Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization
DOI:
https://doi.org/10.1609/aaai.v38i9.28849Keywords:
GTEP: Mechanism Design, GTEP: Applications, GTEP: Game TheoryAbstract
Peer prediction incentive mechanisms for crowdsourcing are generally limited to eliciting samples from categorical distributions. Prior work on extending peer prediction to arbitrary distributions has largely relied on assumptions on the structures of the distributions or known properties of the data providers. We introduce a novel class of incentive mechanisms that extend peer prediction mechanisms to arbitrary distributions by replacing the notion of an exact match with a concept of neighborhood matching. We present conditions on the belief updates of the data providers that guarantee incentive-compatibility for rational data providers, and admit a broad class of possible reasonable updates.Downloads
Published
2024-03-24
How to Cite
Richardson, A., & Faltings, B. (2024). Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization. Proceedings of the AAAI Conference on Artificial Intelligence, 38(9), 9883-9890. https://doi.org/10.1609/aaai.v38i9.28849
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Section
AAAI Technical Track on Game Theory and Economic Paradigms