Algorithmic collusion and a folk theorem from learning with bounded rationality
证明了在有限理性玩家通过算法学习重复势博弈时，只要玩家有足够记忆、耐心且犯错少，就能学到接近指定收益的均衡策略，支持竞争当局对算法合谋的担忧。
We prove a Folk theorem when players with bounded rationality learn as they play a repeated potential game. We use a dynamic generalization of smooth fictitious play with bounded m -recall strategies to model learning with bounded rationality that is consistent with learning by algorithms. In a repeated potential game with perfect monitoring, we use this learning model to show that for any feasible and individually rational payoff profile, if players have sufficient recall, are sufficiently patient, and best respond with sufficiently few mistakes, then the players have a non-zero probability of learning an m -recall strategy profile that achieves an average payoff close to the specified payoff profile for an appropriate continuation game. Moreover, the strategy profile learned is an m -recall ϵ-subgame perfect equilibrium of the repeated game. This finding demonstrates that competition authorities are correct in their concern about algorithmic collusion.