A Markov state modelling approach to characterizing the punctuated equilibrium dynamics of stochastic evolutionary games

Abstract

Stochastic evolutionary games often share a dynamic property called punctuated equilibrium; this means that their sample paths exhibit long periods of stasis near one population state which are infrequently interrupted by switching events after which the sample paths stay close to a different population state, again for a long period of time. This has been described in the literature as a favorable property of stochastic evolutionary games. The methods used so far in stochastic evolutionary game theory, however, do not fully characterize these dynamics. We present an approach that aims at exposing the punctuated equilibrium dynamics by constructing Markov models on a reduced state space which approximate well this dynamic behavior. Besides having good approximation properties, the approach allows a simulation-based algorithm, which is appealing in the case of complex games.