Subjective probabilistic judgments are inevitable in many real life domains. A common way to obtain such judgments is to assess fractiles or confidence intervals. However, such judgments tend to be systematically overconfident. For example, 90% confidence intervals for future uncertain...

The paper develops a model for combining point forecasts into a predictive distribution for a variable of interest. Our approach allows for point forecasts to be correlated and admits uncertainty on the distribution parameters given the forecasts. Further, it provides an easy way to compute an...

When combining forecasts, a simple average of the forecasts performs well, often better than more sophisticated methods. In a prescriptive spirit, we consider some other parsimonious, easy-to-use heuristics for combining interval forecasts and compare their performance with the benchmark...

Probabilistic forecasts are necessary for robust decisions in the face of uncertainty. The M5 Uncertainty competition required participating teams to forecast nine quantiles for unit sales of various products at various aggregation levels and for different time horizons. This paper evaluates the...

This paper suggests a new normative approach for combining beliefs. We call it the evidence-first method. Instead of aggregating credences alone, as the prevailing approaches, we focus instead on eliciting a group's full probability distribution on the basis of the evidence available to its...

We study the gender gap in a competitive environment by exploiting the “naturalistic experiment” of a TV game show with self-selected participants and no gender-specific constraints nor discrimination. Games consist of multiple rounds where contestants answer general knowledge questions...

We study the risk-taking behavior of stock analysts under varying market conditions. We examine how the risk analysts take by providing bold forecasts that deviate from consensus depends on the degree of uncertainty in the environment as well as the analysts' skill level. We provide evidence...