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
APA
Chicago
ICMJE
MLA
NLM
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