Mechanism design, a branch of economics, aims to design rules that can autonomously achieve desired outcomes in resource allocation and public decision making. The research on mechanism design using machine learning is called automated mechanism design or mechanism learning. In our research, we constructed a new network based on the existing method for single auctions and aimed to automatically design a mechanism by applying it to double auctions. In particular, we focused on the following four desirable properties for the mechanism: individual rationality, balanced budget, Pareto efficiency, and incentive compatibility. We conducted experiments assuming a small-scale double auction and clarified how deterministic the trade matching of the obtained mechanism is. We also confirmed how much the learnt mechanism satisfies the four properties compared to two representative protocols. As a result, we verified that the mechanism is more budget-balanced than the VCG protocol and more economically efficient than the MD protocol, with the incentive compatibility mostly guaranteed.

翻译：机制设计作为经济学的一个分支，旨在设计能够自主实现资源分配与公共决策中期望结果的规则。利用机器学习进行机制设计的研究被称为自动化机制设计或机制学习。在本研究中，我们基于现有单拍卖方法构建了一种新网络，旨在通过将其应用于双拍卖来自动设计机制。我们特别关注机制应具备的以下四个理想特性：个体理性、预算平衡、帕累托效率与激励相容性。通过假设小规模双拍卖场景进行实验，我们明确了所得机制的交易匹配确定性程度，并对比两种典型协议验证了学习机制对四项特性的满足程度。结果表明，该机制相比VCG协议具有更好的预算平衡性，较MD协议展现出更高的经济效率，且激励相容性基本得到保障。