Token Economy for Fair and Efficient Dynamic Resource Allocation in Congestion Games

2026-03-18

AI Summary

This work addresses the inefficiency arising from self-interested behavior in shared economies and the fairness concerns inherent in conventional monetary mechanisms due to wealth inequality. To reconcile efficiency with equity while preserving user autonomy, the paper proposes a token-based mechanism grounded in integer pricing. The authors innovatively model the token economy as a continuous-time dynamic game, leveraging mean-field approximation and evolutionary dynamics of boundedly rational agents to achieve a strong approximation of finite-population interactions and enable closed-form control. Theoretically, the mechanism guarantees convergence—regardless of initial conditions—to an allocation that simultaneously optimizes system efficiency and fairness.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Self-interested behavior in sharing economies often leads to inefficient aggregate outcomes compared to a centrally coordinated allocation, ultimately harming users. Yet, centralized coordination removes individual decision power. This issue can be addressed by designing rules that align individual preferences with system-level objectives. Unfortunately, rules based on conventional monetary mechanisms introduce unfairness by discriminating among users based on their wealth. To solve this problem, in this paper, we propose a token-based mechanism for congestion games that achieves efficient and fair dynamic resource allocation. Specifically, we model the token economy as a continuous-time dynamic game with finitely many boundedly rational agents, explicitly capturing their evolutionary policy-revision dynamics. We derive a mean-field approximation of the finite-population game and establish strong approximation guarantees between the mean-field and the finite-population games. This approximation enables the design of integer tolls in closed form that provably steer the aggregate dynamics toward an optimal efficient and fair allocation from any initial condition.

Problem

• Token Economy
• Congestion Games
• Fairness
• Dynamic Resource Allocation
• Bounded Rationality

Innovation

• token economy
• congestion games
• mean-field approximation
• dynamic resource allocation
• fairness