新状态获取中时变信息不对称的抑制
Taming Time-Varying Information Asymmetry in Fresh Status Acquisition
摘要：
许多在线平台正在提供有价值的实时内容（例如，通过连续获取不同兴趣点（PoI）的状态，在状态获取中，确定POI应该多久将其状态上传到平台是具有挑战性的，因为他们是自私的，具有私人和可能随时间变化的偏好。本文考虑了一种通用的多周期状态获取系统，以最大化总社会福利和保证平台新鲜度为目标。新鲜度是通过一个称为信息年龄的度量来衡量的。为此，我们设计了一个长期分解（LtD）机制，以解决随时间变化的信息不对称。其核心思想是构建一个虚拟的社会福利，只依赖于当前的私人信息，然后分解为多个分布式投标问题的POI和平台的每个周期的操作。LtD机制使平台能够在收益最大化和新鲜度条件之间实现可调的权衡。此外，与具有对称信息的基准相比，LtD机制保持相同的社会性能，并且渐进地确保平台新鲜度条件。基于真实世界数据的数值结果表明，当平台更加关注收益最大化时，每个PoI在长期内仍然获得非负收益。
英文摘要：
Many online platforms are providing valuable real-time contents (e.g., traffic) by continuously acquiring the status of different Points of Interest (PoIs). In status acquisition, it is challenging to determine how frequently a PoI should upload its status to a platform, since they are self-interested with private and possibly time-varying preferences. This paper considers a general multi-period status acquisition system, aiming to maximize the aggregate social welfare and ensure the platform freshness. The freshness is measured by a metric termed age of information. For this goal, we devise a long-term decomposition (LtD) mechanism to resolve the time-varying information asymmetry. The key idea is to construct a virtual social welfare that only depends on the current private information, and then decompose the per-period operation into multiple distributed bidding problems for the PoIs and platforms. The LtD mechanism enables the platforms to achieve a tunable trade-off between payoff maximization and freshness conditions. Moreover, the LtD mechanism retains the same social performance compared to the benchmark with symmetric information and asymptotically ensures the platform freshness conditions. Numerical results based on real-world data show that when the platforms pay more attention to payoff maximization, each PoI still obtains a non-negative payoff in the long-term.
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。
