Arbeitspapier

Neural networks would 'vote' according to Borda's rule

Can neural networks learn to select an alternative based on a systematic aggregation of conflicting individual preferences (i.e. a 'voting rule')? And if so, which voting rule best describes their behavior? We show that a prominent neural network can be trained to respect two fundamental principles of voting theory, the unanimity principle and the Pareto property. Building on this positive result, we train the neural network on profiles of ballots possessing a Condorcet winner, a unique Borda winner, and a unique plurality winner, respectively. We investigate which social outcome the trained neural network chooses, and find that among a number of popular voting rules its behavior mimics most closely the Borda rule. Indeed, the neural network chooses the Borda winner most often, no matter on which voting rule it was trained. Neural networks thus seem to give a surprisingly clear-cut answer to one of the most fundamental and controversial problems in voting theory: the determination of the most salient election method.

Sprache
Englisch

Erschienen in
Series: KIT Working Paper Series in Economics ; No. 96

Klassifikation
Wirtschaft
Thema
voting
social choice
neural networks
machine learning
Borda count

Ereignis
Geistige Schöpfung
(wer)
Burka, David
Puppe, Clemens
Szepesvary, Laszlo
Tasnadi, Attila
Ereignis
Veröffentlichung
(wer)
Karlsruher Institut für Technologie (KIT), Institut für Volkswirtschaftslehre (ECON)
(wo)
Karlsruhe
(wann)
2016

DOI
doi:10.5445/IR/1000062014
Handle
URN
urn:nbn:de:swb:90-620147
Letzte Aktualisierung
10.03.2025, 11:46 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Burka, David
  • Puppe, Clemens
  • Szepesvary, Laszlo
  • Tasnadi, Attila
  • Karlsruher Institut für Technologie (KIT), Institut für Volkswirtschaftslehre (ECON)

Entstanden

  • 2016

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