Summary
This paper addresses several social concerns: population trends; quality of urban life; policies for urban growth; and the unexpected, ineffective, or detrimental results often generated by government programs. Society becomes frustrated as repeated attacks on deficiencies in social systems lead only to worse symptoms. Legislation is debated and passed with great hope, but many programs prove to be ineffective. Results are often far short of expectations. Because dynamic behavior of social systems is not understood, government programs often cause exactly the reverse of desired results. The field of system dynamics now can explain how such contrary results happen. Fundamental reasons cause people to misjudge behavior of social systems. Orderly processes in creating human judgment and intuition lead people to wrong decisions when faced with complex and highly interacting systems. Until we reach a much better public understanding of social systems, attempts to develop corrective programs for social troubles will continue to be disappointing. This paper cautions against continuing to depend on the same past approaches that have led to present feelings of frustration. New methods developed over the last 30 years will lead to a better understanding of social systems and thereby to more effective policies for guiding the future.
Relevance for Complex Systems Knowledge
What justification can there be for assuming that we do not know enough to construct models of social systems but believe we do know enough to directly redesign social systems by passing laws and starting new programs?
Mental models are fuzzy, incomplete, and imprecisely stated. Furthermore, within a single individual, mental models change with time, even during the flow of a single conversation. Even when only a single topic is being discussed, each participant in a conversation employs a different mental model to interpret the subject. Fundamental assumptions differ but are never brought into the open. The human mind is not adapted to understanding correctly the consequences implied by a mental model. A mental model may be correct in structure and assumptions but, even so, the human mind--either individually or as a group consensus--is apt to draw the wrong implications for the future. Inability of the human mind to use its own mental models becomes clear when a computer model is constructed to reproduce the assumptions contained in a person’s mental model. The computer model is refined until it fully agrees with the perceptions of a particular person or group. Then, usually, the system that has been described does not act the way the people anticipated.
By contrast to mental models, system dynamics simulation models are explicit about assumptions and how they interrelate. Any concept that can be clearly described in words can be incorporated in a computer model. System dynamics models are not derived statistically from time-series data. Instead, they are statements about system structure and the policies that guide decisions. Models contain the assumptions being made about a system. A model is only as good as the expertise which lies behind its formulation. A good computer model is distinguished from a poor one by the degree to which it captures the essence of a system that it represents. Many other kinds of mathematical models are limited because they will not accept the multiple-feedback-loop and nonlinear nature of real systems.
System dynamics models show how difficulties with actual social systems arise, and demonstrate why so many efforts to improve social systems have failed. Models can be constructed that are far superior to the intuitive models in people’s heads on which national social programs are now based. System dynamics differs in two important ways from common practice in the social sciences and government. Other approaches assume that the major difficulty in understanding systems lies in shortage of information and data. Once data is collected, people have felt confident in interpreting the implications. I differ on both of these attitudes. The problem is not shortage of data but rather inability to perceive the consequences of information we already possess. The system dynamics approach starts with concepts and information on which people are already acting. Generally, available information about system structure and decision-making policies is sufficient. Available information is assembled into a computer model that can show behavioral consequences of well-known parts of a system. Generally, behavior is different from what people have assumed.
In a troubled company, people are usually trying in good conscience and to the best of their abilities to help solve the major difficulties. Policies are being followed that they believe will alleviate the difficulties. One can combine the stated policies into a computer model to show the consequences of how the policies interact with one another. In many instances it emerges that the known policies describe a system which actually causes the observed troubles.
Many characteristics of social systems mislead people. Behavior that people do not anticipate appears in corporate and urban systems and in world-wide pressures now enveloping the planet. Three counterintuitive behaviors of social systems are especially dangerous.
First, social systems are inherently insensitive to most policy changes that people choose in an effort to alter the behavior of systems. In complex dynamic systems, causes are often far removed in both time and space from the symptoms. However, the complex system can mislead in devious ways by presenting an apparent cause that meets the expectations derived from simple systems. The apparent causes are usually coincident occurrences that, like the trouble symptom itself, are being produced by the feedback-loop dynamics of a larger system.
Second, social systems seem to have a few sensitive influence points through which behavior can be changed. These high-influence points are not where most people expect. Contrary to intuitive expectations, the opposite of present practice may actually raise the quality of life
Third, social systems exhibit a conflict between short-term and long-term consequences of a policy change. A policy that produces improvement in the short run is usually one that degrades a system in the long run. Likewise, policies that produce long-run improvement may initially depress behavior of a system. Many problems being faced today are the cumulative result of short-run measures taken in prior decades.
After modelling some scenarios on natural resource depletion and pollution with their connections to population and economic and technological growth, Forrester concludes that there are no utopias in social systems. There appear to be no sustainable modes of behavior that are free of pressures and stresses. But many modes of behavior are possible and some are more desirable than others. The more attractive behaviors in social systems seem possible only if we act on a good understanding of the dynamic behavior of systems and are willing to endure the self-discipline and short-term pressures that will accompany the route to a desirable future.
Implications for action must be examined more deeply and confirmed by more research into the assumptions about structure and detail of the world system. Some of Forresters observations are:
(a)
Industrialization may be a more fundamentally disturbing force in world ecology than is population. In fact, the population explosion is perhaps best viewed as a result of technology and industrialization. I include medicine and public health improvements as a part of the industrialization that has led to rising population.
(b)
Within the next century, the world will be facing a four-pronged dilemma—suppression of modern industrial society by a natural resource shortage, collapse of world population from changes wrought by pollution, population limitation by food shortage, or population control by war, disease, and social stresses caused by physical and psychological crowding.
