspects of the theory would be quite different. Chaos theory has invaded the literature of the fields encompassed in LST. Chaos concepts would surface in the discussion of structure and process at each level of living systems. It is likely that there would be less emphasis on natural selection and greater mention of autopoiesis as mechanisms of evolution. The decider process would focus less on data processing and more on nonlinear aspects such as attractors, catastrophe, and bifurcation. Measurement concepts would be expanded from the traditional CGS system to include fractal dimensions. Gaia might be included among the levels of living systems, or at least discussed in terms of its coevolution with life forms (Lovelock 1979). Guastello (1995) has provided a good beginning in the process of updating LST to include chaos theory, but much more needs to be done.

It is less clear that a new look at the sources of LST would lead to greater consideration of the interactions between systems, subsystems, and components. LST focuses on concrete systems and on the delineation of those systems into living and nonliving, into hierarchies of suprasystem-system-subsystem, and into levels. When systems are viewed as components, for instance, it is assumed that they will play their assigned roles. Yet most components are living systems in their own right. They are subject to conflicting attractors. Their behavior may switch chaotically from playing a role in one system to carrying out the mandates of another or meeting their own needs. It is analysis of this interaction among systems that is most lacking in LST.

All living systems above the level of the single cell consist of a swarm of interacting living and nonliving systems. It is becoming increasingly apparent that all living systems are operating far-from-equilibrium, at the edge of chaos, with behavior that often can best be understood with the tools of chaos theory. To become a more useful model of the world LST must incorporate chaos theory at several levels. Complexity is found in specific structures and processes, in components and subsystems, in the coordinating efforts of the template and decider, and in the evolution of higher levels of living systems. Such complexity cannot be fully understood without the tools of chaos theory.

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