Our AAMAS 2020 paper “Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes” (@SilviuPitis and me) is online! We analyze voting rules under a setup where voters get noisy estimates of some underlying ground truth.
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This is interesting especially when the voting rule has access to auxiliary information e.g. some proxy for voter experience. Our model is applicable for cooperative policy making and peer review--lots more exciting directions to pursue!
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We show our rules compare favorably to rules learned by order-invariant neural networks under different noise models. paper: https://arxiv.org/abs/2001.10092 code:https://github.com/spitis/objective_social_choice …
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