Evolutionary game theory , learning , local interaction , networks
Abstract:
We study a model of local evolution. Agents are located on a network and interact strategically with their neighbours. Strategies are chosen with the help of learning rules that are based on the success of strategies observed in the neighbourhood. The standard literature on local evolution assumes learning rules to be exogenous and fixed. In this paper we consider a specific evolutionary dynamics that determines learning rules endogenously. We find with the help of simulations that in the long run learning rules behave rather deterministically but are asymmetric in the sense that while learning they put more weight on the learning players' experience than on the observed players' one. Nevertheless stage game behaviour under these learning rules is similar to behaviour with symmetric learning rules.
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