Collaborative plans for complex group action
Artificial Intelligence
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
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
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
An anytime algorithm for optimal coalition structure generation
Journal of Artificial Intelligence Research
Complexity of constructing solutions in the core based on synergies among coalitions
Artificial Intelligence
A scoring rule-based mechanism for aggregate demand prediction in the smart grid
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Sharing Rewards Among Strangers Based on Peer Evaluations
Decision Analysis
A consensual linear opinion pool
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We study a problem where a group of agents has to decide how a joint reward should be shared among them. We focus on settings where the share that each agent receives depends on the subjective opinions of its peers concerning that agent's contribution to the group. To this end, we introduce a mechanism to elicit and aggregate subjective opinions as well as for determining agents' shares. The intuition behind the proposed mechanism is that each agent who believes that the others are telling the truth has its expected share maximized to the extent that it is well-evaluated by its peers and that it is truthfully reporting its opinions. Under the assumptions that agents are Bayesian decision-makers and that the underlying population is sufficiently large, we show that our mechanism is incentive-compatible, budgetbalanced, and tractable. We also present strategies to make this mechanism individually rational and fair.