A Boundedly Rational Decision Algorithm
提出并检验了一个基于认知成本考虑的决策模型，该模型用算法模拟人们实际使用的心理捷径，能定量预测行为，替代理性人假设，并适用于树形决策问题。
Cognition requires scarce inputs, including time and concentration. Since cognition is costly, sophisticated decision-makers should use mental shortcuts, or heuristics, to reduce cognitive burdens. A model is proposed and tested that is motivated by these principles. It is believed this model achieves four goals. First, the model makes quantitative behavioral predictions and, hence, provides a precise alternative to the rational-actor hypothesis. Second, the model is psychologically plausible because it is based on the actual decisions algorithms that subjects claim to use. Third, the model is empirically testable; such a test is provided in this paper. Fourth, the model is broadly applicable, because it can be used to analyze decision problems that can be represented in tree form.