Imitation, network size, and efficiency

Abstract

A number of theoretical results have provided sufficient conditions for the selection of payoff-efficient equilibria in games played on networks when agents imitate successful neighbors and make occasional mistakes (stochastic stability). However, those results only guarantee full convergence in the long-run, which might be too restrictive in reality. Here, we employ a more gradual approach relying on agent-based simulations avoiding the double limit underlying these analytical results. We focus on the circular-city model, for which a sufficient condition on the population size relative to the neighborhood size was identified by Alós-Ferrer & Weidenholzer [(2006) Economics Letters, 93, 163–168]. Using more than 100,000 agent-based simulations, we find that selection of the efficient equilibrium prevails also for a large set of parameters violating the previously identified condition. Interestingly, the extent to which efficiency obtains decreases gradually as one moves away from the boundary of this condition.

Imitation and Local Interactions: Long Run Equilibrium Selection

“…A promising way of addressing this issue is to simulate the model. Two recent contributions along this strand of literature are Alós-Ferrer, Buckenmaier, and Farolfi [18,19]. As discussed in Section 2, the specification of the noise determines the outcomes of the model.…”