Decision theory has been plagued with a variety of problems almost from the start. Some, like the Ellsberg paradox [Ellsberg, 1961] merely seem to point to the problems actual people have in calculating probabilities and utilities. Others, like the Newcomb paradox and its relatives, have motivated a re-foundation of decision theory on causal grounds rather than evidential grounds [Joyce, 1999]....

By combining information theory, statistical decision theory, and maximum entropy to address the decision fusion problems, a statistical decision fusion theory is obtained. The theory explains why decision fusion is so difficult and why the performance of decision fusion systems does not always meet expectations. The theory suggests how statistical decision systems such as the conceptual "Famil...

In this paper, we argue that replacing the expectation of the loss in statistical decision theory with the median of the loss leads to a viable and useful alternative to conventional risk minimization particularly because it can be used with heavy tailed distributions. We investigate three possible definitions for such medloss estimators and derive examples of them in several standard settings....

The paper considers the role of econometrics in decision making under uncertainty. This leads to a focus on predictive distributions. The decision maker's subjective distribution is only partly speci"ed; it belongs to a set S of distributions. S can also be regarded as a set of plausible data-generating processes. Criteria are needed to evaluate procedures for constructing predictive distributi...

We argue that, contrary to conventional wisdom, decision theory is not invariant to the physical environment in which a decision is made. Specifically, we show that a decision maker (DM) with access to quantum information resources may be able to do strictly better than a DM with access only to classical information resources. In this respect, our findings are somewhat akin to those in computer...

This paper describes and evaluates a decision theory measurement model that can be used to classify examinees based on their item response patterns. The model has a simple framework that starts with the conditional probabilities of examinees in each category or mastery state responding correctly to each item. An overview of measurement decision theory and its key concepts are presented and illu...