BURTON VOORHEES () (Center for Science, Athabasca University, Canada) JOSEPH SENEZ (Center for Science, Athabasca University, University of Alberta Medical School, Canada) TODD KEELER (Department of Mathematics, Simon Fraser University, Canada) MARTIN CONNORS (Center for Science, Athabasca University, Canada)
Abstract
We present a population model illustrating the concept of virtual stability, i.e. the idea that complex adaptive systems with the capacity for self-monitoring and adaptive control are able to maintain themselves in states that would otherwise be unstable. The advantage gained from this is increased behavioral flexibility in the face of random environmental perturbations. In the model presented, transition probabilities between three population types are used to emulate stability: stable types have low probabilities of making transitions to other types, and unstable types have high transition probabilities. The model itself consists of two stable types and one unstable type, and conditions are explored that lead to dominance by the unstable type. Under certain conditions the unstable type can defeat a stable type, even in an environment that always favors the stable type.
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