Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization
Abstract
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.
Cite
Text
Richardson and Faltings. "Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I9.28849Markdown
[Richardson and Faltings. "Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/richardson2024aaai-peer/) doi:10.1609/AAAI.V38I9.28849BibTeX
@inproceedings{richardson2024aaai-peer,
title = {{Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization}},
author = {Richardson, Adam and Faltings, Boi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {9883-9890},
doi = {10.1609/AAAI.V38I9.28849},
url = {https://mlanthology.org/aaai/2024/richardson2024aaai-peer/}
}