论文标题
非原子路由游戏中的社会学习
Social Learning in Nonatomic Routing Games
论文作者
论文摘要
我们考虑一个离散时间的非原子路由游戏，需求可变和成本不确定。给定具有单个原点和目标的路由网络，每个边缘的成本函数取决于某些不确定的持续状态参数。在每个时期，根据衣柜平衡，通过网络进行随机交通需求。公开观察到已实现的成本，并更新了公共贝叶斯对州参数的信念。我们说，当信仰融合到真理和弱学习时，当平衡流融合到完整信息流时，就会有强大的学习。我们表征了学习的网络。我们证明这些网络具有串联的并行结构，并提供了反例，以表明学习可能在非系列并行网络中失败。
We consider a discrete-time nonatomic routing game with variable demand and uncertain costs. Given a routing network with single origin and destination, the cost function of each edge depends on some uncertain persistent state parameter. At every period, a random traffic demand is routed through the network according to a Wardrop equilibrium. The realized costs are publicly observed and the public Bayesian belief about the state parameter is updated. We say that there is strong learning when beliefs converge to the truth and weak learning when the equilibrium flow converges to the complete-information flow. We characterize the networks for which learning occurs. We prove that these networks have a series-parallel structure and provide a counterexample to show that learning may fail in non-series-parallel networks.
