Reputation-based Incentive Protocols in Crowdsourcing Applications
Yu Zhang, Mihaela van der Schaar

TL;DR
This paper introduces a reputation-based incentive protocol for crowdsourcing platforms, using game theory to align worker and requester incentives, thereby improving social welfare and approaching Pareto efficiency.
Contribution
It proposes a novel social norm-based incentive protocol integrated with pricing schemes, enhancing non-cooperative equilibria in crowdsourcing applications.
Findings
Protocols can achieve near Pareto efficiency.
Reputation mechanisms improve social welfare.
Optimal protocols can maximize platform revenue.
Abstract
Crowdsourcing websites (e.g. Yahoo! Answers, Amazon Mechanical Turk, and etc.) emerged in recent years that allow requesters from all around the world to post tasks and seek help from an equally global pool of workers. However, intrinsic incentive problems reside in crowdsourcing applications as workers and requester are selfish and aim to strategically maximize their own benefit. In this paper, we propose to provide incentives for workers to exert effort using a novel game-theoretic model based on repeated games. As there is always a gap in the social welfare between the non-cooperative equilibria emerging when workers pursue their self-interests and the desirable Pareto efficient outcome, we propose a novel class of incentive protocols based on social norms which integrates reputation mechanisms into the existing pricing schemes currently implemented on crowdsourcing websites, in…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Expert finding and Q&A systems · Auction Theory and Applications
