On Incentivizing Social Information Sharing Through Routing Games

TL;DR

This paper models social information sharing as a routing game, analyzes inefficiencies of selfish behavior, and proposes incentive mechanisms that significantly improve social welfare while maintaining practical properties.

Contribution

It introduces novel incentive mechanisms, AIR and ASP, for routing games with social externalities, achieving improved price of anarchy and practical implementation.

Findings

AIR reduces non-cooperative access to the public good, achieving PoA=1/4.

ASP uses adaptive side-payments, achieving PoA=1/2.

Theoretical results are validated with real-world dataset experiments.

Abstract

Crowdsourcing services, such as Waze, leverage a mass of mobile users to learn massive point-of-interest (PoI) information while traveling and share it as a public good. Given that crowdsourced users mind their travel costs and possess various preferences over the PoI information along different paths, we formulate the problem as a novel non-atomic multi-path routing game with positive network externalities among users in social information sharing. In the absence of any incentive design, our price of anarchy (PoA) analysis shows that users' selfish routing on the path with the lowest cost will limit information diversity and lead to $PoA = 0$ with an arbitrarily large efficiency loss from the social optimum. This motivates us to design effective incentive mechanisms to remedy while upholding desirable properties such as individual rationality, incentive compatibility, and budget balance for practical users. Without requiring a specific user's path preference, we present a non-monetary mechanism called Adaptive Information Restriction (AIR) that reduces non-cooperative users' access to the public good as an indirect penalty, which meets all the desirable properties. By meticulously adapting penalty fractions to the actual user flows along different paths, our AIR achieves non-trivial $PoA = \frac{1}{4}$ with low complexity $O(k\log k+\log m)$, where $k$ and $m$ denote the numbers of involved paths and user types, respectively. If the system can further enable pricing for users, we then propose a new monetary mechanism called Adaptive Side-Payment (ASP), which adaptively charges and rewards users according to their chosen paths, respectively. Our ASP mechanism successively achieves a $PoA = \frac{1}{2}$ with even reduced complexity $O(k\log k)$. Finally, our theoretical findings are well corroborated by our experimental results using a real-world public dataset.