论文标题
顺序采样平衡
Sequential Sampling Equilibrium
论文作者
论文摘要
本文介绍了一个基于顺序抽样的平衡框架，在该框架中，玩家对对手的行为面临战略性不确定性，并获得了信息的信号来解决它。顺序采样平衡提供了一个纪律模型，具有内源性，信念和决策时间的内源性分布，不仅使知名的NASH均衡偏差合理化，而且还对现有数据支持的新预测进行了新的预测。它基于经验学习与战略复杂性之间的关系，并通过固有的采样固有的随机性产生随机选择，而不依赖于冷漠或选择错误。此外，当采样成本消失时，它为NASH平衡提供了理由。
This paper introduces an equilibrium framework based on sequential sampling in which players face strategic uncertainty over their opponents' behavior and acquire informative signals to resolve it. Sequential sampling equilibrium delivers a disciplined model featuring an endogenous distribution of choices, beliefs, and decision times, that not only rationalizes well-known deviations from Nash equilibrium, but also makes novel predictions supported by existing data. It grounds a relationship between empirical learning and strategic sophistication, and generates stochastic choice through randomness inherent to sampling, without relying on indifference or choice mistakes. Further, it provides a rationale for Nash equilibrium when sampling costs vanish.
