Combinatorial Information Market Design
Information Systems Frontiers
Uncertainty Management in Expert Systems
IEEE Expert: Intelligent Systems and Their Applications
Introduction to probabilistic automata (Computer science and applied mathematics)
Introduction to probabilistic automata (Computer science and applied mathematics)
Incentives for expressing opinions in online polls
Proceedings of the 9th ACM conference on Electronic commerce
A Short Introduction to Computational Social Choice
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Sharing a reward based on peer evaluations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A truth serum for sharing rewards
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Eliciting forecasts from self-interested experts: scoring rules for decision makers
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Sharing Rewards Among Strangers Based on Peer Evaluations
Decision Analysis
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An important question when eliciting opinions from experts is how to aggregate the reported opinions. In this paper, we propose a pooling method to aggregate expert opinions. Intuitively, it works as if the experts were continuously updating their opinions in order to accommodate the expertise of others. Each updated opinion takes the form of a linear opinion pool, where the weight that an expert assigns to a peer's opinion is inversely related to the distance between their opinions. In other words, experts are assumed to prefer opinions that are close to their own opinions. We prove that such an updating process leads to consensus, i.e., the experts all converge towards the same opinion. Further, we show that if rational experts are rewarded using the quadratic scoring rule, then the assumption that they prefer opinions that are close to their own opinions follows naturally. We empirically demonstrate the efficacy of the proposed method using real-world data.