Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/147982
Authors: 
Burka, David
Puppe, Clemens
Szepesvary, Laszlo
Tasnadi, Attila
Year of Publication: 
2016
Series/Report no.: 
Working Paper Series in Economics, Karlsruher Institut für Technologie (KIT) 96
Abstract: 
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.
Subjects: 
voting
social choice
neural networks
machine learning
Borda count
Document Type: 
Working Paper

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