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Fair and efficient social choice in dynamic settings

Publication ,  Conference
Freeman, R; Zahedi, SM; Conitzer, V
Published in: IJCAI International Joint Conference on Artificial Intelligence
January 1, 2017

We study a dynamic social choice problem in which an alternative is chosen at each round according to the reported valuations of a set of agents. In the interests of obtaining a solution that is both efficient and fair, we aim to maximize the long-term Nash welfare, which is the product of all agents' utilities. We present and analyze two greedy algorithms for this problem, including the classic Proportional Fair (PF) algorithm. We analyze several versions of the algorithms and how they relate, and provide an axiomatization of PF. Finally, we evaluate the algorithms on data gathered from a computer systems application.

Duke Scholars

Published In

IJCAI International Joint Conference on Artificial Intelligence

DOI

ISSN

1045-0823

ISBN

9780999241103

Publication Date

January 1, 2017

Volume

0

Start / End Page

4580 / 4587
 

Citation

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Chicago
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Freeman, R., Zahedi, S. M., & Conitzer, V. (2017). Fair and efficient social choice in dynamic settings. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 0, pp. 4580–4587). https://doi.org/10.24963/ijcai.2017/639
Freeman, R., S. M. Zahedi, and V. Conitzer. “Fair and efficient social choice in dynamic settings.” In IJCAI International Joint Conference on Artificial Intelligence, 0:4580–87, 2017. https://doi.org/10.24963/ijcai.2017/639.
Freeman R, Zahedi SM, Conitzer V. Fair and efficient social choice in dynamic settings. In: IJCAI International Joint Conference on Artificial Intelligence. 2017. p. 4580–7.
Freeman, R., et al. “Fair and efficient social choice in dynamic settings.” IJCAI International Joint Conference on Artificial Intelligence, vol. 0, 2017, pp. 4580–87. Scopus, doi:10.24963/ijcai.2017/639.
Freeman R, Zahedi SM, Conitzer V. Fair and efficient social choice in dynamic settings. IJCAI International Joint Conference on Artificial Intelligence. 2017. p. 4580–4587.

Published In

IJCAI International Joint Conference on Artificial Intelligence

DOI

ISSN

1045-0823

ISBN

9780999241103

Publication Date

January 1, 2017

Volume

0

Start / End Page

4580 / 4587