Voting: A machine learning approach

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: KIT Working Paper Series in Economics ; No. 145

Classification and Topics

• Classification: Wirtschaft
• Subject: voting ; social choice ; neural networks ; machine learning ; Borda count

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Burka, Dávid ; Puppe, Clemens ; Szepesváry, László ; Tasnádi, Attila
• Event: Veröffentlichung
• (who): Karlsruher Institut für Technologie (KIT), Institut für Volkswirtschaftslehre (ECON)
• (where): Karlsruhe
• (when): 2020

Further information

• Handle: http://hdl.handle.net/10419/227125
• Last update: 10.03.2025, 11:43 AM CET