Decision makers can benefit from the subjective judgment of experts. For example, estimates of disease prevalence are quite valuable, yet can be difficult to measure objectively. Useful features of mechanisms for aggregating expert opinions include the ability to: (1) incentivize participants to be truthful; (2) adjust for the fact that some experts are better informed than others; and (3) circumvent the need for objective, "ground truth" observations. Subsets of these properties are attainable by previous elicitation methods, including proper scoring rules, prediction markets, and the Bayesian truth serum. Our mechanism of collective revelation, however, is the first to simultaneously achieve all three. Furthermore, we introduce a general technique for constructing budget-balanced mechanisms-where no net payments are made to participants--that applies both to collective revelation and to past peer-prediction methods.
['Sharad Goel', 'Daniel M. Reeves', 'David M. Pennock']
Collective revelation: a mechanism for self-verified, weighted, and truthful predictions