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Voting – Publication by Dávid Burka, László Szepesváry and Attila Tasnádi

2022-06-03 10:49:37

The publication by Dávid Burka, László Szepesváry and Attila Tasnádi was published in the European Journal of Operational Research.

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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 prominent voting rules compare in terms of their learnability 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. 



Burka Dávid david.burka@uni-corvinus.hu Rektori Szervezet / Adatelemzés és Informatika Intézet / Számítástudományi Tanszék
Adjunktus / Assistant Professor
Sóház, S.127
Phone: +36 1 482 7480 • Ext: 7480
Dr. Tasnádi Attila attila.tasnadi@uni-corvinus.hu Rektori Szervezet / Adatelemzés és Informatika Intézet / Matematika Tanszék
Egyetemi Tanár / Professor
Sóház, 221/B
Phone: +36 1 482 7446 • Ext: 7446

László Szepesváry 

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GEN.:2024.06.23. - 04:41:31