论文标题
学习模型中的可观察性，优势和归纳
Observability, Dominance, and Induction in Learning Models
论文作者
论文摘要
学习模型通常并不意味着弱主导的策略是无关紧要的或证明了相关的“远期归纳”概念是合理的，因为理性的代理人可以将主导的策略用作实验来了解对手的玩法，并且可能没有足够的数据来排除对手从未使用过的策略。学习模型也不支持这样的想法，即所选的平衡只能仅取决于游戏的正常形式，即使两个具有相同正常形式的游戏表现出具有相同决策问题的玩家，鉴于对他人的玩法固定的信念。但是，玩游戏的广泛形式相当于使用适当的终端节点分区增强的正常形式，以便两种游戏是相当的，即玩家会收到有关他人策略的相同反馈。
Learning models do not in general imply that weakly dominated strategies are irrelevant or justify the related concept of "forward induction," because rational agents may use dominated strategies as experiments to learn how opponents play, and may not have enough data to rule out a strategy that opponents never use. Learning models also do not support the idea that the selected equilibria should only depend on a game's normal form, even though two games with the same normal form present players with the same decision problems given fixed beliefs about how others play. However, playing the extensive form of a game is equivalent to playing the normal form augmented with the appropriate terminal node partitions so that two games are information equivalent, i.e., the players receive the same feedback about others' strategies.
