Psychological Game Analysis for Crowdsourcing with Reciprocity

Xin, Kun; Fan, Sizheng; Cai, Wei

doi:10.1109/icc45855.2022.9838632

Cited by 1 publication

(1 citation statement)

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“…A crowdsourcing framework for user recruitment in Online Social Networks, employing a labor economics approach and formulating the competitive process as a Generalized Colonel Blotto game to determine optimal rewards, is proposed in [20]. Also, the authors in [21] applied psychological game theory to show that crowdsourcers can reduce costs in crowdsourcing by incorporating psychological payoffs, proving optimal incentive plans, and presenting a unique psychological model for improved incentive mechanism design.…”

Section: Incentive Mechanismsmentioning

confidence: 99%

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Adesokan,

Kinney,

Tsiropoulou

2024

Future Internet

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This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH considers incentives, information availability and cost, and the decisions of fellow crowdworkers to model the utility functions for both the crowdworkers and the crowdsourcers. Specifically, the paper presents an initial Approximate CROWDMATCH mechanism grounded in matching theory principles, eliminating externalities from crowdworkers’ decisions and enabling each entity to maximize its utility. Subsequently, the Accurate CROWDMATCH mechanism is introduced, which is initiated by the outcome of the Approximate CROWDMATCH mechanism, and coalition game-theoretic principles are employed to refine the matching process by accounting for externalities. The paper’s contributions include the introduction of the CROWDMATCH system model, the development of both Approximate and Accurate CROWDMATCH mechanisms, and a demonstration of their superior performance through comprehensive simulation results. The mechanisms’ scalability in large-scale crowdsourcing systems and operational advantages are highlighted, distinguishing them from existing methods and highlighting their efficacy in empowering crowdworkers in crowdsourcer selection.

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Section: Incentive Mechanismsmentioning

confidence: 99%