Preference elicitation for risky prospects

Abstract

Minimax-regret preference elicitation allows intelligent decisions to be made on behalf of people facing risky choices. Standard gamble queries, a vital tool in this type of preference elicitation, assume that people, from whom preference information is being elicited, can be modeled using expected utility theory. However, there is strong evidence from psychology that people may systematically deviate from expected utility theory. Cumulative prospect theory is an alternative model to expected utility theory which has been shown empirically, to better explain humans' decision making in risky settings. We show that the current minimax-regret preference elicitation techniques can fail to properly elicit appropriate information if the preferences of the user follow cumulative prospect theory. As a result, we develop a new querying method for preference elicitation that is applicable to cumulative prospect theory models. Simulations show that our method can effectively elicit information for decision making in both cumulative prospect theory and expected utility theory settings, resulting in a flexible and effective preference elicitation method.