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

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Listed:
  • Burka, David
  • Puppe, Clemens
  • Szepesvary, Laszlo
  • Tasnadi, Attila

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.

Suggested Citation

  • Burka, David & Puppe, Clemens & Szepesvary, Laszlo & Tasnadi, Attila, 2016. "Neural networks would 'vote' according to Borda's rule," Working Paper Series in Economics 96, Karlsruhe Institute of Technology (KIT), Department of Economics and Business Engineering.
  • Handle: RePEc:zbw:kitwps:96
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    References listed on IDEAS

    as
    1. Edith Elkind & Piotr Faliszewski & Arkadii Slinko, 2015. "Distance rationalization of voting rules," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 45(2), pages 345-377, September.
    2. Nehring, Klaus & Pivato, Marcus, 2013. "Majority rule in the absence of a majority," MPRA Paper 46721, University Library of Munich, Germany.
    3. Ayça Giritligil Kara & Murat Sertel, 2005. "Does majoritarian approval matter in selecting a social choice rule? An exploratory panel study," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 25(1), pages 43-73, October.
    4. repec:cup:apsrev:v:72:y:1978:i:03:p:831-847_15 is not listed on IDEAS
    5. Sgroi, Daniel & Zizzo, Daniel John, 2009. "Learning to play 3×3 games: Neural networks as bounded-rational players," Journal of Economic Behavior & Organization, Elsevier, vol. 69(1), pages 27-38, January.
    6. McNelis, Paul D., 2004. "Neural Networks in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780124859678.
    7. Mathias Risse, 2005. "Why the count de Borda cannot beat the Marquis de Condorcet," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 25(1), pages 95-113, October.
    8. Smith, John H, 1973. "Aggregation of Preferences with Variable Electorate," Econometrica, Econometric Society, vol. 41(6), pages 1027-1041, November.
    9. Young, H. P., 1974. "An axiomatization of Borda's rule," Journal of Economic Theory, Elsevier, vol. 9(1), pages 43-52, September.
    10. Fishburn, Peter C., 1978. "Axioms for approval voting: Direct proof," Journal of Economic Theory, Elsevier, vol. 19(1), pages 180-185, October.
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    More about this item

    Keywords

    voting; social choice; neural networks; machine learning; Borda count;

    JEL classification:

    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations

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