Peer Prediction with Heterogeneous Tasks

Abstract

Peer prediction is a method to promote contributions of information by users in settings in which there is no way to verify the quality of responses. In multi-task peer prediction, the reports from users across multiple tasks are used to score contributions. This paper extends the correlated agreement (CA) multi-task peer prediction mechanism to allow the reports from users to be on heterogeneous tasks, each associated with different distributions on responses. The motivation comes from wanting to elicit user-generated content about places in a city, where tasks vary because places, and questions about places, vary. We prove that the generalized CA mechanism is informed truthful under weak conditions, meaning that it is strictly beneficial for a user to invest effort and acquire information, and that truthful reporting is the best strategy when investing effort, as well as an equilibrium. We demonstrate that the mechanism has good incentive properties when tested in simulation on distributions derived from user reports on Google Local Guides.