Duality Mappings For The Theory of Risk Aversion with Vector Outcomes

Abstract

The Author considera a decision-making environment with an outcome space that is a convex and compact subset of a vector space belonging to a general class of such spaces. Given this outcome space, he defines general classes of (a) risk averse von Neumann-Morgenstern utility functions defined over the outcome space, (b) multi-valued mappings that yield the certainty equivalent outcomes corresponding to a lottery, (c) multi-valued mappings that yield the risk premia corresponding to a lottery, and (d) multi-valued mappings that yield the acceptance set of lotteries corresponding to an outcome. Their duality results establish that the usual mappings that generate (b), (c) and (d) from (a) are bijective.They apply these results to the problem of computing the value of financial assets to a risk averse decision-maker and show that this value will always be less than the arbitrage-free valuation.[CDS WP NO 160]

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