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On the axiomatic characterization of runoff voting rules

Publication ,  Conference
Freeman, R; Brill, M; Conitzer, V
Published in: Proceedings of the National Conference on Artificial Intelligence
January 1, 2014

Runoff voting rules such as single transferable vote (STV) and Baldwin's rule are of particular interest in computational social choice due to their recursive nature and hardness of manipulation, as well as in (human) practice because they are relatively easy to understand. However, they are not known for their compliance with desirable axiomatic properties, which we attempt to rectify here. We characterize runoff rules that are based on scoring rules using two axioms: a weakening of local independence of irrelevant alternatives and a variant of population-consistency. We then show, as our main technical result, that STV is the only runoff scoring rule satisfying an independence-of-clones property. Furthermore, we provide axiomatizations of Baldwin's rule and Coombs' rule.

Duke Scholars

Published In

Proceedings of the National Conference on Artificial Intelligence

ISBN

9781577356776

Publication Date

January 1, 2014

Volume

1

Start / End Page

675 / 681
 

Citation

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Freeman, R., Brill, M., & Conitzer, V. (2014). On the axiomatic characterization of runoff voting rules. In Proceedings of the National Conference on Artificial Intelligence (Vol. 1, pp. 675–681).
Freeman, R., M. Brill, and V. Conitzer. “On the axiomatic characterization of runoff voting rules.” In Proceedings of the National Conference on Artificial Intelligence, 1:675–81, 2014.
Freeman R, Brill M, Conitzer V. On the axiomatic characterization of runoff voting rules. In: Proceedings of the National Conference on Artificial Intelligence. 2014. p. 675–81.
Freeman, R., et al. “On the axiomatic characterization of runoff voting rules.” Proceedings of the National Conference on Artificial Intelligence, vol. 1, 2014, pp. 675–81.
Freeman R, Brill M, Conitzer V. On the axiomatic characterization of runoff voting rules. Proceedings of the National Conference on Artificial Intelligence. 2014. p. 675–681.

Published In

Proceedings of the National Conference on Artificial Intelligence

ISBN

9781577356776

Publication Date

January 1, 2014

Volume

1

Start / End Page

675 / 681