论文标题
当交通拥堵符合移动众包时：选择性信息披露
When Congestion Games Meet Mobile Crowdsourcing: Selective Information Disclosure
论文作者
论文摘要
在交通拥堵的游戏中，用户可以互相堵塞，而社交计划者则具有有关信息或付款方面的完整信息设计机制的社会计划者。但是，很难获得时间变化的交通状况，并且新兴的众包平台（例如Waze和Google Maps）为移动用户提供了一种方便的方式，可以随着时间的推移学习和分享交通状况。当交通拥堵游戏与移动众包融合时，激励自私的用户要改变其近视路线政策并达到最佳的开发探索权衡至关重要。通过考虑一个简单但基本的并行路由网络，具有一条确定性路径和多个随机路径的原子用户，我们证明，近视路由策略无政府状态（POA）的价格大于$ \ frac {1} {1} {1-ρ} $，这可以任意为折扣因子$ρ\ frirtarrow1 $。为了弥补如此巨大的效率损失，我们提出了选择性信息披露（SID）机制：我们只在用户打算过度探索随机路径时向用户透露最新的流量信息，同时在他们想探索下时隐藏此类信息。我们证明，我们的机制将POA降低到$ \ frac {1} {1- \fracρ{2}} $。除了最差的表现外，我们还通过使用广泛的模拟进一步研究了机制的平均案例性能。
In congestion games, users make myopic routing decisions to jam each other, and the social planner with the full information designs mechanisms on information or payment side to regulate. However, it is difficult to obtain time-varying traffic conditions, and emerging crowdsourcing platforms (e.g., Waze and Google Maps) provide a convenient way for mobile users travelling on the paths to learn and share the traffic conditions over time. When congestion games meet mobile crowdsourcing, it is critical to incentive selfish users to change their myopic routing policy and reach the best exploitation-exploration trade-off. By considering a simple but fundamental parallel routing network with one deterministic path and multiple stochastic paths for atomic users, we prove that the myopic routing policy's price of anarchy (PoA) is larger than $\frac{1}{1-ρ}$, which can be arbitrarily large as discount factor $ρ\rightarrow1$. To remedy such huge efficiency loss, we propose a selective information disclosure (SID) mechanism: we only reveal the latest traffic information to users when they intend to over-explore the stochastic paths, while hiding such information when they want to under-explore. We prove that our mechanism reduces PoA to be less than $\frac{1}{1-\fracρ{2}}$. Besides the worst-case performance, we further examine our mechanism's average-case performance by using extensive simulations.
