Evolutionary Stochastic Games

We extend the notion of Evolutionarily Stable Strategies introduced by Maynard Smith and Price (Nature 246:15–18, <CitationRef CitationID="CR6">1973</CitationRef>) for models ruled by a single fitness matrix A, to the framework of stochastic games developed by Lloyd Shapley (Proc. Natl. Acad. Sci. USA 39:1095–1100, <CitationRef CitationID="CR13">1953</CitationRef>) where, at discrete stages in time, players play one of finitely many matrix games, while the transitions from one matrix game to the next follow a jointly controlled Markov chain. We show that this extension from a single-state model to a multistate model can be done on the assumption of having an irreducible transition law. In a similar way, we extend the notion of Replicator Dynamics introduced by Taylor and Jonker (Math. Biosci. 40:145–156, <CitationRef CitationID="CR16">1978</CitationRef>) to the multistate model. These extensions facilitate the analysis of evolutionary interactions that are richer than the ones that can be handled by the original, single-state, evolutionary game model. Several examples are provided. Copyright Springer Science+Business Media New York 2013