Learning a decision maker's utility function from (possibly) inconsistent behavior

Learning a decision maker's utility function from (possibly) inconsistent behavior

When modeling a decision problem using the influence diagram framework, the quantitative part rests on two principal components: probabilities for representing the decision maker’s uncertainty about the domain and utilities for representing preferences. Over the last decade, several methods have been developed for learning the probabilities from a database. However, methods for learning the uti...

Integrating Data from Possibly Inconsistent Databases

We address the problem of data inconsistencies while integrating data sets from multiple autonomous relational databases. We start by arguing that the semantics of integrating possibly inconsistent data is naturally captured by the maximal consistent subsets of the set of all information contained in the collected data. Based on this idea, we propose a simple and intuitive semantical framework,...

Learning an Agent's Utility Function by Observing Behavior

From Bayesian Decision-Makers to Bayesian Agents

Bayesian approach to decision making is successfully applied in control theory for design of control strategy. However, it is based on on the assumption that a decision-maker is the only active part of the system. Relaxation of this assumption would allow us to build a framework for design of control strategy in multi-agent systems. In Bayesian framework, all information is represented by proba...

Investment behavior under ambiguity: The case of pessimistic decision makers

We de…ne pessimistic, respectively optimistic, investors as CEU (Choquet expected utility) decision makers who update their pessimistic, respectively optimistic, beliefs according to a pessimistic (Dempster-Shafer), respectively optimistic, update rule. This paper then demonstrates that, in contrast to optimistic investors, pessimistic investors may strictly prefer investing in an illiquid asse...