In this note we consider the following problem to study the effect of malicious players on the social optimum in load balancing games: Consider two players SOC and MAL controlling (1-f) and f fraction of the flow in a load balancing game. SOC tries to minimize the total cost faced by her players while MAL tries to maximize the same. If the latencies are linear, we show that this 2-player zero-sum game has a pure strategy Nash equilibrium. Moreover, we show that one of the optimal strategies for MAL is to play selfishly: let the f fraction of the flow be sent as when the flow was controlled by infinitesimal players playing selfishly and reaching a Nash equilibrium. This shows that a malicious player cannot cause more harm in this game than a set of selfish agents. We also introduce the notion of Cost of Malice - the ratio of the cost faced by SOC at equilibrium to (1-f)OPT, where OPT is the social optimum minimizing the cost of all the players. In linear load balancing games we bound the cost of malice by (1+f 2).
A natural generalization of the selfish routing setting arises when some of the users obey a central coordinating authority, while the rest act selfishly. Such behavior can be modeled by dividing the users into an α fraction that are routed according to the central coordinator’s routing strategy (Stackelberg strategy), and the remaining 1−α that determine their strategy selfishly, given the routing of the coordinated users. One seeks to quantify the resulting price of anarchy, i.e., the ratio of the cost of the worst traffic equilibrium to the system optimum, as a function of α. It is well-known that for α=0 and linear latency functions the price of anarchy is at most 4 3 (J. ACM 49, 236–259, 2002). If α tends to 1, the price of anarchy should also tend to 1 for any reasonable algorithm used by the coordinator. We analyze two such algorithms for Stackelberg routing, LLF and SCALE. For general topology networks, multicommodity users, and linear latency functions, we show a price of anarchy bound for SCALE which decreases from 4 3 to 1 as α increases from 0 to 1, and depends only on α. Up to this work, such a tradeoff was known only for the case of two nodes connected with parallel links (SIAM J. Comput. 33, 332–350, 2004), while for general networks it was not clear whether such a result could be achieved, even in the single-commodity case. We show a weaker bound for LLF and also some extensions to general latency functions. The existence of a central coordinator is a rather strong requirement for a network. We show that we can do away with such a coordinator, as long as we are allowed to impose taxes (tolls) on the edges in order to steer the selfish users towards an improved system cost. As long as there is at least a fraction α of users that pay their taxes, we show the existence of taxes that lead to the simulation of SCALE by the tax-payers. The extension of the results mentioned above quantifies the improvement on the system cost as the number of tax-evaders decreases. Noncooperative network routing games are a natural model of userstrying 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 selfishl. 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. We study the problem of optimizing the performance of a system shared by selfish, noncooperative users. We consider the concrete setting of scheduling jobs on a set of shared machines with load-dependent latency functions specifying the length of time necessary to complete a job; we measure system performance by the total latency of the system. Assigning jobs according to the selfish interests of individual users (who wish to minimize only the latency that their own jobs experience) typically results in suboptimal system performance. However, in many systems of this type there is a mixture of “selfishly controlled” and “centrally controlled” jobs; as the assignment of centrally controlled jobs will influence the subsequent actions by selfish users, we aspire to contain the degradation in system performance due to selfish behavior by scheduling the centrally controlled jobs in the best possible way. We formulate this goal as an optimization problem via Stackelberg games , games in which one player acts a leader (here, the centralized authority interested in optimizing system performance) and the rest as followers (the selfish users). The problem is then to compute a strategy for the leader (a em Stackelberg strategy ) that induces the followers to react in a way that (at least approximately) minimizes the total latency in the system. In this paper, we prove that it is NP-hard to compute the optimal Stackelberg strategy and present simple strategies with provable performance guarantees. More precisely, we give a simple algorithm that computes a strategy inducing a job assignment with total latency no more than a constant times that of the optimal assignment of all of the jobs; in the absence of centrally controlled jobs and a Stackelberg strategy, no result of this type is possible. We also prove stronger performance guarantees in the 