Title:
Group Fairness in Combinatorial Optimization

dc.contributor.author Munagala, Kamesh
dc.contributor.corporatename Georgia Institute of Technology. Algorithms and Randomness Center en_US
dc.contributor.corporatename Duke University. Dept. of Computer Science en_US
dc.date.accessioned 2021-11-18T15:31:42Z
dc.date.available 2021-11-18T15:31:42Z
dc.date.issued 2021-11-08
dc.description Presented in-person and online via Bluejeans Meetings at 11:00 a.m. on November 8, 2021. en_US
dc.description Kamesh Munagala is a Professor and Associate Chair in the Computer Science Department at Duke University. His research is in the general area of theoretical computer science, particularly the areas of Approximation Algorithms, Online Algorithms, and Algorithmic Game Theory. en_US
dc.description Runtime: 58:20 minutes en_US
dc.description.abstract Consider the following classical network design model. There are n clients in a multi-graph with a single sink node. Each edge has a cost to buy, and a length if bought; typically, costlier edges have smaller lengths. There is a budget B on the total cost of edges bought. Given a set of bought edges, the distance of a client to the sink is the shortest path according to the edge lengths. Such a model captures buy-at-bulk network design and facility location as special cases. Rather than pose this as a standard optimization problem, we ask a different question: Suppose a provider is allocating budget B to build this network, how should it do so in a manner that is fair to the clients? We consider a classical model of group fairness termed the core in cooperative game theory: If each client contributes its share B/n amount of budget as tax money, no subset of clients should be able to pool their tax money to deviate and build a different network that simultaneously improves all their distances to the sink. The question is: Does such a solution always exist, or approximately exist? We consider an abstract “committee selection” model from social choice literature that captures not only the above problem, but other combinatorial optimization problems where we need to provision public resources subject to combinatorial constraints, in order to provide utility to clients. For this general model, we show that an approximately fair solution always exists, where the approximation scales down the tax money each client can use for deviation by only a constant factor. Our existence result relies on rounding an interesting fractional relaxation to this problem. In certain cases such as the facility location problem, it also implies a polynomial time algorithm. We also show that similar results when the approximation is on the utility that clients derive by deviating. en_US
dc.format.extent 58:20 minutes
dc.identifier.uri http://hdl.handle.net/1853/65498
dc.language.iso en_US en_US
dc.relation.ispartofseries Algorithms and Randomness Center (ARC) Colloquium
dc.subject Network design en_US
dc.subject Optimization en_US
dc.title Group Fairness in Combinatorial Optimization en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename Algorithms and Randomness Center
local.contributor.corporatename College of Computing
local.relation.ispartofseries ARC Colloquium
relation.isOrgUnitOfPublication b53238c2-abff-4a83-89ff-3e7b4e7cba3d
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication c933e0bc-0cb1-4791-abb4-ed23c5b3be7e
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