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

Burka, Dávid and Puppe, Clemens and Szepesváry, László and Tasnádi, Attila (2016) Neural networks would 'vote' according to Borda's Rule. Working Paper. Corvinus University of Budapest Faculty of Economics, Budapest.

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Can neural networks learn to select an alternative based on a systematic aggregation of convicting 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.

Item Type:Monograph (Working Paper)
Series Name:Corvinus Economics Working Papers - CEWP
Series Number / Identification Number:2016/13
Uncontrolled Keywords:voting, social choice, neural networks, machine learning, Borda count
JEL classification:D71 - Analysis of Collective Decision-Making: Social Choice; Clubs; Committees; Associations
Subjects:Mathematics, Econometrics
Projects:OTKA K 112975, MTA-BCE "Lendület" Strategic Interactions Research Group
ID Code:2498
Deposited By: Ádám Hoffmann
Deposited On:03 Nov 2016 19:28
Last Modified:05 Nov 2016 08:17

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