Collective Intelligence in Evolving Systems

Abstract

Ecosystems, comprising diverse living beings with complex, ever changing, compartments and interrelationships, cannot be handled sufficiently by the same kind of models as mechanical systems. Static or equilibrium models may describe short term adaptation phenomena adequately, but in the long term the openness of ecosystems allows them to reach ever new states and structures. Only evolutionary paradigmata can help in understanding ecodynamics and in developing adequate adaptive management strategies. This has been emphasized by biologists like Dobzhansky C1] as well as social scientists like Boulding [21 Eigen and Winkler-Oswatitsch [3], moreover, have shown how to interprète natural phenomena in the framework of evolutionary chance- and-necessity games. Even if the model, presented here, arose from the inverse goal to use nature’s learning strategy for technical meliorization, it may serve as well to learn about the learning process of ecosystems by comparing the effectiveness of variants of the evolutionary strategies. There have been several attempts to do so [4, 5, 6, 7]