Elicitation of Probabilities Using Competitive Scoring Rules
Decision Analysis
The Wisdom of Crowds
Eliciting Informative Feedback: The Peer-Prediction Method
Management Science
Incentives for expressing opinions in online polls
Proceedings of the 9th ACM conference on Electronic commerce
Self-financed wagering mechanisms for forecasting
Proceedings of the 9th ACM conference on Electronic commerce
The Parimutuel Kelly Probability Scoring Rule
Decision Analysis
A mechanism that provides incentives for truthful feedback in peer-to-peer systems
Electronic Commerce Research
Reputation systems for open collaboration
Communications of the ACM
Sharing Rewards Among Strangers Based on Peer Evaluations
Decision Analysis
Crowdsourced judgement elicitation with endogenous proficiency
Proceedings of the 22nd international conference on World Wide Web
| Hi-index | 0.02 |
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.