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. In this paper we explore the maximum entropy principle to estimate the utility function of a risk averse decision maker.

This paper proposes a modeling approach to evaluate the impact of economic policies on the decision maker's behavior. This modeling approach incorporates the agent's preferences, estimated through utility elicitation methods, into the objective function of a discrete sequential stochastic programming model that describes the uncertainties and the constraints faced by the decision maker. Our approach was applied to nine farmers of Portugal. The elicitation of the farmers' preferences reveals that the Cumulative Prospect Theory is relevant to describe the farmers' behavior under risk. Our programming model was used to evaluate the impact of the Common Agricultural Policy with partial and full decoupling of subsidies.

This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover, the GME estimator is more precise than the OLS one. Overall, the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data are generated by utility elicitation methods.

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