Decision Rules in Choice Under Risk

We study choice among lotteries in which the decision maker chooses from a small library of decision rules. At each menu, the applied rule must make the realized choice a strict improvement under a dominance benchmark on perceived lotteries. We characterize 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.