Convergence Of Best-response Dynamics Research Articles

Topological Bounds on the Price of Anarchy of Clustering Games on Networks

We consider clustering games in which the players are embedded into a network and want to coordinate (or anti-coordinate) their strategy with their neighbors. The goal of a player is to choose a strategy that maximizes her utility given the strategies of her neighbors. Recent studies show that even very basic variants of these games exhibit a large Price of Anarchy: A large inefficiency between the total utility generated in centralized outcomes and equilibrium outcomes in which players selfishly maximize their utility. Our main goal is to understand how structural properties of the network topology impact the inefficiency of these games. We derive topological bounds on the Price of Anarchy for different classes of clustering games. These topological bounds provide a more informative assessment of the inefficiency of these games than the corresponding worst-case Price of Anarchy bounds. More specifically, depending on the type of clustering game, our bounds reveal that the Price of Anarchy depends on the maximum subgraph density or the maximum degree of the graph. Among others, these bounds enable us to derive bounds on the Price of Anarchy for clustering games on Erdős-Rényi random graphs. Depending on the graph density, these bounds stand in stark contrast to the known worst-case Price of Anarchy bounds. Additionally, we characterize the set of distribution rules that guarantee the existence of a pure Nash equilibrium or the convergence of best-response dynamics. These results are of a similar spirit as the work of Gopalakrishnan et al. [ 19 ] and complement work of Anshelevich and Sekar [ 4 ].

Convergence of best-response dynamics in extensive-form games

This paper presents a collection of convergence results on best-response dynamics in extensive-form games. We prove that in all finite generic extensive-form games of perfect information, every solution trajectory to the continuous-time best-response dynamic converges to a Nash equilibrium component. We show the robustness of this convergence in the sense that along any interior approximate best-response trajectory, the evolving state is close to the set of Nash equilibria most of the time. We also prove that in any perfect-information game in which every play contains at most one decision node of each player, any interior approximate best-response trajectory converges to the backward-induction strategy profile. Our final result concerns self-confirming equilibria in perfect-information games. If each player always best responds to her conjecture of the current strategy profile, and she updates her conjecture based only on observed moves, then the dynamic will converge to the set of self-confirming equilibria.

Convergence of best-response dynamics in games with conflicting congestion effects

We study the model of resource allocation games with conflicting congestion effects that was introduced by Feldman and Tamir [9]. In this model, an agent's cost consists of its resource's load (which increases with congestion) and its share in the resource's activation cost (which decreases with congestion). The current work studies the convergence rate of best-response dynamics (BRD) in the case of homogeneous agents. Even within this simple setting, interesting phenomena arise. We show that, in contrast to standard congestion games with identical jobs and resources, the convergence rate of BRD under conflicting congestion effects might be super-linear in the number of jobs. Nevertheless, a specific form of BRD is proposed, which is guaranteed to converge in linear time.

The speed of convergence in congestion games under best-response dynamics

We investigate the speed of convergence of best response dynamics to approximately optimal solutions in congestion games with linear delay functions. In Ackermann et al. [2008] it has been shown that the convergence time of such dynamics to Nash equilibrium may be exponential in the number of players n . Motivated by such a negative result, we focus on the study of the states (not necessarily being equilibria) reached after a limited number of players' selfish moves, and we show that Θ( n log log n ) best responses are necessary and sufficient to achieve states that approximate the optimal solution by a constant factor, under the assumption that every O ( n ) steps each player performs a constant (and nonnull) number of best responses. We show that such result is tight also for the simplest case of singleton congestion games.

On best response dynamics in weighted congestion games with polynomial delays

We investigate the speed of convergence of best response dynamics to approximately optimal solutions in weighted congestion games with polynomial delay functions. Awerbuch et al. (Fast convergence to nearly optimal solutions in potential games. ACM Conference on Electronic Commerce, 2008) showed that the convergence time of such dynamics to Nash equilibrium may be exponential in the number of players n even for unweighted players and linear delay functions. Nevertheless, we show that Θ(n log log W) (where W is the sum of all the players’ weights) best responses are necessary and sufficient to achieve states that approximate the optimal solution by a constant factor, under the assumption that every O(n) steps each player performs a constant (and non-null) number of best responses. For congestion games in which computing a best response is a polynomial time solvable problem, such a dynamics naturally implies a polynomial time distributed algorithm for the problem of computing the social optimum in congestion games, approximating the optimal solution by a constant factor.