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

TL;DR

This paper introduces a token-based mechanism for congestion games that ensures fair and efficient resource allocation by aligning individual incentives with system-wide goals, avoiding unfairness of monetary mechanisms.

Contribution

It proposes a novel token economy model for congestion games, providing a mean-field approximation and closed-form tolls that guarantee optimal fair and efficient outcomes.

Findings

Token mechanism achieves fair and efficient allocations.

Mean-field approximation closely matches finite-population dynamics.

Closed-form tolls steer the system to optimal outcomes.

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.