Learning Neural Strategy-Proof Matching Mechanism from Examples

• URL: http://arxiv.org/abs/2410.19384v1
• Date: Fri, 25 Oct 2024 08:34:25 GMT
• Title: Learning Neural Strategy-Proof Matching Mechanism from Examples
• Authors: Ryota Maruo, Koh Takeuchi, Hisashi Kashima,
• Abstract summary: We develop a novel attention-based neural network called NeuralSD, which can learn a strategy-proof mechanism from a human-crafted dataset. We conducted experiments to learn a strategy-proof matching from matching examples with different numbers of agents.
• Score: 24.15688619889342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing effective two-sided matching mechanisms is a major problem in mechanism design, and the goodness of matching cannot always be formulated. The existing work addresses this issue by searching over a parameterized family of mechanisms with certain properties by learning to fit a human-crafted dataset containing examples of preference profiles and matching results. However, this approach does not consider a strategy-proof mechanism, implicitly assumes the number of agents to be a constant, and does not consider the public contextual information of the agents. In this paper, we propose a new parametric family of strategy-proof matching mechanisms by extending the serial dictatorship (SD). We develop a novel attention-based neural network called NeuralSD, which can learn a strategy-proof mechanism from a human-crafted dataset containing public contextual information. NeuralSD is constructed by tensor operations that make SD differentiable and learns a parameterized mechanism by estimating an order of SD from the contextual information. We conducted experiments to learn a strategy-proof matching from matching examples with different numbers of agents. We demonstrated that our method shows the superiority of learning with context-awareness over a baseline in terms of regression performance and other metrics.

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