Perfectly rational decision making is almost always out of reach for people because their computational resources are limited. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging. Here, we present a theoretical framework that allows us to use methods from machine learning to automatically derive the best heuristic to use in any given situation by considering how to make the best use of limited cognitive resources. To demonstrate the generalizability and accuracy of our method, we compare the heuristics it discovers against those used by people across a wide range of multi-attribute risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although their strategy choices do not always fully exploit the structure of the environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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识别风险选择的资源理性启发法。


由于计算资源有限，人们几乎总是无法做出完全理性的决策。相反，人们可能依赖计算上节俭的启发式方法，通常会产生良好的结果。尽管之前的研究已经发现了许多此类启发式方法，但发现好的启发式方法并预测何时使用它们仍然具有挑战性。在这里，我们提出了一个理论框架，允许我们使用机器学习的方法，通过考虑如何充分利用有限的认知资源，自动得出在任何给定情况下使用的最佳启发式方法。为了证明我们的方法的普遍性和准确性，我们将它发现的启发式方法与人们在行为实验中在各种多属性风险选择环境中使用的启发式方法进行比较，该行为实验比以前任何同类实验都要大一个数量级。我们的方法重新发现了已知的启发式方法，将它们识别为针对特定环境的理性策略，并发现了以前被忽视的新颖启发式方法。我们的结果表明，人们会根据环境结构调整他们的决策策略，并且通常会充分利用他们有限的认知资源，尽管他们的策略选择并不总是充分利用环境结构。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。