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Learning to Elect
Cem Anil · Xuchan Bao

Tue Dec 07 04:30 PM -- 06:00 PM (PST) @ None #None

Voting systems have a wide range of applications including recommender systems, web search, product design and elections. Limited by the lack of general-purpose analytical tools, it is difficult to hand-engineer desirable voting rules for each use case. For this reason, it is appealing to automatically discover voting rules geared towards each scenario. In this paper, we show that set-input neural network architectures such as Set Transformers, fully-connected graph networks and DeepSets are both theoretically and empirically well-suited for learning voting rules. In particular, we show that these network models can not only mimic a number of existing voting rules to compelling accuracy --- both position-based (such as Plurality and Borda) and comparison-based (such as Kemeny, Copeland and Maximin) --- but also discover near-optimal voting rules that maximize different social welfare functions. Furthermore, the learned voting rules generalize well to different voter utility distributions and election sizes unseen during training.

Author Information

Cem Anil (University of Toronto; Vector Institute)

I'm a first year PhD student at the University of Toronto and Vector Institute, supervised by Roger Grosse and Geoffrey Hinton.

Xuchan Bao (University of Toronto)

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