Utility function estimation: the entropy approach

TL;DR

This paper applies the maximum entropy principle to estimate utility functions for risk-averse decision makers, establishing an analogy between probability and utility through a utility density function.

Contribution

It introduces a novel approach using maximum entropy to estimate utility functions, expanding the application of entropy methods in decision theory.

Findings

Maximum entropy provides a flexible framework for utility estimation.

The method can incorporate partial preference information.

It generalizes existing utility estimation techniques.

Abstract

The maximum entropy principle can be used to assign utility values when only partial information is available about the decision maker's preferences. In order to obtain such utility values it is necessary to establish an analogy between probability and utility through the notion of a utility density function. According to some authors [Soofi (1990), Abbas (2006a) (2006b), Sandow et al. (2006), Friedman and Sandow (2006), Darooneh (2006)] the maximum entropy utility solution embeds a large family of utility functions. In this paper we explore the maximum entropy principle to estimate the utility function of a risk averse decision maker.