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.

中文翻译：

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模仿、网络规模和效率

当代理模仿成功的邻居并偶尔犯错误（随机稳定性）时，许多理论结果为在网络上玩的游戏中选择支付效率均衡提供了充分的条件。然而，这些结果只能保证长期的完全收敛，这在现实中可能过于严格。在这里，我们采用了一种更渐进的方法，依赖于基于代理的模拟，避免了这些分析结果背后的双重限制。我们专注于圆形城市模型，Alós-Ferrer 和 Weidenholzer [(2006) 确定了人口规模相对于社区规模的充分条件。经济学快报,93, 163–168]。使用超过 100,000 个基于代理的模拟，我们发现对于违反先前确定条件的大量参数，有效平衡的选择也很普遍。有趣的是，当一个人离开这个条件的边界时，效率获得的程度会逐渐降低。