ISP President Ralph Widner has suggested that there are at least four big methodological challenges that we face when we apply panetic analysis to complex decision making or instances of human suffering:

(1) The Challenge of Complexity;

(2) The Challenge of Forseeability;

(3) The Challenge of Time Limits; and--

(4) The Challenge of Conflicting Perceptiuons and Values.

Dr. Song provides a "state of the art" summary of current approaches to complexity. He gives us a framework to address both the challenge of complexity and the challenge of conflicting perceptions and values. He focuses much of his attention on the pioneering work of Professor John Warfield, a member of the Board of Governors of the International Society for Panetics.

Introduction

Most of us remember when the Queen's banker, Barings, was brought down by the activities of one trader in a very short time; or when the Soviet Union disintegrated in 1991; or when many East Asian countries followed each other into financial crisis during 1997 and 1998.

Why did no one see these events coming? While quite different, they actually have quite a lot in common when you look more closely. Many independent agents were interacting with each other in a great many ways. Each of the agents was trying to turn whatever was happening to his advantage.

Every one of these cases involved a complex, self-organizing, adaptive “system” possessed of a kind of dynamism that makes them qualitatively different from static objects such as computer chips or snowflakes.

Complex systems are more spontaneous, more disorderly, more alive than that. Moreover every complex system is structurally nonlinear rather than linear.

Many of the complex decisions to which we might want to apply panetic analysis involve systems of this type. We must think very carefully about how panetic analysis can be applied in such situations. As Einstein once said, "without changing our pattern of thought, we will not be able to solve the problems we created with our current patterns of thought."

Let us review the schools of thought that have evolved to help us think about how to understand and manage complexity. Their origin can be dated back to the 19th century.

The physicist, Sadi Carnot, and other researchers realized it was both tedious and impractical to describe every interaction taking place in physical systems. Based on Newtonian concepts, system predictions became the law of thermodynamics. Their existence could explain the increase in temperature and pressure when gas molecules are heated in a container. But at the same time thermodynamics did not provide a complete description of the most complex interactions as, for example, in the case of gas molecules strongly attracted to one another.

Subsequently, Henri Poincare (1854-1912) realized that if a system consisted of a few parts that interacted strongly, it could exhibit unpredictable behavior. This concept lies at the origin of chaos theory. There have been many attempts to solve nonlinear dynamics problems so far.

As the Science of Complexity has evolved through the I9th and 20th century, five schools of thought about complexity have developed:

--System Dynamics founded by Jay Forrester atMIT.

--Chaos Theory developed by groups in many locations.

--Adaptive Systems formulated by the Santa Fe Institute

--Structure-based Science of Complexity developed by John N. Warfield.

Main Thoughts and Principals of the Schools System Dynamics

System Dynamics (SD)

Developed by Jay W. Forrester at the end of 1950s, System Dynamics is based on the theories of feedback, decision-making analysis, and simulation methodology. In the view of System Dynamics, every social and economic system is a feedback system the features of which are determined by its inner structure. The Club of Rome retained an MIT team in 1968 to project world limits to growth using SD. [This work will be reviewed in a separate paper.]

Chaos Theory

Meteorologist Edward Lorenz first perceived “chaos” as such back in 1960 while working on the problem of weather prediction. He intended to model the weather's behavior using a set of twelve equations with a computer. This led him to identify what came to be known as the “butterfly effect”, later to become the symbol of chaos theory.

Robert May, a biologist who became concerned with a problem that occurs with the projection of populations, found that a straight-line broke in two as soon as the growth rate passed a magnitude of three, meaning that instead of settling down to a single population projection, it would jump between two different ones from one year to another. Raising the value of the growth rate a little more caused it to jump between four different values. And as the parameter rose further the line bifurcated or doubled again. Bifurcation came faster and faster until chaos suddenly appeared.

After a certain value of growth rate is passed it becomes impossible to predict the population, a kind of chaos. Presently, chaos theory poses great challenges for traditional approaches to science. It has been extended as the “science of complexity”. Considering the range of scientific disciplines that have found points of resemblance and complementarity in these theories, this fresh way to proceed has produced valuable new insights into how our world and the universe operate.

