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Crowdsourcing has emerged as a paradigm for leveraging human intelligence andactivity to solve a wide range of tasks. However, strategic workers will findenticement in their self-interest to free-ride and attack in a crowdsourcingcontest dilemma game. Hence, incentive mechanisms are of great importance toovercome the inefficiency of the socially undesirable equilibrium. Existingincentive mechanisms are not effective in providing incentives for cooperationin crowdsourcing competitions due to the following features: heterogeneousworkers compete against each other in a crowdsourcing platform with imperfectmonitoring. In this paper, we take these features into consideration, anddevelop a novel game-theoretic design of rating protocols, which integratesbinary rating labels with differential pricing to maximize the requester'sutility, by extorting selfish workers and enforcing cooperation among them. Byquantifying necessary and sufficient conditions for the sustainable socialnorm, we formulate the problem of maximizing the revenue of the requester amongall sustainable rating protocols, provide design guidelines for optimal ratingprotocols, and design a low-complexity algorithm to select optimal designparameters which are related to differential punishments and pricing schemes.Simulation results demonstrate how intrinsic parameters impact on designparameters, as well as the performance gain of the proposed rating protocol.

Rating Protocol Design for Extortion and Cooperation in the Crowdsourcing Contest Dilemma