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arXiv 2501.03055cs.GTcs.AI
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分析与调控拥塞博弈中的人在回路学习

To Analyze and Regulate Human-in-the-loop Learning for Congestion Games

Singapore University of Technology and Design(新加坡科技设计大学)

Hongbo Li, Lingjie Duan

AI总结:

本研究针对拥塞博弈中短视路由策略的效率损失问题,提出选择性信息披露机制,将无政府状态价格降至2以下,并扩展至多中间节点线性路径图,为交通路由调控提供新方案。

摘要:

在拥塞博弈中,自利用户会短视地涌向最短路径,而社会规划者会设计机制,通过信息或付费激励来调控这种自私的路由行为。然而,此类机制设计需要掌握时变的交通状况,而正是用户自身在学习并向社会规划者(例如Waze或Google Maps)上报过往的道路体验。当拥塞博弈遇上移动众包时,如何激励自利用户探索非最短路径,以实现最优的利用-探索权衡,是一个至关重要的问题。首先,我们考虑一个简单但基础的并行路由网络,该网络包含一条确定性路径和多条随机性路径,用户的平均到达概率为$\lambda$。我们证明,与社会最优策略相比,当前的短视路由策略(在Waze和Google Maps中被广泛使用)既存在探索不足(当危险信念较强时)也存在利用不足(当危险信念较弱时)的问题。由于短视策略的探索不足,我们证明其导致的无政府状态价格(PoA)大于$\frac{1}{1-\rho^{\frac{1}{\lambda}}}$,当折扣因子$\rho\rightarrow1$时,该值可以任意大。为了缓解如此巨大的效率损失,我们提出了一种新颖的选择性信息披露(SID)机制:当用户到达时打算过度探索随机性路径时,我们仅向其披露最新的交通信息;而当用户想要探索不足时,则隐藏此类信息。我们证明,我们的机制成功将PoA降低至小于2。除了并行路由网络之外,我们还进一步将我们的机制和PoA结果扩展到任意具有多个中间节点的线性路径图。

英文摘要:

In congestion games, selfish users behave myopically to crowd to the shortest paths, and the social planner designs mechanisms to regulate such selfish routing through information or payment incentives. However, such mechanism design requires the knowledge of time-varying traffic conditions and it is the users themselves to learn and report past road experiences to the social planner (e.g., Waze or Google Maps). When congestion games meet mobile crowdsourcing, it is critical to incentivize selfish users to explore non-shortest paths in the best exploitation-exploration trade-off. First, we consider a simple but fundamental parallel routing network with one deterministic path and multiple stochastic paths for users with an average arrival probability $λ$. We prove that the current myopic routing policy (widely used in Waze and Google Maps) misses both exploration (when strong hazard belief) and exploitation (when weak hazard belief) as compared to the social optimum. Due to the myopic policy's under-exploration, we prove that the caused price of anarchy (PoA) is larger than \(\frac{1}{1-ρ^{\frac{1}λ}}\), which can be arbitrarily large as discount factor \(ρ\rightarrow1\). To mitigate such huge efficiency loss, we propose a novel selective information disclosure (SID) mechanism: we only reveal the latest traffic information to users when they intend to over-explore stochastic paths upon arrival, while hiding such information when they want to under-explore. We prove that our mechanism successfully reduces PoA to be less than~\(2\). Besides the parallel routing network, we further extend our mechanism and PoA results to any linear path graphs with multiple intermediate nodes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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