Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/227125
Authors: 
Burka, Dávid
Puppe, Clemens
Szepesváry, László
Tasnádi, Attila
Year of Publication: 
2020
Series/Report no.: 
KIT Working Paper Series in Economics No. 145
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.
Subjects: 
voting
social choice
neural networks
machine learning
Borda count
Document Type: 
Working Paper

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