Working Paper: NBER ID: w30002
Authors: Yuval Salant; Jorg L. Spenkuch
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.
Keywords: No keywords provided
JEL Codes: D00; D01; D03; D9; D90
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| increased complexity of high-value alternative (D81) | choice probabilities of other alternatives (D79) |
| complexity of winning moves (C72) | choice probabilities (C25) |
| complexity of losing moves (C73) | choice probabilities (C25) |
| maximization behavior statistically rejected (C52) | prevalence of satisficing behavior (D91) |
| complexity (C60) | evaluation errors (C52) |
| complexity (C60) | accuracy of value perception (D46) |
| object complexity (C60) | response times (C41) |