  Crowdsourcing has emerged as a paradigm for leveraging human intelligence and
activity to solve a wide range of tasks. However, strategic workers will find
enticement in their self-interest to free-ride and attack in a crowdsourcing
contest dilemma game. Hence, incentive mechanisms are of great importance to
overcome the inefficiency of the socially undesirable equilibrium. Existing
incentive mechanisms are not effective in providing incentives for cooperation
in crowdsourcing competitions due to the following features: heterogeneous
workers compete against each other in a crowdsourcing platform with imperfect
monitoring. In this paper, we take these features into consideration, and
develop a novel game-theoretic design of rating protocols, which integrates
binary rating labels with differential pricing to maximize the requester's
utility, by extorting selfish workers and enforcing cooperation among them. By
quantifying necessary and sufficient conditions for the sustainable social
norm, we formulate the problem of maximizing the revenue of the requester among
all sustainable rating protocols, provide design guidelines for optimal rating
protocols, and design a low-complexity algorithm to select optimal design
parameters which are related to differential punishments and pricing schemes.
Simulation results demonstrate how intrinsic parameters impact on design
parameters, as well as the performance gain of the proposed rating protocol.
