Behavior in Strategic Settings: Evidence from a Million Rock-Paper-Scissors Games
Behavior in Strategic Settings: Evidence from a Million Rock-Paper-Scissors Games
复制标题
战略设置中的行为：来自百万次石头剪刀布游戏的证据
作者：
D. Batzilis;Sonia Jaffe;S. Levitt;J. List;Jeffrey Picel
我们利用来自Facebook应用程序的数据，其中数十万人同时进行了一次零和游戏-石头-剪刀布-不同的信息来分析战略设置是否与现存的理论一致。我们报告三个主要的见解。首先，我们观察到，大多数人至少在某些时候采用与纳什一致的策略。第二，然而，玩家策略性地使用对手之前的信息，这是一种非纳什均衡行为;当这种行为的预期收益增加时，他们更有可能这样做。第三，经验很重要：经验丰富的玩家比经验不足的玩家更有效地利用对手的信息，因此更有可能获胜。我们还探讨了在何种程度上从纳什预测的偏差是一致的各种非均衡模型。我们分析了一个水平k框架和一个适应的量子响应模型。这些策略的简单版本--玩家最大化获胜的概率而不考虑失败的概率--比标准的公式更好。当一组人使用类似于量子反应的策略时，还有另一组人使用接近k1的策略;对于幼稚策略，后一组人要大得多。
We make use of data from a Facebook application where hundreds of thousands of people played a simultaneous move, zero-sum game—rock-paper-scissors—with varying information to analyze whether play in strategic settings is consistent with extant theories. We report three main insights. First, we observe that most people employ strategies consistent with Nash, at least some of the time. Second, however, players strategically use information on previous play of their opponents, a non-Nash equilibrium behavior; they are more likely to do so when the expected payoffs for such actions increase. Third, experience matters: players with more experience use information on their opponents more effectively than less experienced players, and are more likely to win as a result. We also explore the degree to which the deviations from Nash predictions are consistent with various non-equilibrium models. We analyze both a level-k framework and an adapted quantal response model. The naive version of each these strategies—where players maximize the probability of winning without considering the probability of losing—does better than the standard formulation. While one set of people use strategies that resemble quantal response, there is another group of people who employ strategies that are close to k 1 ; for naive strategies the latter group is much larger.