Revealed Decision Rules in Choices Under Risk

Avner Seror

Papers from arXiv.org

Abstract: We study decision making under risk when perception may be menu-dependent. Behavior is modeled as the outcome of a small library of psychologically interpretable, menu-specific rules that transform each objective menu into a perceived one. At each menu, the applied rule must make the realized choice a strict improvement under a dominance benchmark on perceived lotteries. We introduce the Maximum Rule Concentration Index (MRCI), the maximal Herfindahl-Hirschman concentration of rule shares over all locally admissible assignments, and diagnostics that distinguish rules that unify behavior across many menus from rules that mainly act as substitutes. We provide a MIQP formulation, a scalable heuristic, and a finite-sample permutation test of excess concentration relative to a menu-independent random-choice benchmark. Applied to the CPC18 dataset (N=686 subjects, each making 500-700 repeated binary lottery choices), the mean MRCI is 0.545, and 64.1% of subjects reject random choice at the 1% level. Concentration gains are primarily driven by modal-payoff focusing, salience-thinking, and regret-based comparisons.

Date: 2026-01
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