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How Is Cooperation/Collusion Sustained in Repeated Multimarket Contact with Observation Errors?

机译:如何在重复的多星形与观察误差接触中持续的合作/勾结?

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摘要

This paper analyzes repeated multimarket contact with observation errors where two players operate in multiple markets simultaneously. Multimarket contact has received much attention from the literature of economics, management, and information systems. Despite vast empirical studies that examine whether multimarket contact fosters cooperation/collusion, little is theoretically known as to how players behave in an equilibrium when each player receives a noisy observation of other firms' actions. This paper tackles an essentially realistic situation where the players do not share common information; each player may observe a different signal (private monitoring). Thus, players have difficulty in having a common understanding about which market their opponent should be punished in and when punishment should be started and ended. We first theoretically show that an extension of 1-period mutual punishment (1MP) for an arbitrary number of markets can be an equilibrium. Second, by applying a verification method, we identify a simple equilibrium strategy called "locally cautioning (LC)" that restores collusion after observation error or deviation. We then numerically reveal that LC significantly outperforms 1MP and achieves the highest degree of collusion.
机译:本文分析了与观察误差的重复多星形接触,其中两个玩家同时在多个市场中运行。 Multimarket联系人从经济学,管理和信息系统的文献中获得了很多关注。尽管探讨了多星形联系促进合作/勾结的巨大实证研究,但在理论上都知道玩家在每个玩家接受对其他公司行动的嘈杂观察时,玩家在均衡中的行为。本文解决了一个基本上现实的情况,球员不分享普通信息;每个玩家可以观察到不同的信号(私人监控)。因此,玩家难以遇到一个共同的理解,他们对他们的对手应该受到惩罚,当应该开始和结束惩罚时。我们首先理论上表明,任意数量的市场的1周期相互惩罚(1MP)的延伸可以是平衡的。其次,通过应用验证方法,我们确定一个称为“本地警告(LC)”的简单均衡策略,该策略在观察误差或偏差后恢复串行。然后,我们在数字上揭示了LC显着优于1MP并实现了最高的勾结程度。

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