Abstract. We axiomatically characterise a class of algorithms for making sequential decisions in situations of complete ignorance. These algorithms assume that a decision maker (DM...
In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model...
This paper explores biases in the elicitation of utilities under risk and the contribution that generalizations of expected utility can make to the resolution of these biases. We ...
The paper takes a fresh look at algorithms for maximizing expected utility over a set of policies, that is, a set of possible ways of reacting to observations about an uncertain s...
The outcome of a legal dispute, namely, the decision of its adjudicator, is uncertain, and both parties develop their strategies on the basis of their appreciation of the probabili...
Abstract. We introduce take-it-or-leave-it auctions (TLAs) as an allocation mechanism that allows buyers to retain much of their private valuation information, yet generates close-...
Bayesian advocates of expected utility maximization use sets of probability distributions to represent very different ideas. Strict Bayesians insist that probability judgment is n...
We discuss the problem of scheduling tasks that consume uncertain amounts of a resource with known capacity and where the tasks have uncertain utility. In these circumstances, we w...
We discuss a solution to the winner determination problem which takes into account not only costs but also risk aversion of the agent that accepts the bids. We are interested in b...
We contrast three decision rules that extend Expected Utility to contexts where a convex set of probabilities is used to depict uncertainty: Γ-Maximin, Maximality, and E-admissib...
Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kad...