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

TL;DR

This paper models strategic behavior in crowd sensing systems with referral-based passive rewards, revealing free riding issues and informing better incentive mechanism design.

Contribution

It introduces a game-theoretic model for referral-based crowd sensing with passive rewards, deriving equilibrium efforts and highlighting free riding effects.

Findings

Passive rewards can lead to free riding among agents.

Geometric incentive mechanisms may not proportionally increase effort.

The model provides insights for designing effective recruitment incentives.

Abstract

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.