Complexity and Satisficing: Theory and Evidence from Chess
We develop a model of satisficing with evaluation errors that incorporates complexity at the level of individual alternatives. In addition to making sharp predictions about the effect of complexity on choice probabilities, the theory yields a new empirical test that leverages complexity to distinguish satisficing from a large class of maximization-based choice procedures. We test and confirm the model predictions in a novel data set with information on hundreds of millions of chess moves by highly experienced players. We further document that skill and time moderate the adverse effect of complexity on the quality of decision-making, and that they complement each other in doing so. Our findings help to shed some of the first light on how complexity affects choice behavior outside of the laboratory.
Salant, Yuval, and Jorg Spenkuch. 2022. Complexity and Satisficing: Theory and Evidence from Chess.LINK