Entropy methods for adaptive utility elicitation

This paper presents an optimal question-selection algorithm to elicit von Neumann and Morgenstern utility values for a set of ordered prospects of a decision situation. The approach uses information theory and entropy-coding principles to select the minimum expected number of questions needed for utility elicitation. At each stage of the questionnaire, we use the question that will provide the largest reduction in the entropy of the joint distribution of the utility values. The algorithm uses questions that require binary responses, which are easier to provide than numeric values, and uses an adaptive question-selection scheme where each new question depends on the previous response obtained from the decision maker. We present a geometric interpretation for utility elicitation and work through a full example to illustrate the approach.