We study a new incentive problem of social information sharing for location-based services (e.g., Waze and Yelp). The problem aims to crowdsource a mass of mobile users to learn massive point-of-interest (PoI) information while traveling and share it with each other as a public good. Given that crowdsourced users mind their own travel costs and possess various preferences over the PoI information along different paths, we formulate the problem as a non-atomic routing game with positive network externalities. We first show by price of anarchy (PoA) analysis that, in the absence of any incentive design, users' selfish routing on the path with the lowest cost will limit information diversity and lead to an arbitrarily large efficiency loss from the social optimum. This motivates us to explore effective incentive mechanisms to remedy while upholding individual rationality, incentive compatibility, and budget balance to ensure practical feasibility. We start by presenting an adaptive information restriction (AIR) mechanism that dynamically customizes restriction fractions, depending on the real user flows along different paths, to govern users' access to the shared PoI aggregation. We show that AIR achieves a PoA of 0.25 for homogeneous users (of identical PoI preferences over paths) and 0.125 for heterogeneous users in a typical network of two parallel paths. Further, we propose a side-payment mechanism (ASP) that adaptively charges or rewards users along certain paths. With those charges and rewards well-tailored, ASP significantly improves the PoA to 1 (optimal) and 0.5 for homogeneous and heterogeneous users in the two-path network, respectively. For a generalized network of multiple parallel paths, we further advance ASP to be able to guarantee a PoA of 0.5. Additionally, our theoretical results are well corroborated by our numerical findings.

翻译：我们研究了基于位置服务（如Waze和Yelp）中社交信息共享的新型激励机制问题。该问题旨在众包海量移动用户在出行过程中学习大量兴趣点（PoI）信息，并将其作为公共品相互共享。考虑到众包用户在意自身出行成本，且对不同路径的PoI信息具有异质性偏好，我们将该问题建模为具有正网络外部性的非原子路由博弈。首先通过无政府价格（PoA）分析表明：在缺乏激励机制的情况下，用户选择最低成本路径的自私路由行为将限制信息多样性，导致社会福利效率损失任意增大。这促使我们探索在满足个体理性、激励相容和预算平衡等实际可行性约束下的有效激励机制。我们提出自适应信息限制（AIR）机制，该机制根据用户在不同路径上的实时流量动态定制限制比例，以管控用户对共享PoI聚合信息的访问权限。研究表明：在两条平行路径的典型网络中，AIR对同质用户（对各路径PoI偏好相同）可实现0.25的PoA，对异质用户可实现0.125的PoA。进一步，我们提出自适应侧向支付（ASP）机制，通过对特定路径用户进行自适应收费或奖励。通过精心设计的收支方案，ASP在双路径网络中将同质用户的PoA提升至1（最优值），异质用户提升至0.5。针对多平行路径的广义网络，我们进一步改进ASP使其能保证0.5的PoA。此外，数值实验结果充分验证了我们的理论发现。