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We study the distribution of strategies in a large game that models howagents choose among different double auction markets. We classify the possiblemean field Nash equilibria, which include potentially segregated states wherean agent population can split into subpopulations adopting differentstrategies. As the game is aggregative, the actual equilibrium strategydistributions remain undetermined, however. We therefore compare with theresults of Experience-Weighted Attraction (EWA) learning, which at long timesleads to Nash equilibria in the appropriate limits of large intensity ofchoice, low noise (long agent memory) and perfect imputation of missing scores(fictitious play). The learning dynamics breaks the indeterminacy of the Nashequilibria. Non-trivially, depending on how the relevant limits are taken, morethan one type of equilibrium can be selected. These include the standardhomogeneous mixed and heterogeneous pure states, but also \emph{heterogeneousmixed} states where different agents play different strategies that are not allpure. The analysis of the EWA learning involves Fokker-Planck modeling combinedwith large deviation methods. The theoretical results are confirmed bymulti-agent simulations.

Dynamical selection of Nash equilibria using Experience Weighted Attraction Learning: emergence of heterogeneous mixed equilibria