Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
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 with respect to their sets of priors. We show that a natural Pareto condition is equivalent to the existence of a set Λ of probability vectors over the experts, interpreted as possible allocations of weights to the experts, such that (i) the decision makerʼs set of priors is precisely all the weighted-averages of priors, where an expertʼs prior is taken from her set and the weight vector is taken from Λ; (ii) the decision makerʼs valuation of an act is the minimal weighted valuation, over all weight vectors in Λ, of the expertsʼ valuations.