Abstract Selfish routing is a classical mathematical model of how self-interested users might route traffic through a congested network. The outcome of selfish routing is generally inefficient, in that it fails to optimize natural objective functions. The price of anarchy is a quantitative measure of this inefficiency. We survey recent work that analyzes the price of anarchy of selfish routing. We also describe related results on bounding the worst-possible severity of a phenomenon called Braess's Paradox, and on three techniques for reducing the price of anarchy of selfish routing. This survey concentrates on the contributions of the author's PhD thesis, but also discusses several more recent results in the area.

In this paper, we study the price of anarchy of traffic routing, under the assumption that users are partially altruistic or spiteful. We model such behavior by positing that the “cost ” perceived by a user is a linear combination of the actual latency of the route chosen (selfish component), and the increase in latency the user causes for others (altruistic component). We show that if all users have a coefficient of at least β> 0 for the altruistic component, then the price of anarchy is bounded by 1/β, for all network topologies, arbitrary commodities, and arbitrary semi-convex latency functions. We extend this result to give more precise bounds on the price of anarchy for specific classes of latency functions, even for β < 0 modeling spiteful behavior. In particular, we show that if all latency functions are linear, the price of anarchy is bounded by 4/(3 + 2β − β 2). We next study non-uniform altruism distributions, where different users may have different coefficients β. We prove that all such games, even with infinitely many types of players, have a Nash Equilibrium. We show that if the average of the coefficients for the altruistic components of all users is ¯ β, then the price of anarchy is bounded by 1 / ¯ β, for single commodity parallel link networks, and arbitrary convex latency functions. In particular, this result generalizes, albeit non-constructively, the Stackelberg routing results of Roughgarden and of Swamy. More generally, we bound the price of anarchy based on the class of allowable latency functions, and as a corollary obtain tighter bounds for Stackelberg routing than a recent result of Swamy.

Noncooperative network routing games are a natural model of users trying to selfishly route flow through a network in order to minimize their own delays. It is well known that the solution resulting from this selfish routing (called the Nash equilibrium) can have social cost strictly higher than the cost of the optimum solution. One way to improve the quality of the resulting solution is to centrally control a fraction of the flow. A natural problem for the network administrator then is to route the centrally controlled flow in such a way that the overall cost of the solution is minimized after the remaining fraction has routed itself selfishly. This problem falls in the class of well-studied Stackelberg routing games. We consider the scenario where the network administrator wants the final solution to be (strictly) better than the Nash equilibrium. In other words, she wants to control enough flow such that the cost of the resulting solution is strictly less than the cost of the Nash equilibrium. We call the minimum fraction of users that must be centrally routed to improve the quality of the resulting solution the Stackelberg threshold. We give a closed form expression for the Stackelberg threshold for parallel links networks with linear latency functions. The expression is in terms of Nash equilibrium flows and optimum flows. It turns out that the Stackelberg threshold is the minimum of Nash flows on links which have more optimum flow than Nash flow. Using our approach to characterize the Stackelberg thresholds, we are able to give a simpler proof of an earlier result which finds the minimum fraction required to be centrally controlled to induce an optimum solution.

the date of receipt and acceptance should be inserted later Abstract We present efficient algorithms for computing approximate Wardrop equilibria in a distributed and concurrent fashion. Our algorithms are exexuted by a finite number of agents each of which controls the flow of one commodity striving to balance the induced latency over all utilised paths. The set of allowed paths is represented by a DAG. Our algorithms are based on previous work on policies for infinite populations of agents. These policies achieve a convergence time which is independent of the underlying network and depends mildly on the latency functions. These policies can neither be applied to a finite set of agents nor can they be simulated directly due to the exponential number of paths. Our algorithms circumvent these problems by computing a randomised path decomposition in every communication round. Based on this decomposition, flow is shifted from overloaded to underloaded paths. This way, our algorithm can handle exponentially large path collections in polynomial time. Our algorithms are stateless, and the number of communication rounds depends polynomially on the approximation quality and is independent of the topology and size of the network.

Abstract. We investigate the existence of optimal tolls for atomic symmetric network congestion games with unsplittable traffic and arbitrary non-negative and non-decreasing latency functions. We focus on pure Nash equilibria and a natural toll mechanism, which we call cost-balancing tolls. A set of cost-balancing tolls turns every path with positive traffic on its edges into a minimum cost path. Hence any given configuration is induced as a pure Nash equilibrium of the modified game with the corresponding cost-balancing tolls. We show how to compute in linear time a set of cost-balancing tolls for the optimal solution such that the total amount of tolls paid by any player in any pure Nash equilibrium of the modified game does not exceed the latency on the maximum latency path in the optimal solution. Our main result is that for congestion games on series-parallel networks with increasing latencies, the optimal solution is induced as the unique pure Nash equilibrium of the game with the corresponding cost-balancing tolls. To the best of our knowledge, only linear congestion games on parallel links were known to admit optimal tolls prior to this work. To demonstrate the difficulty of computing a better set of optimal tolls, we show that even for 2-player linear congestion games on series-parallel networks, it is NP-hard to decide whether the optimal solution is the unique pure Nash equilibrium or there is another equilibrium of total cost at least 6/5 times the optimal cost. 1

Abstract. We study taxes in the well-known game theoretic traffic model due to Wardrop. Given a network and a subset of edges, on which we can impose taxes, the problem is to find taxes inducing an equilibrium flow of minimal networkwide latency cost. If all edges are taxable, then marginal cost pricing is known to induce the socially optimal flow for arbitrary multi-commodity networks. In contrast, if only a strict subset of edges is taxable, we show NP-hardness of finding optimal taxes for general networks with linear latency functions and two commodities. On the positive side, for single-commodity networks with parallel links and linear latency function, we provide a polynomial time algorithm for finding optimal taxes. 1

Abstract. We study economic means to improve network performance in the well-known game theoretic traffic model due to Wardrop. We introduce two sorts of spam flow- auxiliary and adversarial flow- that have no intrinsic value. Auxiliary/adversarial flows are a separate commodity with the sole objective to minimize/maximize the latency at the induced Wardrop equilibrium of the selfish flow. By this means a separate access to the edges is not necessary and the latency of the regulating flow does not distort the arising latency cost. We present networks where auxiliary flow is able to improve the network performance. However, we show that the optimal auxiliary flow is NP-hard to compute and not approximable within a factor of less then 4 3. The minimal amount of auxiliary flow needed to induce the best possible equilibrium is even hard to approximate by any subexponential factor. These hardness results are complemented by proving NP-hardness for the optimal adversarial flow. All hardness results hold even for single-commodity networks. 1

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