Utilizing Information Optimally to Influence Distributed Network Routing
Utilizing Information Optimally to Influence Distributed Network Routing
复制标题
优化利用信息影响分布式网络路由
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Jason R. Marden
中科院分区：
文献类型：
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作者：
Bryce L. Ferguson;Philip N. Brown;Jason R. Marden
系统设计者如何利用系统级知识来获得激励，以最佳地影响社会行为网络路由的文献包含了许多研究结果的应用货币通行费的影响行为和提高效率的自利网络流量路由。这些结果通常分为两类：（1）最优通行费，它激励网络和人口的已知实现的社会最优行为，或（2）鲁棒通行费，它可证明减少拥塞，给出关于网络和用户类型的不确定性，但通常可能无法优化路由。本文提出了强大的影响，机制的研究，问系统设计者如何能够最佳地利用额外的信息，网络结构和用户的价格敏感性，设计影响行为的定价机制。我们设计了一类并行网络路由博弈的最优缩放边际成本定价机制，并在网络结构和/或平均用户价格敏感度已知的情况下得到了严格的性能保证。我们的研究结果表明，从系统运营商的角度来看，在一般情况下，它是更重要的是要知道网络的结构比它是知道关于用户群体的分布信息。
How can a system designer exploit system-level knowledge to derive incentives to optimally influence social behaviorƒ The literature on network routing contains many results studying the application of monetary tolls to influence behavior and improve the efficiency of self-interested network traffic routing. These results typically fall into two categories: (1) optimal tolls which incentivize socially-optimal behavior for a known realization of the network and population, or (2) robust tolls which provably reduce congestion given uncertainty regarding networks and user types, but may fail to optimize routing in general. This paper advances the study of robust influencing, mechanisms asking how a system designer can optimally exploit additional information regarding the network structure and user price sensitivities to design pricing mechanisms which influence behavior. We design optimal scaled marginal-cost pricing mechanisms for a class of parallel-network routing games and derive the tight performance guarantees when the network structure and/or the average user price-sensitivity is known. Our results demonstrate that from the standpoint of the system operator, in general it is more important to know the structure of the network than it is to know distributional information regarding the user population.