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问题。该问题旨在众包大量移动用户在出行过程中学习海量兴趣点（PoI）信息，并将其作为公共物品相互分享。考虑到众包用户关注自身出行成本，且对不同路径上的PoI信息具有多样化偏好，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，在缺乏激励设计的情况下，用户自私地选择最低成本路径会限制信息多样性，并导致社会福利与最优状态之间的任意大效率损失。这促使我们探索有效的激励机制，在保证个体理性、激励相容性和预算平衡的前提下弥补这一缺陷，以确保实际可行性。我们首先提出自适应信息限制（AIR）机制，该机制根据各路径上的实时用户流量动态定制限制比例，以控制用户对共享PoI聚合信息的访问。研究表明，在典型双平行路径网络中，AIR对同质用户（对路径具有相同的PoI偏好）实现了0.25的PoA，对异质用户实现了0.125的PoA。进一步地，我们提出自适应侧支付（ASP）机制，对特定路径上的用户进行有差别的收费或奖励。通过精细调整这些收费与奖励，ASP将双路径网络中同质用户和异质用户的PoA分别显著提升至1（最优）和0.5。对于多平行路径的广义网络，我们进一步优化ASP以保证0.5的PoA。此外，我们的理论结果得到了数值实验的充分验证。