Strategic Information Revelation in Crowdsourcing Systems Without Verification

TL;DR

This paper investigates how a crowdsourcing platform can strategically reveal information about worker accuracy to incentivize truthful and high-quality solutions without verification, considering both naive and strategic workers.

Contribution

It introduces a model of asymmetric information revelation in crowdsourcing, analyzing its effects on worker behavior and platform payoff, including counterintuitive insights.

Findings

Announcing high accuracy maximizes payoff with naive workers.

Strategic workers may reduce platform payoff by trusting high accuracy announcements.

Platform may benefit from underreporting accuracy to strategic workers.

Abstract

We study a crowdsourcing problem where the platform aims to incentivize distributed workers to provide high quality and truthful solutions without the ability to verify the solutions. While most prior work assumes that the platform and workers have symmetric information, we study an asymmetric information scenario where the platform has informational advantages. Specifically, the platform knows more information regarding worker average solution accuracy, and can strategically reveal such information to workers. Workers will utilize the announced information to determine the likelihood that they obtain a reward if exerting effort on the task. We study two types of workers, naive workers who fully trust the announcement, and strategic workers who update prior belief based on the announcement. For naive workers, we show that the platform should always announce a high average accuracy to maximize its payoff. However, this is not always optimal for strategic workers, as it may reduce the credibility of the platform announcement and hence reduce the platform payoff. Interestingly, the platform may have an incentive to even announce an average accuracy lower than the actual value when facing strategic workers. Another counterintuitive result is that the platform payoff may decrease in the number of high accuracy workers.