Complex Adaptive Systems, Evolutionism, and Ecology within Anthropology: Interdisciplinary Research for Understanding Cultural and Ecological Dynamics

Article excerpt

We now know that far from equilibrium, new types of structures may originate spontaneously. In far-from-equilibrium conditions we may have transformation from disorder, from thermal chaos, into order. New dynamic states of matter may originate, states that reflect the interaction of a given system with its surroundings. We have called these new structures dissipative structures to emphasize the constructive role of dissipative processes in their formation.

-Prigogine and Stengers 1984:12

A Science of Complex Systems

Recently the ecologist C.S. Holling has discussed the conflict between "two streams of science" and the confusion it creates for politicians and the public (Holling 1995:12-16; see also Holling 1993:553-4). One stream is experimental, reductionist, and narrowly disciplinary. It is familiar to us as the scientific ideal. The less familiar stream is interdisciplinary, integrative, historical, analytical, comparative, and experimental at appropriate scales. Examples given of the first form are molecular biology and genetic engineering. The second form is found in evolutionary biology and systems approaches in populations, ecosystems, landscapes, and global dynamics. One stream is a science of parts, the other a science of the integration of parts.

Anthropology has held itself up to the first stream ideal of science. But the first stream ideal does not always produce the results in anthropology that proponents and critics alike have demanded. Our knowledge of detail is incomplete at societal scales, and prediction can fail. Disproof by experiment is unlikely even with "natural experiments". And unanimous agreement over results is almost never reached. One response by anthropologists has been to shrink temporal and spatial scales, and hold fast to the ideal; to let the requirements of the scientific methods of this first stream of science structure our research. Anthropologists are often dissatisfied with such restrictions on our object of study, but see little alternative if anthropology is to become a mature science.

But science itself is always evolving. Many anthropologists, both proponents of science and critics, are unaware of the constructive critiques now coming from the mature disciplines of science, from the "hard" sciences. For over twenty years scientists like Holling and Nobel prize chemist Ilya Prigogine (Prigogine 1980, Prigogine and Stengers 1984) have been arguing that the first stream of science is limited to certain problem sets. They contend that a science of complexity has fundamentally different features, and is the proper approach to other problem sets. The subject matter of anthropological inquiry, it will be shown, is commonly addressed in problem sets of the second type. In fact, anthropologists long have argued their case for understanding cultures in terms that sound remarkably like those advocated by the new science of complexity. We have been righting to resemble the ideal of science, while a second form is coming to look like us.

Points for Anthropology

Holling identifies a number of characteristics of the integrative stream of science. It incorporates technologies and results from reductionist, experimental science, but does not expect disproof by experiment and ultimate agreement by the scientific community. Models are multivariate and multi-scaled, and testing of alternative hypotheses is done by planned and unplanned interventions into whole systems in case studies, with the evaluation of the integrated consequence of each alternative. Multiple lines of converging evidence are used to argue for one hypothesis over another in a process of peer assessment and judgment.

While these ground rules for research might be revolutionary in the physical sciences, anthropology has long been forced by our subject into this type of science. Case study ethnographies are our hallmark. Experimental disproof is difficult and uncertainty is high. …


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