Fair enough: guaranteeing approximate maximin shares
By Ariel D. Procaccia, Junxing Wang. ACM Conference on Economics and Computation, 2014
370 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/1c86a777f00cb37cc9acd884501844374e67db1a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Nisarg Shah
- Ariel D. Procaccia
- Ioannis Caragiannis
- Hervé Moulin
- David Kurokawa
- Junxing Wang
- Gabriel D. Carroll
- Jingyi Xue
In Management Science and Operations Research
- Nisarg Shah
- Ariel D. Procaccia
- Ioannis Caragiannis
- Hervé Moulin
- David Kurokawa
- Junxing Wang
- Gabriel D. Carroll
- Jingyi Xue
Citations in the corpus
- The Unreasonable Fairness of Maximum Nash Welfare (2016, positive): “…Carnegie Mellon University NISARG SHAH, Carnegie Mellon University JUNXING WANG, Carnegie Mellon University The maximum Nash welfare (MNW) solution — which selects an allocation that maximizes the product of utilities — is known to provide outstanding fairness guarantees when allocating…”