演講者簡介 : Professor Yuval Salant received his PhD in Economic Analysis and Policy from Stanford University in 2008. He is currently a Professor of Managerial Economics and Decision Sciences at Northwestern University. His research interests are Foundations of Behavioral Economics, Bounded Rationality, Economics and Statistics, and Choice Theory.
演講摘要 : We develop a model of satisficing in which the complexity of the alternatives available for choice is assumed to lead to errors in the perception of value. This assumption is confirmed in an experiment we conduct. The satisficing model yields sharp predictions about the effect of complexity on choice probabilities, which qualitatively depart from the predictions of maximization-based choice models. We test and confirm the predictions of satisficing, and hence reject the predictions of maximization, in a novel data set with information on hundreds of millions of chess games by highly experienced players. Our findings point at the importance of complexity and satisficing outside the laboratory.
