Statistical Foundations of Virtual Democracy
Statistical Foundations of Virtual Democracy
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发表时间：
2019-05
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通讯作者：
Anson Kahng;Min Kyung Lee;Ritesh Noothigattu;Ariel D. Procaccia;Alexandros Psomas
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作者：
Anson Kahng;Min Kyung Lee;Ritesh Noothigattu;Ariel D. Procaccia;Alexandros Psomas
虚拟民主是一种自动化决策的方法，通过学习个人偏好的模型，并在运行时汇总这些人对手头困境的预测偏好。其中一个关键问题是使用哪种聚合方法或投票规则;我们提供了一个新的统计观点，提供了指导。具体来说，我们寻求对预测误差具有鲁棒性的投票规则，因为它们对人们真实偏好的输出可能与它们对噪声估计的输出一致。我们证明了经典的Borda计数规则在这个意义上是强大的，而任何投票规则属于广泛的家庭的成对多数一致的规则是没有。我们的实证结果进一步支持，更精确地衡量，Borda计数的鲁棒性。
Virtual democracy is an approach to automating decisions, by learning models of the preferences of individual people, and, at runtime, aggregating the predicted preferences of those people on the dilemma at hand. One of the key questions is which aggregation method — or voting rule — to use; we offer a novel statistical viewpoint that provides guidance. Specifically, we seek voting rules that are robust to prediction errors, in that their output on people’s true preferences is likely to coincide with their output on noisy estimates thereof. We prove that the classic Borda count rule is robust in this sense, whereas any voting rule belonging to the wide family of pairwise-majority consistent rules is not. Our empirical results further support, and more precisely measure, the robustness of Borda count.