ReACT-TTC：面向共享 CPS 后选择重新分配的容量感知顶级交易周期
ReACT-TTC: Capacity-Aware Top Trading Cycles for Post-Choice Reassignment in Shared CPS
AI总结：
ReACT-TTC通过引入容量感知的顶级交易周期机制，解决共享CPS中用户不合规问题，提升用户满意度和分配质量。
摘要：
在人类决策存在的背景下，网络物理系统（CPS）越来越多地管理共享物理资源，其中系统分配的动作必须由用户或代理在物理世界中执行。在这种情况下，一个根本性的挑战是用户不合规：个体可能由于个人偏好或本地信息而偏离分配的资源，从而降低系统效率并要求轻量级重新分配方案。本文提出了一种针对共享资源CPS的后偏差重新分配框架，该框架基于任何初始分配算法，并且仅在用户偏离规定分配时被调用。我们推进了顶级交易周期（TTC）机制，以在偏差事件后实现自愿、基于偏好的交换，并将其扩展以处理许多对一资源容量和未分配资源的情况，这些情况不被经典TTC支持。我们正式化这些结构性案例，引入了容量感知的循环检测规则，并证明了终止性以及帕累托效率、个人理性性和策略无偏性的保持。进一步纳入了前景理论（PT）偏好模型，以捕捉现实用户的满意度行为。我们通过一个电动车（EV）充电案例研究展示了该框架的应用，使用真实世界数据，其中在非合规行为下，它提高了用户满意度和有效分配质量。
英文摘要：
Cyber-physical systems (CPS) increasingly manage shared physical resources in the presence of human decision-making, where system-assigned actions must be executed by users or agents in the physical world. A fundamental challenge in such settings is user non-compliance: individuals may deviate from assigned resources due to personal preferences or local information, degrading system efficiency and requiring light-weight reassignment schemes. This paper proposes a post-deviation reassignment framework for shared-resource CPS that operates on top of any initial allocation algorithm and is invoked only when users diverge from prescribed assignments. We advance the Top-Trading-Cycle (TTC) mechanism to enable voluntary, preference-driven exchanges after deviation events, and extend it to handle many-to-one resource capacities and unassigned resource conditions that are not supported by the classical TTC. We formalize these structural cases, introduce capacity-aware cycle-detection rules, and prove termination along with the preservation of Pareto efficiency, individual rationality, and strategy-proofness. A Prospect-Theoretic (PT) preference model is further incorporated to capture realistic user satisfaction behavior. We demonstrate the applicability of this framework on an electric-vehicle (EV) charging case study using real-world data, where it increases user satisfaction and effective assignment quality under non-compliant behavior.
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。
