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Bayesian Generalized Network Design

Authors Yuval Emek, Shay Kutten, Ron Lavi, Yangguang Shi

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Author Details

Yuval Emek
  • Faculty of Industrial Engineering and Management, Technion, Haifa, Israel
Shay Kutten
  • Faculty of Industrial Engineering and Management, Technion, Haifa, Israel
Ron Lavi
  • Faculty of Industrial Engineering and Management, Technion, Haifa, Israel
Yangguang Shi
  • Faculty of Industrial Engineering and Management, Technion, Haifa, Israel

Funding

  • Emek, Yuval: The work of Yuval Emek was supported in part by an Israeli Science Foundation grant number 1016/17.
  • Kutten, Shay: The work of Shay Kutten was supported in part by a grant from the ministry of science in the program that is joint with JSPS and in part by the BSF.
  • Lavi, Ron: The work of Ron Lavi was partially supported by the ISF-NSFC joint research program (grant No. 2560/17).
  • Shi, Yangguang: The work of Yangguang Shi was partially supported at the Technion by a fellowship of the Israel Council for Higher Education.

Cite AsGet BibTex

Yuval Emek, Shay Kutten, Ron Lavi, and Yangguang Shi. Bayesian Generalized Network Design. In 27th Annual European Symposium on Algorithms (ESA 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 144, pp. 45:1-45:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ESA.2019.45

Abstract

We study network coordination problems, as captured by the setting of generalized network design (Emek et al., STOC 2018), in the face of uncertainty resulting from partial information that the network users hold regarding the actions of their peers. This uncertainty is formalized using Alon et al.’s Bayesian ignorance framework (TCS 2012). While the approach of Alon et al. is purely combinatorial, the current paper takes into account computational considerations: Our main technical contribution is the development of (strongly) polynomial time algorithms for local decision making in the face of Bayesian uncertainty.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
  • Theory of computation → Mathematical optimization
  • Theory of computation → Algorithm design techniques
Keywords
  • approximation algorithms
  • Bayesian competitive ratio
  • Bayesian ignorance
  • generalized network design
  • diseconomies of scale
  • energy consumption
  • smoothness
  • best response dynamics

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