We state the problem of inverse reinforcement learning in terms of preference elicitation, resulting in a principled (Bayesian) statistical formulation. This generalises previous work on Bayesian inverse reinforcement learning and allows us to obtain a posterior distribution on the agent’s preferences, policy and optionally, the obtained reward sequence, from observations. We examine the relati...

We test whether induced mood states have an effect on elicited risk and time preferences in a conventional laboratory experiment. We jointly estimate risk and time preferences and find that subjects induced into a negative mood exhibit economically significant higher risk aversion than those in the control treatment. Those in the positive mood treatment exhibit even higher risk aversion. We fin...

In this paper, we define the concept of incomplete hesitant fuzzy preference relations to deal with the cases where the decision makers express their judgments by using hesitant fuzzy preference relations with incomplete information, and investigate the consistency of the incomplete hesitant fuzzypreference relations and obtain the reliable priority weights. We first establish a goal programmin...

We compare the economic efficiency achieved by multiattribute auctions that use an accurate GAI modeling of preferences, with a multiattribute auction that is limited to an additive representation. We draw random GAI-structured utility functions with various internal structures, generate additive functions that approximate the GAI utility, and compare the performance of the auctions using the t...

Revealed preference theory studies the possibility of modeling an agent’s revealed preferences and the construction of a consistent utility function. However, modeling agent’s choices over preference orderings is not always practical and demands strong assumptions on human rationality and data-acquisition abilities. Therefore, we propose a simple generative choice model where agents are assumed...