Stochastic Learning and the Evolution of Conventions

This paper shows that aspiration driven, stochastic learning processes can enable the members of a population to reach more efficient conventions, which are modeled as equilibria of a coordination game. As opposed to adaptation processes based on either myopic optimisation or imitatiing behaviour, which generally select the risk dominant equilibrium, the present paper provides conditions under which the efficient equilibrium is selected, even if it is risk dominated by another equilibrium. Further, the impact of players' aspiration levels on the existence and local stability of a convention is analysed. In general, higher aspirations induce more efficient outcomes. In addition, in the case of a local interaction structure there exist an inverse relation between the size of the individual neighbourhoods and the aspiration level required in order to sustain efficient outcomes