Distilling Models of Bounded-Rational Choice: A Constraint Programming Approach

2026-07-04

AI Summary

This study addresses a central challenge in behavioral economics: extracting boundedly rational models that are both descriptively accurate and welfare-relevant from computationally complex and imperfectly rational choice behavior. The authors introduce techniques from constraint programming and combinatorial optimization to jointly analyze two prominent classes of bounded rationality models—“consideration set” and “limited attention” frameworks—and propose selection criteria that substantially reduce predictive ambiguity. Using real-world human choice data, the analysis reveals that limited attention models exhibit superior explanatory power and greater inclusiveness. Moreover, combining both model classes accounts for nearly all observed choices and significantly narrows welfare-relevant prediction intervals, thereby enhancing the practical applicability of these models for policy and welfare analysis.

Abstract

We provide an analytical framework that allows for distilling the full explanatory and welfare-relevant content of influential yet computationally hard models of bounded-rational general choice. We do so by introducing constraint programming methods and tools from the optimization literature. We focus on the prominent "shortlisting" and "limited-attention" models. Applying our framework on imperfectly rational human choice data, we find that these models jointly account for nearly all behaviors, with limited-attention ones explaining better while being more permissive. Selection criteria that we introduce narrow down the models' welfare-relevant predictions, considerably alleviating their indeterminacy and contributing toward their practical applicability.

Problem

• bounded-rational choice
• shortlisting
• limited-attention
• welfare-relevant predictions
• model indeterminacy

Innovation

• constraint programming
• bounded rationality
• limited attention
• shortlisting
• welfare analysis