Curvature of the probability weighting function
Management Science
Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Parameter-Free Elicitation of Utility and Probability Weighting Functions
Management Science
On the foundations of expected expected utility
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Incremental utility elicitation with minimax regret decision criterion
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Regret-based reward elicitation for Markov decision processes
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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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.