93 research outputs found
Resource Buying Games
In resource buying games a set of players jointly buys a subset of a finite
resource set E (e.g., machines, edges, or nodes in a digraph). The cost of a
resource e depends on the number (or load) of players using e, and has to be
paid completely by the players before it becomes available. Each player i needs
at least one set of a predefined family S_i in 2^E to be available. Thus,
resource buying games can be seen as a variant of congestion games in which the
load-dependent costs of the resources can be shared arbitrarily among the
players. A strategy of player i in resource buying games is a tuple consisting
of one of i's desired configurations S_i together with a payment vector p_i in
R^E_+ indicating how much i is willing to contribute towards the purchase of
the chosen resources. In this paper, we study the existence and computational
complexity of pure Nash equilibria (PNE, for short) of resource buying games.
In contrast to classical congestion games for which equilibria are guaranteed
to exist, the existence of equilibria in resource buying games strongly depends
on the underlying structure of the S_i's and the behavior of the cost
functions. We show that for marginally non-increasing cost functions, matroids
are exactly the right structure to consider, and that resource buying games
with marginally non-decreasing cost functions always admit a PNE
Matroids are Immune to Braess Paradox
The famous Braess paradox describes the following phenomenon: It might happen
that the improvement of resources, like building a new street within a
congested network, may in fact lead to larger costs for the players in an
equilibrium. In this paper we consider general nonatomic congestion games and
give a characterization of the maximal combinatorial property of strategy
spaces for which Braess paradox does not occur. In a nutshell, bases of
matroids are exactly this maximal structure. We prove our characterization by
two novel sensitivity results for convex separable optimization problems over
polymatroid base polyhedra which may be of independent interest.Comment: 21 page
Path deviations outperform approximate stability in heterogeneous congestion games
We consider non-atomic network congestion games with heterogeneous players
where the latencies of the paths are subject to some bounded deviations. This
model encompasses several well-studied extensions of the classical Wardrop
model which incorporate, for example, risk-aversion, altruism or travel time
delays. Our main goal is to analyze the worst-case deterioration in social cost
of a perturbed Nash flow (i.e., for the perturbed latencies) with respect to an
original Nash flow. We show that for homogeneous players perturbed Nash flows
coincide with approximate Nash flows and derive tight bounds on their
inefficiency. In contrast, we show that for heterogeneous populations this
equivalence does not hold. We derive tight bounds on the inefficiency of both
perturbed and approximate Nash flows for arbitrary player sensitivity
distributions. Intuitively, our results suggest that the negative impact of
path deviations (e.g., caused by risk-averse behavior or latency perturbations)
is less severe than approximate stability (e.g., caused by limited
responsiveness or bounded rationality). We also obtain a tight bound on the
inefficiency of perturbed Nash flows for matroid congestion games and
homogeneous populations if the path deviations can be decomposed into edge
deviations. In particular, this provides a tight bound on the Price of
Risk-Aversion for matroid congestion games
Sampling from the {G}ibbs Distribution in Congestion Games
Logit dynamics is a form of randomized game dynamics where players have a bias towards strategic deviations that give a higher improvement in cost. It is used extensively in practice. In congestion (or potential) games, the dynamics converges to the so-called Gibbs distribution over the set of all strategy profiles, when interpreted as a Markov chain. In general, logit dynamics might converge slowly to the Gibbs distribution, but beyond that, not much is known about their algorithmic aspects, nor that of the Gibbs distribution. In this work, we are interested in the following two questions for congestion games: i) Is there an efficient algorithm for sampling from the Gibbs distribution? ii) If yes, do there also exist natural randomized dynamics that converges quickly to the Gibbs distribution? We first study these questions in extension parallel congestion games, a well-studied special case of symmetric network congestion games. As our main result, we show that there is a simple variation on the logit dynamics (in which we in addition are allowed to randomly interchange the strategies of two players) that converges quickly to the Gibbs distribution in such games. This answers both questions above affirmatively. We also address the first question for the class of so-called capacitated -uniform congestion games. To prove our results, we rely on the recent breakthrough work of Anari, Liu, Oveis-Gharan and Vinzant (2019) concerning the approximate sampling of the base of a matroid according to strongly log-concave probability distribution
Sharing Non-Anonymous Costs of Multiple Resources Optimally
In cost sharing games, the existence and efficiency of pure Nash equilibria
fundamentally depends on the method that is used to share the resources' costs.
