Disentangling strict and weak choice in random expected utility models
研究了随机期望效用最大化与打破平局规则下的随机选择结构，给出了部分识别结果，并刻画了一类能最小化无差异情形的随机选择规则。
I examine the structure of random choice resulting from random expected utility maximization and a tie-breaking rule. I provide a partial identification result, characterizing the set of random expected utility models that could have generated the observed choice frequencies. I then consider a particular class of random choice rules for which it is possible to constructively identify the consistent random utility model that produces indifference the least. These random choice rules are characterized by breaking ties in favor of strict convex combinations. Towards proving these results, I introduce and axiomatize the notion of a choice capacity, representing the frequency of choice by strict maximization. Choice capacities, while not necessarily observable themselves, provide the technical machinery to translate arbitrary random expected utility models into choice behavior.