Deep Learning for Two-Sided Matching
Sai Srivatsa Ravindranath, Zhe Feng, Shira Li, Jonathan Ma, Scott D., Kominers, David C. Parkes
TL;DR
This paper explores using deep learning to design two-sided matching mechanisms, aiming to understand tradeoffs between strategy-proofness and stability, and introduces novel differentiable surrogates to improve the efficiency frontier.
Contribution
It introduces differentiable surrogates for strategy-proofness and stability, enabling the training of mechanisms that outperform traditional methods on the efficiency frontier.
Findings
Learned mechanisms surpass traditional baselines in the efficiency frontier.
Differentiable surrogates effectively quantify strategy-proofness and stability.
New machine learning approach opens possibilities for market design.
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…
Peer Reviews
Decision·Submitted to ICLR 2025
1. The paper initiates the study of using neural networks to discover two-sided mechanisms from data. The idea of employing neural networks for mechanism learning is compelling. 2. The learned mechanism is capable of identifying a more efficient frontier than traditional approaches. 3. The paper establishes a bridge between deep learning and traditional algorithmic game theory, which I believe is interesting to ICLR community.
1. The experiments are limited to two prior distributions of preferences; incorporating additional distributions would be beneficial. 2. There are no diagrams to illustrate the architecture of the proposed neural network.
The idea of studying the tradeoff between SP and stability with deep learning is novel and intuitive. The paper opens up a new direction in mechanism design with machine learning techniques. I generally believe that this is a good paper with novel ideas and clear presentation.
The experiment is conducted on synthetic data as compared to real-world data.
1 The use of deep learning to design two-sided matching mechanisms is highly innovative and represents a significant shift from traditional economic approaches. By employing neural networks, the paper opens up new possibilities for discovering previously unknown tradeoffs between stability and strategy-proofness. 2 The paper successfully demonstrates that deep learning can uncover a superior tradeoff frontier compared to traditional convex combinations of existing mechanisms (DA, TTC, and RSD).
1 One major limitation of the proposed method is its scalability. While the paper claims to scale efficiently to markets with up to 50 agents on each side, this size is relatively small for many real-world applications. In large-scale matching markets, such as job markets or school admissions, the number of participants can easily reach into the thousands. Therefore, the current methodology may struggle to handle the complexity of large-scale matching problems, where the number of workers and fi
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Taxonomy
TopicsGame Theory and Voting Systems · Auction Theory and Applications · Privacy-Preserving Technologies in Data