---
title: Peer Prediction
type: vocabulary
url: "https://www.envisioning.com/vocab/peer-prediction"
summary: A mechanism design technique for incentivizing honest reports by scoring agents based on how well their reports correlate with peers, without ground truth.
year: 2004
generality: 0.40
---

# Peer Prediction

A mechanism design technique for incentivizing honest reports by scoring agents based on how well their reports correlate with peers, without ground truth.
Peer prediction is a class of mechanism design scoring rules that incentivize honest reporting by paying agents based on the correlation between their reports and the reports of their peers, without access to a ground truth label. The original formulation by Miller, Resnick, and Zeckhauser (2004) showed that the Proper Bayesian Surprise scoring rule dominates truth-telling in expectation.

The technique was developed for crowdsourcing markets where the requester cannot verify answers but can ask multiple workers. In AI safety, peer prediction has been proposed as a way to score AI agent reports without a human or ground truth: if multiple agents see the same input and their honest reports agree, the agreement is rewarded.

The 2026 paper by Bergemann, Koh, and Morris uses peer prediction as a stylized example of discipline via higher-order beliefs. Agents in a mechanism that scores their reports against peers have an incentive to report what they actually believe, even when the principal cannot verify.

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Source: Envisioning — Technology Research Institute (https://www.envisioning.com/vocab/peer-prediction)