Stackelberg Strategies and Collusion in Network Games with Splittable Flow

T. Harks*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


We study the impact of collusion in network games with splittable flow and focus on the well established price of anarchy as a measure of this impact. We first investigate symmetric load balancing games and show that the price of anarchy is at most m, where m denotes the number of coalitions. For general networks, we present an instance showing that the price of anarchy is unbounded, even in the case of two coalitions. If latencies are restricted to polynomials with nonnegative coefficients and bounded degree, we prove upper bounds on the price of anarchy for general networks, which improve upon the current best ones except for affine light of the negative results even for two coalitions, we analyze the effectiveness of stackelberg strategies as a means to improve the quality of nash equilibria. In this setting, an a fraction of the entire demand is first routed centrally by a stackelberg leader according to a predefined stackelberg strategy and the remaining demand is then routed selfishly by the coalitions (followers).for a single coalitional follower and parallel arcs, we develop an efficiently computable stackelberg strategy that reduces the price of anarchy to one. For general networks and a single coalitional follower, we show that a simple strategy, called scale, reduces the price of anarchy to 1+a. Finally, we investigate scale for multiple coalitional followers, general networks, and affine latencies. We present the first known upper bound on the price of anarchy in this case. Our bound smoothly varies between 1.5 for a=0 and full efficiency for a=1.
Original languageEnglish
Pages (from-to)781-802
Number of pages22
JournalTheory of Computing Systems
Issue number4
Publication statusPublished - 1 Jan 2011

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