Methods for eliciting and aggregating expert judgment are necessary when decision-relevant data are scarce. Such methods have been used for aggregating the judgments of a large, heterogeneous group of forecasters, as well as the multiple judgments produced from an individual forecaster. This...

We present a new model analyzing the effect of uncertainty faced by bookmakers. It is shown that bettors with inside information or expert analysis decrease the odds set by profit maximizing bookmakers. Data on previously unraced two year old horses and those that have raced previously are used...

We address the problem of decision making under “deep uncertainty,” introducing an approach we call Robust Portfolio Decision Analysis. We introduce the idea of Belief Dominance as a prescriptive operationalization of a concept that has appeared in the literature under a number of names. We...

A key role of science-based non-governmental organizations (NGOs) is to communicate scientific knowledge to policymakers. However, recent evidence has emerged showing that industry-backed groups often attempt to undermine the credibility of such NGOs and weaken their ability to influence policy....

We study the problem a diagnostic expert (e.g., a physician) faces when offering a diagnosis to a client (e.g., a patient) that may be based only on her own diagnostic ability or supplemented by a diagnostic test — conventional and artificial intelligence (AI) tools alike — revealing the...

We propose a decomposition to distinguish between Knightian uncertainty (ambiguity) and risk, where the first measures the uncertainty about the probability distribution generating the data, while the second measures uncertainty about the odds of the outcomes when the probability distribution is...