Strategy sets closed under payoff sampling
研究了程序理性玩家通过随机匹配测试各策略并选择最优的收益抽样动态，刻画了该动态在所有博弈中静止点的支撑集，并分析了其所属面的渐近稳定性。
We consider population games played by procedurally rational players who, when revising their current strategy, test each of their available strategies independently in a series of random matches –i.e., a battery of tests–, and then choose the strategy that performed best in this battery of tests. This revision protocol leads to the so-called payoff-sampling dynamics (aka test-all Best Experienced Payoff dynamics). In this paper we characterize the support of all the rest points of these dynamics in any game and analyze the asymptotic stability of the faces to which they belong. We do this by defining strategy sets closed under payoff sampling, and by proving that the identification of these sets can be made in terms of simple comparisons between some of the payoffs of the game.