Experts are asked to provide their advice in a situation of uncertainty. They adopt the decision maker’s utility function, but each has a potentially different set of prior probabilities, and so does the decision maker. The decision maker and the experts maximize the minimal expected utility...

Experts are asked to provide their advice in a situation of uncertainty. They adopt the decision maker’s utility function, but each has a potentially different set of prior probabilities, and so does the decision maker. The decision maker and the experts maximize the minimal expected utility...

Experts are asked to provide their advice in a situation of uncertainty. They adopt the decision makerʼs utility function, but each has a potentially different set of prior probabilities, and so does the decision maker. The decision maker and the experts maximize the minimal expected utility...

In this paper, we consider a decision-maker who tries to learn the distribution of outcomes from previously observed cases. For each observed database of cases the decision-maker predicts a set of priors expressing his beliefs about the underlying probability distribution. We impose a version of...

In this paper we give an alternative characterization for time-consistent sets of measures in a discrete setting. For each measure P in a time-consistent set Ρ we get a distinct set of predictable processes which in return decribe the P uniquely. This implies we get a one-to-one correspondence...

In this paper we give an alternative characterization for time-consistent sets of measures in a discrete setting. For each measure \mathbb{P} in a time-consistent set \mathcal{P} we get a distinct set of predictable processes which in return decribe the \mathbb{P} uniquely. This implies we get a...