Learning Agents for Iterative Voting

Airiau, Stéphane; Grandi, Umberto; Studzinski Perotto, Filipo

Airiau, Stéphane; Grandi, Umberto; Studzinski Perotto, Filipo (2017), Learning Agents for Iterative Voting, in Rothe, Jörg, Algorithmic Decision Theory - 5th International Conference (ADT 2017), Springer International Publishing : Berlin Heidelberg, p. 139-152. 10.1007/978-3-319-67504-6_10

Type

Communication / Conférence

Date

2017

Conference title

5th International Conference (ADT 2017)

Conference date

2017-10

Conference city

Luxembourg

Conference country

Luxembourg

Book title

Algorithmic Decision Theory - 5th International Conference (ADT 2017)

Book author

Rothe, Jörg

Publisher

Springer International Publishing

Published in

Berlin Heidelberg

ISBN

978-3-319-67503-9

Number of pages

390

Pages

139-152

Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Grandi, Umberto

Studzinski Perotto, Filipo

Abstract (EN)

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.

Subjects / Keywords

Computational social choice; Iterative voting; Bandit algorithms