我們研究了一個衆包問題，該平台旨在激勵分布的員工提供高品質和真實的解決方案，但沒有能力驗證解決方案。雖然之前的研究大多假設平台和員工擁有對稱的資訊，但我們研究的是一個平台具有資訊優勢的資訊不對稱場景。具體來說，平台了解更多關于勞工平均溶液精度的資訊，并有政策地将這些資訊透露給勞工。員工将利用宣布的資訊來确定他們在努力完成任務時獲得獎勵的可能性。我們研究了兩種類型的勞工:完全信任聲明的天真勞工和基于聲明更新先前信念的戰略勞工。對于天真的勞工，我們表明平台應該總是宣布一個高平均精度，以最大化其收益。然而，對于戰略工作者來說，這并不總是最優的，因為這可能會降低平台公告的可信度，進而降低平台收益。有趣的是，當面對戰略工作者時，該平台甚至可能會宣布平均精度低于實際值。另一個違反直覺的結果是，平台收益可能會減少高精度勞工的數量。
原文題目：Strategic Information Revelation in Crowdsourcing Systems Without Verification
原文：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.
無驗證的衆包系統中的戰略資訊披露.pdf