Voting: A machine learning approach

Dávid Burka, Clemens Puppe, László Szepesváry and Attila Tasnádi

No 145, Working Paper Series in Economics from Karlsruhe Institute of Technology (KIT), Department of Economics and Management

Abstract: Voting rules can be assessed from quite different perspectives: the axiomatic, the pragmatic, in terms of computational or conceptual simplicity, susceptibility to manipulation, and many others aspects. In this paper, we take the machine learning perspective and ask how 'well' a few prominent voting rules can be learned by a neural network. To address this question, we train the neural network to choosing Condorcet, Borda, and plurality winners, respectively. Remarkably, our statistical results show that, when trained on a limited (but still reasonably large) sample, the neural network mimics most closely the Borda rule, no matter on which rule it was previously trained. The main overall conclusion is that the necessary training sample size for a neural network varies significantly with the voting rule, and we rank a number of popular voting rules in terms of the sample size required.

Keywords: voting; social choice; neural networks; machine learning; Borda count (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big and nep-cmp
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Journal Article: Voting: A machine learning approach (2022)
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