How Many Mechanisms? Measuring Parsimony in Risky Choice

TL;DR

This paper introduces a new index to measure how simply risky choice data can be explained by a small set of behavioral decision rules, revealing common decision patterns.

Contribution

It develops the Maximum Rule Concentration Index to quantify parsimony in risky choice models using canonical behavioral theories and applies it to real datasets.

Findings

Most subjects' choices are more parsimoniously explained than standard utility models suggest.

Data show decision patterns around salience, modal-payoff focusing, and regret.

The index detects significant parsimony in three lottery-choice datasets.

Abstract

Behavioral theories rest on parsimony: a small number of mechanisms organizing many decisions. We define a Maximum Rule Concentration Index that measures how parsimoniously a dataset of risky choices can be organized through a library of simple, parameter-free decision rules drawn from canonical behavioral theories: salience, regret, disappointment, modal-payoff focusing, extreme-outcome screening, and limited attention. Applied to three lottery-choice datasets, the data exhibit detectable parsimony: for a majority of subjects, observed concentration exceeds what standard utility models generate on the same menus. The concentration organizes around salience thinking, modal-payoff focusing, and regret.