DeepVoting:利用定制嵌入学习投票规则

Leonardo Matone, Ben Abramowitz, Nicholas Mattei, Avinash Balakrishnan
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引用次数: 0

摘要

将多个代理的偏好聚合成一个集体决策,是计算机科学领域许多重要问题(包括信息检索、强化学习和推荐系统)的共同步骤。正如社会选择理论(Social Choice Theory)所表明的那样,为具有特定属性(公理)的聚合规则设计算法可能是个难题,在某些情况下甚至是不可能的。与其手工设计算法,不如从数据中学习聚合规则,尤其是投票规则。然而,这一领域的前人工作需要极其庞大的模型,或受限于偏好表示(即嵌入)的选择。我们将设计一个好的投票规则的问题重塑为一个学习概率版投票规则的问题,该规则输出一组候选人的分布。具体来说,我们利用神经网络从文献中学习概率社会选择函数。我们的研究表明,如果嵌入是根据学习目标量身定做的,那么从社会选择文献中得出的偏好特征嵌入可以让我们更高效地学习现有的投票规则,并比其他工作更容易扩展到更大的选民群体。此外,我们还证明,利用嵌入学习到的规则可以进行调整,以创建具有改进公理属性的新型投票规则。也就是说,我们证明了现有的投票规则只需稍加修改,就能对抗概率版的 "无展示悖论"。
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DeepVoting: Learning Voting Rules with Tailored Embeddings
Aggregating the preferences of multiple agents into a collective decision is a common step in many important problems across areas of computer science including information retrieval, reinforcement learning, and recommender systems. As Social Choice Theory has shown, the problem of designing algorithms for aggregation rules with specific properties (axioms) can be difficult, or provably impossible in some cases. Instead of designing algorithms by hand, one can learn aggregation rules, particularly voting rules, from data. However, the prior work in this area has required extremely large models, or been limited by the choice of preference representation, i.e., embedding. We recast the problem of designing a good voting rule into one of learning probabilistic versions of voting rules that output distributions over a set of candidates. Specifically, we use neural networks to learn probabilistic social choice functions from the literature. We show that embeddings of preference profiles derived from the social choice literature allows us to learn existing voting rules more efficiently and scale to larger populations of voters more easily than other work if the embedding is tailored to the learning objective. Moreover, we show that rules learned using embeddings can be tweaked to create novel voting rules with improved axiomatic properties. Namely, we show that existing voting rules require only minor modification to combat a probabilistic version of the No Show Paradox.
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