8 research outputs found
Deep Learning for Two-Sided Matching
We initiate the use of a multi-layer neural network to model two-sided
matching and to explore the design space between strategy-proofness and
stability. It is well known that both properties cannot be achieved
simultaneously but the efficient frontier in this design space is not
understood. We show empirically that it is possible to achieve a good
compromise between stability and strategy-proofness-substantially better than
that achievable through a convex combination of deferred acceptance (stable and
strategy-proof for only one side of the market) and randomized serial
dictatorship (strategy-proof but not stable)
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Automated Mechanism Design without Money via Machine Learning
We use statistical machine learning to develop methods for automatically designing mechanisms in domains without money. Our goal is to find a mechanism that best approximates a given target function subject to a design constraint such as strategy-proofness or stability. The proposed approach involves identifying a rich parametrized class of mechanisms that resemble discriminant-based multiclass classifiers, and relaxing the resulting search problem into an SVM-style surrogate optimization problem. We use this methodology to design strategy-proof mechanisms for social choice problems with single-peaked preferences, and stable mechanisms for two-sided matching problems. To the best of our knowledge, ours is the first automated approach for designing stable matching rules. Experiments on synthetic and real-world data confirm the usefulness of our methods.Engineering and Applied Science