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Game Theoretic Analysis of Tree Based Referrals for Crowd Sensing Social Systems with Passive Rewards

Kandhway, Kundan and Kotnis, Bhushan (2016) Game Theoretic Analysis of Tree Based Referrals for Crowd Sensing Social Systems with Passive Rewards. In: 8th International Conference on Communication Systems and Networks (COMSNETS), JAN 05-10, 2016, Bangalore, INDIA.

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Official URL: http://dx.doi.org/10.1109/COMSNETS.2016.7439947


Participatory crowd sensing social systems rely on the participation of large number of individuals. Since humans are strategic by nature, effective incentive mechanisms are needed to encourage participation. A popular mechanism to recruit individuals is through referrals and passive incentives such as geometric incentive mechanisms used by the winning team in the 2009 DARPA Network Challenge and in multi level marketing schemes. The effect of such recruitment schemes on the effort put in by recruited strategic individuals is not clear. This paper attempts to fill this gap. Given a referral tree and the direct and passive reward mechanism, we formulate a network game where agents compete for finishing crowd sensing tasks. We characterize the Nash equilibrium efforts put in by the agents and derive closed form expressions for the same. We discover free riding behavior among nodes who obtain large passive rewards. This work has implications on designing effective recruitment mechanisms for crowd sourced tasks. For example, usage of geometric incentive mechanisms to recruit large number of individuals may not result in proportionate effort because of free riding.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Depositing User: Id for Latest eprints
Date Deposited: 22 Oct 2016 09:09
Last Modified: 22 Oct 2016 09:09
URI: http://eprints.iisc.ac.in/id/eprint/54982

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