Complexity and Satisficing: Theory with Evidence from Chess
构建了一个考虑选项复杂性的满意化选择模型，通过国际象棋棋手数百万步真实棋步数据验证了模型预测，并拒绝了最大化模型，为将复杂性纳入选择模型提供了蓝图。
Abstract We develop a satisficing model of choice in which the available alternatives differ in their inherent complexity. We assume—and experimentally validate—that complexity leads to errors in the perception of alternatives’ values. The model yields sharp predictions about the effect of complexity on choice probabilities, some of which qualitatively contrast with those of maximization-based choice models. We confirm the predictions of the satisficing model—and thus reject maximization—in a novel data set with information on hundreds of millions of real-world chess moves by highly experienced players. Looking beyond chess, our work offers a blueprint for incorporating complexity at the level of individual objects into models of choice and for detecting satisficing outside of the laboratory.