The ecological rationality of heuristics has been extensively investigated in the domain of individual decision making. In strategic decision making, however, the focus has been on repeated games, and there is a lack of research on 1-shot games, where opponents and the game itself can vary from one interaction to another. Mapping the performance of simple versus more complex decision policies (or strategies) from the experimental game theory literature is an important first step in this direction. We investigate how 10 policies fare conditional on strategic properties of the games and 2 classes of uncertainty. The strategic properties are the complexity (number of actions) and the degree of harmony (competitiveness) of the games. The first class of uncertainty is environmental (or payoff) uncertainty, arising from missing payoff values. The second class is strategic uncertainty about the type of opponent a player is facing. Policies' performance was measured by 3 criteria: a mean criterion averaging over the whole set of opponent policies, a maxmin criterion capturing the worst-case scenario and another criterion measuring robustness to different distributions of opponent policies. Heuristics performed well and were more robust than complex policies such as pure-strategy Nash equilibria, while simultaneously requiring significantly less information and fewer computational resources. Our ranking of the decision policies' performance was closely aligned to their prevalence in experimental studies of games. In particular, the Level-1 policy, which completely ignores an opponent's payoffs and uses equal weighting to determine the expected payoffs of different actions, exhibited a robust beauty. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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不确定战略世界的生态理性启发法地图。

启发式方法的生态合理性已在个人决策领域进行了广泛研究。但是，在战略决策中，重点一直放在重复比赛上，而对于单发游戏却缺乏研究，在这种情况下，对手和游戏本身可能因一次互动而异。从实验博弈论文献中绘制简单决策策略与更复杂决策策略（或策略）的性能图，是朝这个方向迈出的重要第一步。我们研究了10项政策的收费如何取决于游戏的战略属性和2种不确定性。战略属性是游戏的复杂性（动作数）和和谐程度（竞争性）。第一类不确定性是环境（或收益）不确定性，它是由缺失的收益值引起的。第二类是关于玩家所面对的对手类型的战略不确定性。策略的绩效由3个标准衡量：平均标准是对整个对等策略的平均值，最大最小值是捕获最坏情况的准则，另一个是衡量对不同策略的鲁棒性的标准。启发式算法表现良好，并且比诸如纯策略Nash均衡之类的复杂策略更强大，同时还需要更少的信息和更少的计算资源。我们对决策政策绩效的排名与其在游戏实验研究中的普遍程度紧密相关。特别是，第1级政策会完全忽略对手的收益，并使用相等的权重来确定不同动作的预期收益，表现出强大的美感。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。