论文标题
流量路由中的最佳动态信息提供
Optimal dynamic information provision in traffic routing
论文作者
论文摘要
我们考虑了一款两道路动态的路由游戏，其中一条道路的状态（“有风险的道路”）是随机的，并且可能会随着时间而变化。这为实验带来了空间。中央策划者可能希望诱使一些（有限数量的原子代理商）使用风险的道路，即使那里的预期旅行成本很高，以便获得有关道路状况的准确信息。由于代理人是战略性的，我们表明，为了产生实验激励措施，中央计划者需要在风险的道路上预期的旅行成本较低时限制使用风险的道路的代理数量。特别是，由于拥挤，在国家有利时过多地使用了风险的道路，这会使实验不再兼容。我们表征了最佳的激励兼容推荐系统，首先是在两个阶段的游戏中，然后在无限马设置中。在这两种情况下，该系统仅诱导代理之间的部分而不是完全的信息共享（否则，当成本较低时，对风险的道路的开发过多）。
We consider a two-road dynamic routing game where the state of one of the roads (the "risky road") is stochastic and may change over time. This generates room for experimentation. A central planner may wish to induce some of the (finite number of atomic) agents to use the risky road even when the expected cost of travel there is high in order to obtain accurate information about the state of the road. Since agents are strategic, we show that in order to generate incentives for experimentation the central planner however needs to limit the number of agents using the risky road when the expected cost of travel on the risky road is low. In particular, because of congestion, too much use of the risky road when the state is favorable would make experimentation no longer incentive compatible. We characterize the optimal incentive compatible recommendation system, first in a two-stage game and then in an infinite-horizon setting. In both cases, this system induces only partial, rather than full, information sharing among the agents (otherwise there would be too much exploitation of the risky road when costs there are low).
