MenuNet: A Strategy-Proof Mechanism for Matching Markets
Zhaohong Sun, Makoto Yokoo

TL;DR
MenuNet introduces a neural framework for designing strategy-proof, stable matching mechanisms that handle complex constraints and optimize fairness and efficiency trade-offs.
Contribution
It presents a novel neural-based approach to generate personalized menus ensuring strategy-proofness and balancing stability properties in matching markets.
Findings
MenuNet outperforms RSD in envy minimization.
MenuNet outperforms DA in waste reduction.
It maintains scalability and computational efficiency.
Abstract
Strategy-proofness is a fundamental desideratum in mechanism design, ensuring truthful reporting and robust participation. Stability is another central requirement in matching markets, widely adopted in applications such as school choice and labor market clearing. In practice, however, these markets are invariably governed by complex distributional constraints, ranging from diversity quotas and regional balance to global capacity slacks, under which stable matchings often fail to exist. This raises a fundamental question: how to distribute unavoidable instability across agents while preserving strategy-proofness? To address this, we propose \texttt{MenuNet}, a strategy-proof mechanism design framework based on a neural representation of menus. Rather than directly constructing assignments, \texttt{MenuNet} learns to generate personalized probabilistic menus, from which assignments are…
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