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Learning Agents for Iterative Voting

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Algorithmic Decision Theory (ADT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10576))

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Abstract

This paper assesses the learning capabilities of agents in a situation of collective choice. Each agent is endowed with a private preference concerning a number of alternative candidates, and participates in an iterated plurality election. Agents get rewards depending on the winner of each election, and adjust their voting strategy using reinforcement learning. By conducting extensive simulations, we show that our agents are capable of learning how to take decisions at the level of well-known voting procedures, and that these decisions maintain good choice-theoretic properties when increasing the number of agents or candidates.

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Notes

  1. 1.

    See, e.g., [13] for a proof.

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Author information

Authors and Affiliations

  1. Université Paris-Dauphine, PSL Research University, LAMSADE, Paris, France

    Stéphane Airiau

  2. IRIT, University of Toulouse, Toulouse, France

    Umberto Grandi & Filipo Studzinski Perotto

Authors
  1. Stéphane Airiau
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  2. Umberto Grandi
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  3. Filipo Studzinski Perotto
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Corresponding author

Correspondence to Stéphane Airiau .

Editor information

Editors and Affiliations

  1. Heinrich Heine University, Düsseldorf, Germany

    Jörg Rothe

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Airiau, S., Grandi, U., Perotto, F.S. (2017). Learning Agents for Iterative Voting. In: Rothe, J. (eds) Algorithmic Decision Theory. ADT 2017. Lecture Notes in Computer Science(), vol 10576. Springer, Cham. https://doi.org/10.1007/978-3-319-67504-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-67504-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67503-9

  • Online ISBN: 978-3-319-67504-6

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