Regularized Proportional Fairness Mechanism for Resource Allocation Without Money

TL;DR

This paper introduces RPF-Net, a neural network-based mechanism that balances social welfare maximization and incentive compatibility in resource allocation without monetary payments, using regularization to reduce exploitability.

Contribution

It proposes a novel neural network architecture, RPF-Net, that regularizes proportional fairness to approximate incentive-compatible resource allocation without money.

Findings

RPF-Net reduces incentive to misreport utilities.

The mechanism achieves competitive social welfare.

Theoretical bounds guarantee performance on finite and out-of-distribution data.

Abstract

Mechanism design in resource allocation studies dividing limited resources among self-interested agents whose satisfaction with the allocation depends on privately held utilities. We consider the problem in a payment-free setting, with the aim of maximizing social welfare while enforcing incentive compatibility (IC), i.e., agents cannot inflate allocations by misreporting their utilities. The well-known proportional fairness (PF) mechanism achieves the maximum possible social welfare but incurs an undesirably high exploitability (the maximum unilateral inflation in utility from misreport and a measure of deviation from IC). In fact, it is known that no mechanism can achieve the maximum social welfare and exact incentive compatibility (IC) simultaneously without the use of monetary incentives (Cole et al., 2013). Motivated by this fact, we propose learning an approximate mechanism that desirably trades off the competing objectives. Our main contribution is to design an innovative neural network architecture tailored to the resource allocation problem, which we name Regularized Proportional Fairness Network (RPF-Net). RPF-Net regularizes the output of the PF mechanism by a learned function approximator of the most exploitable allocation, with the aim of reducing the incentive for any agent to misreport. We derive generalization bounds that guarantee the mechanism performance when trained under finite and out-of-distribution samples and experimentally demonstrate the merits of the proposed mechanism compared to the state-of-the-art.