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

TL;DR

This paper proposes a game-theoretic rating protocol for crowdsourcing contests that incentivizes cooperation and deters selfish behavior, improving requester revenue under conditions of heterogeneous workers and imperfect monitoring.

Contribution

It introduces a novel rating protocol design integrating binary labels with differential pricing, tailored for heterogeneous workers in imperfect monitoring environments.

Findings

The protocol effectively promotes cooperation among workers.

Simulation shows significant revenue gains for requesters.

Design guidelines for optimal rating parameters are established.

Abstract

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.