# Bayesian Generalized Network Design

## TL;DR

This paper addresses Bayesian network design problems under uncertainty, developing polynomial-time algorithms for local decision making, advancing the computational understanding of network coordination with partial information.

## Contribution

It introduces polynomial-time algorithms for local decision making in Bayesian generalized network design, integrating computational aspects into the combinatorial framework.

## Key findings

- Developed strongly polynomial algorithms for local decisions
- Enhanced understanding of network coordination under Bayesian uncertainty
- Bridged combinatorial and computational approaches in network design

## 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.