Deep Learning for Two-Sided Matching

ICLR 2025 Conference Submission10726 Authors

Keywords: Mechanism Design, Matching Markets, Game Theory, Differentiable Economics, Two-Sided Matching

Abstract: We initiate the study of deep learning for the automated design of two-sided matching mechanisms. What is of most interest is to use machine learning to understand the possibility of new tradeoffs between *strategy-proofness* and *stability*. These properties cannot be achieved simultaneously, but the efficient frontier is not understood. We introduce novel differentiable surrogates for quantifying ordinal strategy-proofness and stability and use them to train differentiable matching mechanisms that map discrete preferences to valid randomized matchings. We demonstrate that the efficient frontier characterized by these learned mechanisms is substantially better than that achievable through a convex combination of baselines of *deferred acceptance* (stable and strategy-proof for only one side of the market), *top trading cycles* (strategy-proof for one side, but not stable), and *randomized serial dictatorship* (strategy-proof for both sides, but not stable). This gives a new target for economic theory and opens up new possibilities for machine learning pipelines in matching market design.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 10726