Abstract
We give exponential lower bounds on the Price of Stability (PoS) of weighted congestion games with polynomial cost functions. In particular, for any positive integer $d$ we construct rather simple games with cost functions of degree at most $d$ which have a PoS of at least $\varOmega(\Phi_d)^{d+1}$, where $\Phi_d\sim d/\ln d$ is the unique positive root of the equation $x^{d+1}=(x+1)^d$. This almost closes the huge gap between $\varTheta(d)$ and $\Phi_d^{d+1}$. Our bound extends also to network congestion games. We further show that the PoS remains exponential even for singleton games. More generally, we provide a lower bound of $\varOmega((1+1/\alpha)^d/d)$ on the PoS of $\alpha$-approximate Nash equilibria for singleton games. All our lower bounds hold for mixed and correlated equilibria as well. On the positive side, we give a general upper bound on the PoS of $\alpha$-approximate Nash equilibria, which is sensitive to the range $W$ of the player weights and the approximation parameter $\alpha$. We do this by explicitly constructing a novel approximate potential function, based on Faulhaber's formula, that generalizes Rosenthal's potential in a continuous, analytic way. From the general theorem, we deduce two interesting corollaries. First, we derive the existence of an approximate pure Nash equilibrium with PoS at most $(d+3)/2$; the equilibrium's approximation parameter ranges from $\varTheta(1)$ to $d+1$ in a smooth way with respect to $W$. Second, we show that for unweighted congestion games, the PoS of $\alpha$-approximate Nash equilibria is at most $(d+1)/\alpha$. Read More: https://epubs.siam.org/doi/10.1137/18M1207880
Highlights
In the last 20 years, a central strand of research within Algorithmic Game Theory has focused on understanding and quantifying the inefficiency of equilibria compared to centralized, optimal solutions
We provide lower and upper bounds on the Price of Stability for the class of weighted congestion games with polynomial latencies with nonnegative coefficients
As a matter of fact, this new lower bound comes as a corollary of a more general result that we show in Theorem 5, that extends to approximate equilibria and gives a lower bound of Ω((1 + 1/α)d/d) on the Price of Stability (PoS) of αapproximate equilibria, for any approximation parameter α ∈ [1, d)
Summary
In the last 20 years, a central strand of research within Algorithmic Game Theory has focused on understanding and quantifying the inefficiency of equilibria compared to centralized, optimal solutions. Though, an immediate dichotomy between weighted and unweighted congestion games occurs: the former may not even have pure Nash equilibria [36, 25, 27, 30]; as a matter of fact, it is a strongly NP-hard problem to even determine if that’s the case [20] In such games there does not, in general, exist a potential function [37, 31], which is the main tool for proving equilibrium existence in the unweighted case. The main focus of this work is precisely to deal with this lack of understanding, and to determine the Price of Stability of weighted congestion games What makes this problem challenging is that the only general known technique for showing upper bounds for the Price of Stability is the potential method, which is applicable only to potential games. That these results do not take into account computational complexity considerations; if we insist in polynomial-time algorithms for finding those equilibria, the currently best approximation parameter becomes dO(d) [12, 13, 22]
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