Research Details

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. These findings point to the importance of complexity and satisficing for decision making outside of the laboratory.

Type

Working Paper

Author(s)

Yuval Salant, Jorg Spenkuch

Date Published

2023

Citations

Salant, Yuval, and Jorg Spenkuch. 2023. Complexity and Satisficing: Theory with Evidence from Chess.

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