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Developing the aggregate empirical side of computational social choice

Developing the aggregate empirical side of computational social choice The aggregate empirical side of computational social choice has received relatively little attention. This paper provides a progress report on our on-going research project on statistical characterizations of the outcomes of vote-casting processes. We describe a statistical model that is capable of generating voting situations for three-candidate elections that have a distribution very similar to that of observed voting situations. We show that our simulated voting situations can provide interesting insights into the question of which voting rule is most likely to identify the best candidate. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Developing the aggregate empirical side of computational social choice

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References (23)

Publisher
Springer Journals
Copyright
Copyright © 2013 by Springer Science+Business Media Dordrecht
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mathematics, general; Computer Science, general; Statistical Physics, Dynamical Systems and Complexity
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/s10472-013-9360-z
Publisher site
See Article on Publisher Site

Abstract

The aggregate empirical side of computational social choice has received relatively little attention. This paper provides a progress report on our on-going research project on statistical characterizations of the outcomes of vote-casting processes. We describe a statistical model that is capable of generating voting situations for three-candidate elections that have a distribution very similar to that of observed voting situations. We show that our simulated voting situations can provide interesting insights into the question of which voting rule is most likely to identify the best candidate.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: May 31, 2013

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