Numerous researchers have put forward heuristics as models of human decision-making. However, where such heuristics come from is still a topic of ongoing debate. In this work, we propose a novel computational model that advances our understanding of heuristic decision-making by explaining how different heuristics are discovered and how they are selected. This model—called bounded meta-learned inference (BMI)—is based on the idea that people make environment-specific inferences about which strategies to use while being efficient in terms of how they use computational resources. We show that our approach discovers two previously suggested types of heuristics—one reason decision-making and equal weighting—in specific environments. Furthermore, the model provides clear and precise predictions about when each heuristic should be applied: Knowing the correct ranking of attributes leads to one reason decision-making, knowing the directions of the attributes leads to equal weighting, and not knowing about either leads to strategies that use weighted combinations of multiple attributes. In three empirical paired comparison studies with continuous features, we verify predictions of our theory and show that it captures several characteristics of human decision-making not explained by alternative theories.

中文翻译：

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来自有界元学习推理的启发式方法。

许多研究人员提出启发式作为人类决策的模型。然而，这种启发式从何而来仍然是一个争论不休的话题。在这项工作中，我们提出了一种新颖的计算模型，通过解释如何发现不同的启发式方法以及如何选择它们来加深我们对启发式决策的理解。这种称为有界元学习推理 (BMI) 的模型基于这样一种想法，即人们可以针对特定环境做出关于使用哪些策略的推理，同时在他们如何使用计算资源方面更加高效。我们表明，我们的方法在特定环境中发现了两种先前建议的启发式类型——一个原因决策和同等权重。此外，该模型提供了关于何时应应用每种启发式方法的清晰准确的预测：知道属性的正确排序会导致决策制定的一个原因，知道属性的方向会导致相等的权重，而不知道任何一个会导致使用多个属性的加权组合的策略。在三项具有连续特征的实证配对比较研究中，我们验证了我们理论的预测，并表明它捕捉了人类决策的几个特征，而替代理论无法解释。