论文标题
部分可观测时空混沌系统的无模型预测
Avoiding Unintended Consequences: How Incentives Aid Information Provisioning in Bayesian Congestion Games
论文作者
论文摘要
当用户缺乏对各种系统参数的特定知识时,他们的不确定性可能会导致他们在决策中造成不良偏差。为了减轻这一点,知情的系统运营商可以选择向不知情的用户发出信息,以期说服他们采取更可取的行动。在这项工作中,我们在平行网络上贝叶斯拥堵游戏的背景下研究公共和真实的信号传导机制。我们提供了信号政策可以提供的可能利益的界限,也没有同时使用货币激励措施。我们发现,尽管揭示信息可以降低某些设置的系统成本,但它也可能是有害的,而且性能差,而不是没有信号。但是,通过同时利用信号传导和激励机制,系统操作员可以保证揭示信息不会恶化性能,同时提供类似的改进机会。这些发现从我们根据信号策略所能提供的收益获得的封闭形式范围出现。我们提供了一个数字示例,该示例说明了这种现象,即当不使用激励措施时,揭示更多信息会降低性能并在使用激励措施时提高性能。
When users lack specific knowledge of various system parameters, their uncertainty may lead them to make undesirable deviations in their decision making. To alleviate this, an informed system operator may elect to signal information to uninformed users with the hope of persuading them to take more preferable actions. In this work, we study public and truthful signalling mechanisms in the context of Bayesian congestion games on parallel networks. We provide bounds on the possible benefit a signalling policy can provide with and without the concurrent use of monetary incentives. We find that though revealing information can reduce system cost in some settings, it can also be detrimental and cause worse performance than not signalling at all. However, by utilizing both signalling and incentive mechanisms, the system operator can guarantee that revealing information does not worsen performance while offering similar opportunities for improvement. These findings emerge from the closed form bounds we derive on the benefit a signalling policy can provide. We provide a numerical example which illustrates the phenomenon that revealing more information can degrade performance when incentives are not used and improves performance when incentives are used.