Adaptive Systems Theory

The Santa Fe Institute (SFI) initiated research on adaptive system theory (CAS). The basic concept of CAS is that complexity originates from the adaptive initiatives of the active agents in a system. The chain of their cumulative interactions changes the system and its environment in ways unpredictable with older methodologies. The researchers at SFI developed a new methodology and a kind of software called SWARM. J. Holland developed a CAS model to simulate the behavior of general adaptive systems.The goal is to deal with the challenges of complexity through computer simulation.

Structure-Based Science of Complexity

As one of the schools of the Science of Complexity, Structure-Based Science of Complexity can be said to have its origins pretty far back in the history of science from the work of—

• Aristotle (384-322 BC, Greece) who created the concepts of category and the syllogism. Both of them are important for SBSC.

• Abelard (1079-1142, France) who articulated the generic form of the syllogism in a single prose proposition.

• Leibniz (1646- 1716, Germany) who applied graphical symbols to assist in the analysis and portrayal of logical relationships.

• Boole and De Morgan (1815-1864 & 1806-1871) who invented a calculus of propositions, a language of logic, and the theory of relations, the fundamental formal language.

• C. S. Peirce (1839-1914) who expanded and interpreted the theory of relations, and conceived and justified the philosophy of science.

• Harary (1921) who united several branches of mathematics to produce the analytical theory of structural models.

In order to make decisions about complex situations, John N. Warfield provided the enabling method to convert Harary's theory into practical applications. And then he conceived Structure-Based Science of Complexity (SBSC).

What is the Structured-Based Science of Complexity (SBSC)?

Definition 1: Complexity is that sensation experienced when, in observing or considering a system, frustration arises from lack of comprehension of what is being explored. Those who think that complexity is a property of what is being observed have to face up to the challenge of trying to find, in a multitude of observable systems, an attribute that is shared across all the many different types of observable systems. Even if that could be done, the question would remain about how they could do this for those systems that are merely being thought about, such as systems to be designed, which do not even have any observable character. And then they would also have to explain why some observable systems seem to be understood by some people, but not by others.

Because SBSC is founded on the assumption that the perception of complexity is a product of mind, analysis is done through groups bringing varied perceptions and values to the problem.

Definition 2 : Structured-Based Science of Complexity consists of the following components:

• Chronologies which add historical perspective to the context;

• Definitions which serve essentially as the suppositions or axioms upon which the science is founded;

• Laws of Complexity which advise on circumstances, which identify hurdles that methodology must overcome in order to avoid useless labor, and which point the way to enhancement of productivity;

• Metrics which permit numerical values to be attached to problematic situations for comparison with other problematic situations;

• Empirical Evidence, which helps establish the validity of the other components of SBSC.

In other words, SBSC is a science that recognizes the fundamental properties of complex individual, group and organizational behavior, the laws of complexity, the practical methodologies to make design decisions on complex issues, and guidance to resolve situations involved in complexity.

Philosophical Underpinnings

Because SBSC is based upon work of people in groups, the philosophy of SBSC was derived from an integrated interpretation of thoughts about behavioral pathologies—-individual, group and organizational. Individual behavioral pathologies were pointed out by Robert F. Bales (1951), Kenneth Boulding ( l 910-1994), Michel Foucault (1969-1993), George Miller (1956), Herbert A. Simon (1974), and Sir Geoffry Vickers (1894- 1982). Their opinions can be summarized and integrated as follows:

• The amount of information that can be managed in short term memory is limited;

• Inappropriate categories that are inadequate to the task at hand may be chosen;

• Individuals may disrupt group activity through exercise of emotional negatives;

• There may be mindless acceptance of received doctrine that biases an inquiry from the outset;

• There may be an inherent inability to allocate importance across members of a large set in the light of relative saliency;

• An attempt may be made to downgrade the language of science to suit individual preferences;

• There may be inadequate use of external learning adjuncts to compensate for mental limitations;

• There may be a lack of interest in the origins and trajectories of bodies of belief;

• There may be uncritical propagation of dysfunctional received doctrine;

• There may be a lack of self-recognition of physiologically based mental limitations when pressing personal beliefs on others;

• There may be excessive emphasis upon products of physical science when working with human systems;

• There may be self-generated action frameworks that may incorporate combinations of the foregoing.

Each participant brings to the group a variety of “Killer Assumptions” and each individual's own behavioral pathologies. As a result, new pathologies may be encountered that arise from the group.

