# Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization

arXiv: https://arxiv.org/abs/2312.12303 · HTML: https://mlsanity.com/paper/2312.12303/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [Incentive-Compatible Recovery from Manipulated Signals, with Applications to Decentralized Physical Infrastructure](https://mlsanity.com/paper/2503.07558/) (2025)

## Abstract (arXiv)

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.