有限理性下的决策与绩效：一种计算基准方法
Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach
摘要：
本文提出了一种新的方法来分析人类的决策，包括比较职业棋手的行为相对于认知有限理性的计算基准。该基准是使用现代国际象棋引擎的算法构建的，并允许在单个移动观察水平上调查行为，从而代表了计算界优化的自然基准。分析提供了新的见解，隔离偏离这个基准的有限理性，以及他们的原因和后果的性能。研究结果证明存在几个不同的维度的行为偏差，这与不对称的位置评估方面的损失和收益，时间压力，疲劳和复杂性。结果还表明，偏离基准并不一定会导致更差的性能。更快的决策与更频繁地偏离基准相关，但它们也与更好的性能相关。这些发现与直觉和经验的重要影响是一致的，从而为最近关于认知过程中计算合理性的争论提供了新的线索。
英文摘要：
This paper presents a novel approach to analyze human decision-making that involves comparing the behavior of professional chess players relative to a computational benchmark of cognitively bounded rationality. This benchmark is constructed using algorithms of modern chess engines and allows investigating behavior at the level of individual move-by-move observations, thus representing a natural benchmark for computationally bounded optimization. The analysis delivers novel insights by isolating deviations from this benchmark of bounded rationality as well as their causes and consequences for performance. The findings document the existence of several distinct dimensions of behavioral deviations, which are related to asymmetric positional evaluation in terms of losses and gains, time pressure, fatigue, and complexity. The results also document that deviations from the benchmark do not necessarily entail worse performance. Faster decisions are associated with more frequent deviations from the benchmark, yet they are also associated with better performance. The findings are consistent with an important influence of intuition and experience, thereby shedding new light on the recent debate about computational rationality in cognitive processes.
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