They can be summarized and integrated as follows: “ Clanthink” (Warfield and Teigen, 1993) “ Spreadthink” (Warfield, 1995); and— “Underconceptualization.”

The cumulative effect of these escalates the difficulties in group work. The group is susceptible both to “groupthink” and to “clanthink”, either or both threatening the quality of the group product.

Add to that the common practice of failing to understand the importance of the working infrastructure when struggling with complexity, and there is already a tower of reasons to suppose that the group product cannot help resolve complexity.

Recognizing further that the language that is needed to portray complexity in any particular situation cannot be ad hoc, but must carefully evolve from belief about that situation as the group proceeds, and that must portray structural non-linearity; the work of ordinary groups, no matter how prominent and no matter how frequently occurring, can hardly be taken seriously by anyone who is seeking a modicum of understanding.

Organizational behavior pathologies were discovered by Argyris (1982), Anthony Downs (1966, 1994), Harold D. Lasswell (1960, 1963, 1971), and Herbert A. Simon (1955).

Careful design of a system to resolve complexity is required in order to be responsive to these collective pathologies, and to find ways to circumvent their mutually reinforcing negative effects. Thus the stress upon the SBSC Methodology. The Methodology of SBSC necessarily reflects careful consideration of behavioral pathologies and relies heavily upon an understanding of language requirements for representing complexity. In SBSC these ideas have been incorporated into what is called "The Work Program of Complexity (WPOC)".

The WPOC is a scientific interface between (a) the arena in which problematic situations arise that involve complexity and (b) the learning actions that take place enroute to resolving the problematic situation. A system of management that matches the requirements of the WPOC is called "Interactive Management (IM)" (Warfield and Cardenas, 1994). It has been heavily tested and revised for about two decades and has been found to be highly effective. “Discovery” reflects the idea that no one understands the complexity. A period of time devoted to “Discovery” is required for two reasons: first, to describe the situation; and second, to it. While the “Description” and “Diagnosis” are two tangible products of the “Discovery” component, the processes of arriving at these products are designed to resolve many of the issues related to behavioral pathologies; and also to assist in developing an appropriate object language with which to analyze, describe, and (re)design consideration. “Resolution” can be started once the “Discovery” work has produced sufficient understanding to make possible the design (or redesign) that is required in order to resolve the complexity associated with the problematic situation. “Resolution” incorporates recognition of need for resources for the purpose of implementing the design, and that such resources normally are found only in organizations, because of the size and scope of complexity.

Differences in Approach Among the Schools

As can be seen, there are big differences in approach between the schools of the science of complexity. First of all, SBSC assumes that complexity lies in the mind while the others suppose the complexity lies in the system observed. Secondly, SBSC is focused on how people can work effectively together to define, understand and resolve complex matters. System Dynamics, Chaos Theory and Adaptive System Theory try to explore the mechanism of chaos systems and then to control them.

Thirdly, SBSC is adequate to socio-technical systems that include organization and management systems, while Chaos theory is more suited for technology and physical systems.

SBSC offers a unique support system for developing an understanding of what is thought to be complex; and for arriving at a widely understood plan of action for resolving the complexity. Because of this uniqueness, and because there is little that is much more basic than thought as the entry point for resolving complexity, SBSC provides a basis for structuring collective belief that is more basic than products of other systems intended to support design and decision practices.

Applications of SBSC

There have been hundreds of examples of the application of SBSC over the past 20 years. In the United States, the Ford Motor Company is been the most frequent user. The Americans for Indian Opportunity have used it as their basic tool for planning and decision-making.In Mexico, the State of Guanajuato has used it in more than a hundred workshops on socio-economic problems there. In Japan, Hitachi has used it for internal planning. It has been used in Brazil for several economic projects.

Conclusion

SBSC is well suited to deal with two of the methodological challenges in applying panetic analysis. First, by drawing upon the cooperative work of a group, it enables us to bring all the differences in values and perceptions among social and political groups to bear upon the definition, discovery, analysis and resolution of a problem while screening out the individual, group and organizational behaviors that inhibit effective discovery and resolution.

Secondly, it provides the tools necessary to plumb the multiple levels of complex and unanticipated suffering that may flow from decisions made or yet to be made.

In designing future panetic research projects, SBSC should be considered seriously as a process component to deal with highly complex issues about which there are many perceptions and values.