text: We consider the well-studied game-theoretic version of machine scheduling in which jobs correspond to self-interested users and machines correspond to resources. Here each user chooses a machine trying to minimize her own cost, and such selfish behavior typically results in some equilibrium which is not globally optimal: An equilibrium is an allocation where no user can reduce her own cost by moving to another machine, which in general need not minimize the makespan, i.e., the maximum load over the machines. We provide tight bounds on two well-studied notions in algorithmic game theory, namely, the price of anarchy and the strong price of anarchy on machine scheduling setting which lies in between the related and the unrelated machine case. Both notions study the social cost (makespan) of the worst equilibrium compared to the optimum, with the strong price of anarchy restricting to a stronger form of equilibria. Our results extend a prior study comparing the price of anarchy to the strong price of anarchy for two related machines (Epstein, Acta Informatica 2010), thus providing further insights on the relation between these concepts. Our exact bounds give a qualitative and quantitative comparison between the two models. The bounds also show that the setting is indeed easier than the two unrelated machines: In the latter, the strong price of anarchy is @math , while in ours it is strictly smaller.
  @cite_18: We study the outcome of natural learning algorithms in atomic congestion games. Atomic congestion games have a wide variety of equilibria often with vastly differing social costs. We show that in almost all such games, the well- known multiplicative-weights learning algorithm results in convergence to pure equilibria. Our results show that nat- ural learning behavior can avoid bad outcomes predicted by the price of anarchy in atomic congestion games such as the load-balancing game introduced by Koutsoupias and Pa- padimitriou, which has super-constant price of anarchy and has correlated equilibria that are exponentially worse than any mixed Nash equilibrium. Our results identify a set of mixed Nash equilibria that we call weakly stable equilibria. Our notion of weakly stable is defined game-theoretically, but we show that this property holds whenever a stability criterion from the theory of dy- namical systems is satisfied. This allows us to show that in every congestion game, the distribution of play converges to the set of weakly stable equilibria. Pure Nash equilibria are weakly stable, and we show using techniques from algebraic geometry that the converse is true with probability 1 when congestion costs are selected at random independently on each edge (from any monotonically parametrized distribu- tion). We further extend our results to show that players can use algorithms with different (sufficiently small) learn- ing rates, i.e. they can trade off convergence speed and long term average regret differently.
  @cite_14: A strong equilibrium is a pure Nash equilibrium which is resilient to deviations by coalitions. We define the strong price of anarchy (SPoA) to be the ratio of the worst strong equilibrium to the social optimum. Differently from the Price of Anarchy (defined as the ratio of the worst Nash Equilibrium to the social optimum), it quantifies the loss incurred from the lack of a central designer in settings that allow for coordination. We study the SPoA in two settings, namely job scheduling and network creation. In the job scheduling game we show that for unrelated machines the SPoA can be bounded as a function of the number of machines and the size of the coalition. For the network creation game we show that the SPoA is at most 2. In both cases we show that a strong equilibrium always exists, except for a well defined subset of network creation games.
  @cite_22: We consider the classical machine scheduling, where n jobs need to be scheduled on m machines, with the goal of minimizing the makespan, i.e., the maximum load of any machine in the schedule. We study inefficiency of schedules that are obtained when jobs arrive sequentially one by one, and choose themselves the machine on which they will be scheduled. Every job is only interested to be on a machine with a small load (and does not care about the loads of other machines). We measure the inefficiency of a schedule as the ratio of the makespan obtained in the worst-case equilibrium schedule, and of the optimum makespan. This ratio is known as the sequential price of anarchy. We also introduce alternative inefficiency measures, which allow for a favorable choice of the order in which the jobs make their decisions. We first disprove the conjecture of Hassin and Yovel (OR Letters, 2015) claiming that for unrelated machines, i.e., for the setting where every job can have a different processing time on every machine, the sequential price of anarchy for m = 2 machines is at most 3. We provide an answer for the setting with m = 2 and show that the sequential price of anarchy grows at least linearly with the number of players. Furthermore, we show that for a certain order of the jobs, the resulting makespan is at most linearly larger than the optimum makespan. Furthermore, we show that if an authority can change the order of the jobs adaptively to the decisions made by the jobs so far (but cannot influence the decisions of the jobs), then there exists an adaptive ordering in which the jobs end up in an optimum schedule. To the end we consider identical machines, i.e., the setting where every job has the same processing time on every machine, and provide matching lower bound examples to the existing upper bounds on the sequential price of anarchy.
  @cite_7: In situations without central coordination, the price of anarchy relates the quality of any Nash equilibrium to the quality of a global optimum. Instead of assuming that all players choose their actions simultaneously, we consider games where players choose their actions sequentially. The sequential price of anarchy, recently introduced by Paes Leme, Syrgkanis, and Tardos, relates the quality of any subgame perfect equilibrium to the quality of a global optimum. The effect of sequential decision making on the quality of equilibria, depends on the specific game under consideration. We analyze the sequential price of anarchy for atomic congestion games with affine cost functions. We derive several lower and upper bounds, showing that sequential decisions mitigate the worst case outcomes known for the classical price of anarchy. Next to tight bounds on the sequential price of anarchy, a methodological contribution of our work is, among other things, a “factor revealing” linear programming approach we use to solve the case of three players.
  @cite_21: 
  @cite_3: 
  @cite_19: We consider a job scheduling game with two uniformly related parallel machines (or links). Jobs are atomic players, and the delay of a job is the completion time of the machine running it. The private goal of each job is to minimize its own delay and the social goal is to minimize the maximum delay of any job, that is, to minimize the makespan. We consider the well known price of anarchy as well as the strong price of anarchy, and show that for a wide range of speed ratios these two measures are very different whereas for other speed ratios these two measures give the exact same bound. We extend all our results for models of restricted assignment, where a machine may have an initial load resulting from jobs that can only be assigned to this machine, and show tight results for all variants.
  @cite_12: 
  @cite_11: 
summary: The bad Nash equilibrium in Example appears in several works @cite_12 @cite_21 @cite_11 @cite_22 to show that even for two machines the price of anarchy is unbounded, thus suggesting that the notion should be refined. Among these, the , which considers NE, is studied in @cite_12 @cite_14 @cite_19 . The , which considers equilibria arising in extensive form games, is studied in @cite_21 @cite_3 @cite_7 @cite_22 . In @cite_11 the authors investigate equilibria and the resulting , while @cite_18 focuses on the equilibria produced by the . A further distinction is between (randomized) and (deterministic) equilibria: in the former, players choose a probability distribution over the strategies and regard their expected cost, in the latter they choose deterministically one strategy. In this work we focus on pure equilibria and in the remaining of this section we write @math to denote the bounds on the price of anarchy for mixed equilibria.