Tradeoffs in worst-case equilibria

Awerbuch, Baruch; Azar, Yossi; Richter, Yossi; Tsur, Dekel

doi:10.1016/j.tcs.2006.05.010

Cited by 55 publications

(27 citation statements)

References 14 publications

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“…The same bound was shown independently by Gairing et al [28] and Awerbuch et al [9] for restricted assignment machines. We discuss the lower bound for both cases; for the case B||C max we show that the lower bound is true for strong equilibria as well.…”

Section: Theoremsupporting

confidence: 75%

“…In section 3 we discuss several results regarding the most studied coordination mechanisms from the literature. These include the scheduling model (preemptive mechanism) introduced in [36] and further studied in [23,20,28,9,24,46] (subsection 3.1), non-preemptive ordering mechanisms studied in [5,19,34,22] (subsection 3.2) and a brief account of mechanisms for the most general scheduling environment of unrelated machines (subsection 3.5) [34,10,14]. In section 4 we revisit nonpreemptive ordering mechanisms in the context of truthfulness [19,18], where agents may misreport their processing loads to the mechanism so as to minimize their completion time in the outcome of the game.…”

Section: Introductionmentioning

confidence: 99%

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Strategic Scheduling Games: Equilibria and Efficiency

Gourvès

Monnot

Telelis

2011

Just-in-Time Systems

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Section: Theoremsupporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Strategic Scheduling Games: Equilibria and Efficiency

Gourvès

Monnot

Telelis

2011

Just-in-Time Systems

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“…In [9] Czumaj and Vöcking were able to prove tight bounds for this problem. Recently, Awerbuch et al [5] and Gairing et al [11] have extended these results to a model where each agent can use only a subset of the (parallel) links, namely, a permissible set, and Fotakis et al [10] have showed that the same bound holds in layered networks as well.…”

Section: Related Workmentioning

confidence: 96%

Selfish Load Balancing and Atomic Congestion Games

2006

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Abstract. We revisit a classical load balancing problem in the modern context of decentralized systems and self-interested clients. In particular, there is a set of clients, each of whom must choose a server from a permissible set. Each client has a unit-length job and selfishly wants to minimize its own latency (job completion time). A server's latency is inversely proportional to its speed, but it grows linearly with (or, more generally, as the pth power of) the number of clients matched to it. This interaction is naturally modeled as an atomic congestion game, which we call selfish load balancing. We analyze the Nash equilibria of this game and prove nearly tight bounds on the price of anarchy (worst-case ratio between a Nash solution and the social optimum). In particular, for linear latency functions, we show that if the server speeds are relatively bounded and the number of clients is large compared with the number of servers, then every Nash assignment approaches social optimum. Without any assumptions on the number of clients, servers, and server speeds, the price of anarchy is at most 2.5. If all servers have the same speed, then the price of anarchy further improves to 1 + 2/ √ 3 ≈ 2.15. We also exhibit a lower bound of 2.01. Our proof techniques can also be adapted for the coordinated load balancing problem under L 2 norm, where it slightly improves the best previously known upper bound on the competitive ratio of a simple greedy scheme.

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“…The KP model was recently extended to restricted strategy sets [9,35], where the strategy set of each player is a subset of the links. Furthermore, the KP model was extended to general latency functions and studied with respect to different definitions of social cost [36,37,63].…”

Section: Price Of Anarchymentioning

confidence: 99%

“…In a different piece of work, the same problem has been viewed as a congestion game where latency functions are player-specific [41], or a congestion game under the restriction that the link for each user must be chosen from a certain set of allowed links for the user [9,26].…”

Section: Selfish Routing With Incomplete Informationmentioning

confidence: 99%

Algorithmic Game Theory and Applications

Nayak

2007

Handbook of Applied Algorithms

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Most of the existing and foreseen complex networks, such as the Internet, are operated and built by thousands of large and small entities (autonomous agents), which collaborate to process and deliver end-to-end flows originating from and terminating at any of them. The distributed nature of the Internet implies a lack of coordination among its users. Instead, each user attempts to obtain maximum performance according to his own parameters and objectives.Methods from game theory and mathematical economics have been proven to be a powerful modeling tool, which can be applied to understand, control, and efficiently design such dynamic, complex networks. Game theory provides a good starting point for computer scientists in their endeavor to understand selfish rational behavior in complex networks with many agents (players). Such scenarios are readily modeled using techniques from game theory, where players with potentially conflicting goals participate in a common setting with well-prescribed interactions.Nash equilibrium [73,74] distinguishes itself as the predominant concept of rationality in noncooperative settings. So, game theory and its various concepts of equilibria provide a rich framework for modeling the behavior of selfish agents in these kinds of distributed or networked environments; they offer mechanisms to achieve efficient and desirable global outcomes in spite of the selfish behavior.Mechanism design, a subfield of game theory, asks how one can design systems so that agents' selfish behavior results to desired systemwide goals. Algorithmic mechanism design additionally considers computational tractability to the set of concerns of mechanism design. Work on algorithmic mechanism design has focused on the complexity of centralized implementations of game-theoretic mechanisms for distributed optimization problems. Moreover, in such huge and heterogeneous networks, each agent does not have access to (and may not process) complete information. 286 ALGORITHMIC GAME THEORY AND APPLICATIONSThe notion of bounded rationality for agents and the design of corresponding incomplete-information distributed algorithms have been successfully utilized to capture the aspect of lack of global knowledge in information networks.In this chapter, we review some of the most thrilling algorithmic problems and solutions, and corresponding advances, achieved on the account of game theory. The areas addressed are the following.Congestion games A central problem arising in the management of large-scale communication networks is that of routing traffic through the network. However, due to the large size of these networks, it is often impossible to employ a centralized traffic management. A natural assumption to make in the absence of central regulation is that network users behave selfishly and aim at optimizing their own individual welfare. One way to address this problem is to model this scenario as a noncooperative multiplayer game and formalize it using congestion game. Congestion games (either unweighted or weighted) offer ...

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Tradeoffs in worst-case equilibria

Cited by 55 publications

References 14 publications

Strategic Scheduling Games: Equilibria and Efficiency

Strategic Scheduling Games: Equilibria and Efficiency

Selfish Load Balancing and Atomic Congestion Games

Algorithmic Game Theory and Applications

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