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Voting: a machine learning approach

Burka, Dávid; Puppe, Clemens; Szepesváry, László; Tasnádi, Attila

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.

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Volltext §
DOI: 10.5445/IR/1000127945
Veröffentlicht am 22.12.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Volkswirtschaftslehre (ECON)
Publikationstyp Forschungsbericht/Preprint
Publikationsmonat/-jahr 11.2020
Sprache Englisch
Identifikator ISSN: 2190-9806
KITopen-ID: 1000127945
Verlag Karlsruher Institut für Technologie (KIT)
Umfang 22 S.
Serie Working Paper Series in Economics ; 145
Schlagwörter voting, social choice, neural networks, machine learning, Borda count
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