In the face of adverse motives, it is indispensable to achieve a consensus. Elections have been the canonical way by which modern democracy has operated since the 17th century. Nowadays, they regulate markets, provide an engine for modern recommender systems or peer-to-peer networks, and remain the main approach to represent democracy. However, a desirable universal voting rule that satisfies all hypothetical scenarios is still a challenging topic, and the design of these systems is at the forefront of mechanism design research. Automated mechanism design is a promising approach, and recent works have demonstrated that set-invariant architectures are uniquely suited to modelling electoral systems. However, various concerns prevent the direct application to real-world settings, such as robustness to strategic voting. In this paper, we generalise the expressive capability of learned voting rules, and combine improvements in neural network architecture with adversarial training to improve the resilience of voting rules while maximizing social welfare. We evaluate the effectiveness of our methods on both synthetic and real-world datasets. Our method resolves critical limitations of prior work regarding learning voting rules by representing elections using bipartite graphs, and learning such voting rules using graph neural networks. We believe this opens new frontiers for applying machine learning to real-world elections.

翻译：面对不利动机时，达成共识至关重要。自17世纪以来，选举一直是现代民主运作的典范方式。如今，选举规范市场运行，为现代推荐系统或点对点网络提供动力引擎，并仍是代表民主的主要途径。然而，设计一种能满足所有假设场景的理想普适投票规则仍是一个具有挑战性的课题，这类系统的设计处于机制设计研究的前沿。自动化机制设计是一种前景广阔的方法，近期研究表明集合不变架构特别适用于选举系统建模。但诸多因素阻碍了其在实际场景中的直接应用，例如对策略性投票的鲁棒性。本文推广了学习型投票规则的表达能力，将神经网络架构的改进与对抗训练相结合，在最大化社会福利的同时提升投票规则的弹性。我们在合成数据集和真实数据集上评估了方法的有效性。我们的方法通过使用二分图表示选举，并利用图神经网络学习此类投票规则，解决了先前工作中关于学习投票规则的关键局限性。我们相信这为机器学习应用于现实选举开辟了新的前沿领域。