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SOCGRAD MINI-LECTURES
by
A. Chaos and the Properties of Social Systems: If chaos is ubiquitous, and I think we will find that to be the case for social dynamics, then some of the ideas from Chaos theory would be most helpful to unraveling nonlinear social dynamics.
Some basic ideas:
1. The Bifurcation Map. A bifurcation map is a diagram of the pathway from order to disorder for all chaotic systems [remember a chaotic system is not really 'chaotic' in the lay sense...such systems display a lot of order... it's just that they are not easily detectable]. If bifurcation maps depict the progression from tightly ordered dynamics to much looser, less predictable regimes for such social data as
a) white collar crime,
b) teenage pregnancies,
c) gender relations or say,
d) kondratieff cycles, of boom and bust in the economy,
then we will be able to find out which [set] of key variables/parameters are involved in the transition from order to disorder. I.e., we might find out that three variables interact to produce new forms of white collar crime; new ways to do gender relations or new ways to do politics.
The thing about a bifurcation map is that the transformation from order to disorder follows an elegantly precise pattern...there are precise points at which new mixtures of order/disorder pop up...these are called feigenbaum points and serve to guide policy were such research designs in place...seems mysterious to me why there should be such regularity in the 'cascade' to disorder but those who work the field say so...
As disorder increases [at each bifurcation], causality fades and fails as does predictivity and, you will not be surprized, so too does the utility of planning and social control tactics...it will not escape your notice that the policy of building more prisons and using harsher sentences loses whatever efficacy it might have had when people live in more ordered, more predictable times.
2) Outcome basins...also called 'Attractors:' At each bifurcation, the number of end states/outcomes doubles.
In modern science, there is one and only one outcome 'basin' to which all 'normal' systems go...and they are supposed to behave very dependably as they go to their destiny. Not so in chaos/complex nonlinear dynamics. The same set of variables/parameters can produce two, four, eight or more basins of attraction...and, as the basins of attraction increase, the ability to predict the behavior of any system [nowise different from its mates] becomes less possible.
Thus, in a cohort of kids from Chicago, given small changes in key parameters [e.g., the virulence of racism or the unemployment percentages or the quality of a teacher in the third grade, some number of kids might go to college; some number to the state prison....it would be impossible to predict which kids go where but it would be fairly stable that a given number would go...stable, that is until the next bifurcation point on a key variable came round.
3) Feedback loops. In Chaos/complex dynamics, the concept of the feedback loop replaces the concept of causality. One can keep the notion of causality for simple systems exhibiting either positive or negative feedback. That is negative feedback pushes a system to extinction in a fairly smooth pathway.
Or positive feedback pushes a system to expand to fill the space available to it in well ordered pattern. But with complex systems, negative or positive feedback produces untrackable jumps, twists, turns and drops. Thus, if you think keynsian economics will work to produce, say, more housing by state subsidies to new families, you might find just the right results to confirm your expectation until a bifurcation point is reached, then more help from the state may produce a sharp drop in new housing starts...the point is then not to predict but to observe, model and, finding the chaotic regime in question, use a very light touch in matching that regime with its complementary regime.
A guy by the name of Hubler [umlaut U] at the Beckman Institute at U-Illinios has done some preliminary work on the management of chaos in deeply chaotic regimes. The point is that simple positive or negative feedback loops do not work to regulate complex systems...indeed, only nonlinear loops can stabilize chaotic regimes...an important variation on Ashby's Law of Requisite Variety.
Nonlinear feedback loops become very important in stabilizing all kinds of social behavior if this new way of understanding complex systems apply to societies...the curious person will want to know what form 'nonlinearity' takes for, say the control of crime. Positive feedback tends to explode the number of people in prisons...that is to say, the rationalization of police, courts, prisons and probation will soon fill up the prisons and jails...requiring the building of still more. So one need 'irrational' systems to control crime...what does that mean...well, it probably means that much of what people do that is illegal should be overlooked unless there is special reason to respond. It probably means that prison sentences should not be automatic nor prison sentences served in full.
It probably means that mercy is more useful than consistency when prevention of crime is of interest. The implications of Chaos/Complexity for social behavior are manifold. I expect that the next fifty years will bring us insights and understandings that elude us when we try to impose a linear causality on social dynamics....both Marx and Teilhard de Chardin noted that information allows human beings to escape the laws of nature and to augment human agency in the design and operation of social institutions. The new realms of information might well validate their view...we'll see. In my next posting, I will offer a couple ideas about falsification and its epistemological utility as well as some tentative conjectures about postmodern phil/sci grounded on this new approach....
B. In Non-Linear Social Systems [and almost all of them are non-linear], The Concept of Causality fails the knowledge process. As bifurcations increases, non-linearity explodes...and as non-linear dynamics increase, patterns loosen, predication fails and causality fades.
C. In Non-Linear Social Dynamics, new structures appear. Some of them become 'entrained' in a given eco-system. At this point, causality once again become increasingly useful to the knowledge process. Causality is entrained when other systems in the environment began to respond and change as a result of the new system. Thus new plants become the food of other organisms; new ways of organizing production begin to affect family and gender dynamics; new ways of communicating begin to affect economics and politics.
D. All this means that those research tactics which privilege truth, certainty and control thus become enemy to the knowledge process. Graduate Students set at the task of finding tight and stable correlations are, then, set upon a Fool's Errand.
E. Instead, the postmodern sociologist must:
1. Ascertain change points of key social variables.
2. Identify newly emerging structures [new ways of doing gender, doing ethnicity, doing class and doing religion.
3. Determine the fractal Structures of new systems [how much time-space they take of all possible time-space]
4. Determine how which and how many new systems are helpful to given social values [the concept of deviancy thus gives way to the new sciences of uncertainty].
5. Determine which complementary settings of non-linear feedback are useful in stabilizing or changing fractal structures.
F. Conclusion. The new sciences of chaos and complexity are a great challenge to graduate students in sociology. There is much to do and much to learn before we can have better understanding of nature and society. This mini-lecture does not begin to answer to an adequate postmodern sociology...but it is a start.
work hard, have fun, enjoy life and help the next generation of children, TR