IN THIS SUPPLEMENT, we provide several supporting results that are used in the main paper. In Section S.1, we provide a general local expected-utility result for mixture-averse preferences that nests Proposition 1 in the main paper as a special case. In Section S.2, we establish the relationship between mixture-averse preferences and several prominent non-expected-utility theories, including rank-dependent utility, betweenness, disappointment aversion, and cautious expected utility. Section S.3 describes the implications of preference for diversification for insurance demand, and discusses how preference for diversification is equivalent to risk aversion for either rank-dependent utility or any preference that is quasiconcave in probabilities. Section S.4 establishes the existence of a value function for the optimal risk attitude representation. Proofs are contained in Section S.5.
S.1. LOCAL EXPECTED-UTILITY ANALYSISWhen applying the optimal risk attitude model, one important consideration is how properties of the set of transformations in the representation relate to properties of the corresponding risk preference. In this section, we show that the certainty equivalent for an ORA representation respects a stochastic order if and only if each of the transformations φ ∈ also respects this order. This result is similar in spirit to the local expected-utility analysis introduced in the influential paper by Machina (1982). After presenting the main theorem of this section, we will make precise connections to Machina's results and the many generalizations and extensions that appeared in the literature that followed. We will also describe how the expected-utility core recently developed by Cerreia-Vioglio, Maccheroni, and Marinacci (2017) can be related to the ORA representation.The main result of this section applies to any convex utility representation on a set of lotteries (X), where X is any compact metric space. Of particular interest is the special case where X is an interval, for example, a set of monetary outcomes or the set of continuation values for an ORA representation. We first state a general definition of stochastic orders generated by sets of functions. DEFINITION S.1: Let C be a set in the space of real-valued continuous functions C(X) for some compact metric space X. The order ≥ C on (X) generated by C is defined by μ ≥ C η ⇐⇒ φ(x) dμ(x) ≥ φ(x) dη(x) ∀φ ∈ C A function W : (X) → R is monotone with respect to the order ≥ C if μ ≥ C η implies W (μ) ≥ W (η).