ReACT-TTC: Capacity-Aware Top Trading Cycles for Post-Choice Reassignment in Shared CPS

2026-01-31

AI Summary

This work addresses efficiency losses in shared cyber-physical systems caused by user deviations from assigned allocations by proposing a lightweight, compliance-oriented ex-post reallocation mechanism. The proposed approach extends the classical Top Trading Cycles (TTC) algorithm to accommodate many-to-one capacity constraints and scenarios with unallocated resources, introducing a capacity-aware cycle detection rule and integrating prospect theory to model user preferences. While preserving Pareto efficiency, individual rationality, and strategy-proofness, the method demonstrates significant improvements in user satisfaction and allocation quality, as validated through a real-world electric vehicle charging case study.

Technology Category

• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

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.

Problem

• user non-compliance
• shared CPS
• post-choice reassignment
• resource allocation
• preference-driven exchanges

Innovation

• Top-Trading-Cycles
• capacity-aware
• post-deviation reassignment
• Prospect Theory
• shared CPS