We study network coordination problems, as captured by the setting of generalized network design (, 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 's Bayesian ignorance framework (TCS 2012). While the approach of 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.
In this paper, we study online algorithms when the input is not chosen adversarially, but consists of draws from some given probability distribution. While this model has been studied for online problems like paging and k-server, it is not known how to beat the Φ(log n) bound for online Steiner tree if at each time instant, the demand vertex is a uniformly random vertex from the graph. For the online Steiner tree problem, we show that if each demand vertex is an independent draw from some probability distribution π: V → [0, 1], a variant of the natural greedy algorithm achieves Eω[A(ω)] Eω[OPT (ω)] = O(1); moreover, this result can be extended to some other subadditive problems. Both assumptions that the input sequence consists of independent draws from π, and that π is known to the algorithm are both essential; we show (almost) logarithmic lower bounds if either assumption is violated. Moreover, we give preliminary results on extending the Steiner tree results above to the related "expected ratio" measure Eω[ω(ω) OPT (ω)]. Finally, we use these ideas to give an average-case analysis of the Universal TSP problem.