We consider a general class of resource allocation problems in which a set of
resources is used by a heterogeneous set of selfish users. The cost of a
resource is a (non-decreasing) function of the set of its users. Under the
assumption that the costs of the resources are shared by uniform cost sharing
protocols, i.e., protocols that use only local information of the resource's
cost structure and its users to determine the cost shares, we exactly quantify
the inefficiency of the resulting pure Nash equilibria. Specifically, we show
tight bounds on prices of stability and anarchy for games with only submodular
and only supermodular cost functions, respectively, and an asymptotically tight
bound for games with arbitrary set-functions. While all our upper bounds are
attained for the well-known Shapley cost sharing protocol, our lower bounds
hold for arbitrary uniform cost sharing protocols and are even valid for games
with anonymous costs, i.e., games in which the cost of each resource only
depends on the cardinality of the set of its users
On the Impact of Singleton Strategies in Congestion Games
To what extent does the structure of the players\u27 strategy space influence the efficiency of decentralized solutions in congestion games? In this work, we investigate whether better performance is possible when restricting to load balancing games in which players can only choose among single resources. We consider three different solutions concepts, namely, approximate pure Nash equilibria, approximate one-round walks generated by selfish players aiming at minimizing their personal cost and approximate one-round walks generated by cooperative players aiming at minimizing the marginal increase in the sum of the players\u27 personal costs. The last two concepts can also be interpreted as solutions of simple greedy online algorithms for the related resource selection problem. Under fairly general latency functions on the resources, we show that, for all three types of solutions, better bounds cannot be achieved if players are either weighted or asymmetric. On the positive side, we prove that, under mild assumptions on the latency functions, improvements on the performance of approximate pure Nash equilibria are possible for load balancing games with weighted and symmetric players in the case of identical resources. We also design lower bounds on the performance of one-round walks in load balancing games with unweighted players and identical resources (in this case, solutions generated by selfish and cooperative players coincide)
Uniform Mixed Equilibria in Network Congestion Games with Link Failures
Motivated by possible applications in fault-tolerant routing, we introduce the notion of uniform mixed equilibria in network congestion games with adversarial link failures, where players need to route traffic from a source to a destination node. Given an integer rho >= 1, a rho-uniform mixed strategy is a mixed strategy in which a player plays exactly rho edge disjoint paths with uniform probabilities, so that a rho-uniform mixed equilibrium is a tuple of rho-uniform mixed strategies, one for each player, in which no player can lower her cost by deviating to another rho-uniform mixed strategy. For games with weighted players and affine latency functions, we show existence of rho-uniform mixed equilibria and provide a tight characterization of their price of anarchy. For games with unweighted players, instead, we extend the existential guarantee to any class of latency functions and, restricted to games with affine latencies, we derive a tight characterization of both the prices of anarchy and stability
Routing Games with Progressive Filling
Max-min fairness (MMF) is a widely known approach to a fair allocation of
bandwidth to each of the users in a network. This allocation can be computed by
uniformly raising the bandwidths of all users without violating capacity
constraints. We consider an extension of these allocations by raising the
bandwidth with arbitrary and not necessarily uniform time-depending velocities
(allocation rates). These allocations are used in a game-theoretic context for
routing choices, which we formalize in progressive filling games (PFGs).
We present a variety of results for equilibria in PFGs. We show that these
games possess pure Nash and strong equilibria. While computation in general is
NP-hard, there are polynomial-time algorithms for prominent classes of
Max-Min-Fair Games (MMFG), including the case when all users have the same
source-destination pair. We characterize prices of anarchy and stability for
pure Nash and strong equilibria in PFGs and MMFGs when players have different
or the same source-destination pairs. In addition, we show that when a designer
can adjust allocation rates, it is possible to design games with optimal strong
equilibria. Some initial results on polynomial-time algorithms in this
direction are also derived
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