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Nash Dynamics in Constant Player and Bounded Jump Congestion Games

机译:常量播放器和有界跳跃拥塞游戏中的Nash动力学

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摘要

We study the convergence time of Nash dynamics in two classes of congestion games - constant player congestion games and bounded jump congestion games. It was shown by Ackermann and Skopa-lik [2] that even 3-player congestion games are PLS-complete. We design an FPTAS for congestion games with constant number of players. In particular, for any ∈ > 0, we establish a stronger result, namely, any sequence of (1 + ∈)-greedy improvement steps converges to a (1 + ∈)-approximate equilibrium in a number of steps that is polynomial in ∈~(-1) and the size of the input. As the number of strategies of a player can be exponential in the size of the input, our FPTAS result assumes that a (1 + ∈)-greedy improvement step, if it exists, can be computed in polynomial time. This assumption holds in previously studied models of congestion games, including network congestion games [9] and restricted network congestion games [2].rnFor bounded jump games, where jumps in the delay functions of resources are bounded by β, we show that there exists a game with an exponentially long sequence of α-greedy best response steps that does not converge to an α-approximate equilibrium, for all α ≤ β~(o(n/log n)), where n is the number of players and the size of the game is O(n). So in the worst case, Nash dynamics may fail to converge in polynomial time to such an approximate equilibrium. We also prove the same result for bounded jump network congestion games. In contrast, we observe that it is easy to show that a β~(2n)-approximate equilibrium is reached in at most n best response steps.
机译:我们研究了两类拥塞游戏中的Nash动力学的收敛时间-恒定玩家拥塞游戏和界跳拥塞游戏。 Ackermann和Skopa-lik [2]证明,即使是3人游戏,其拥塞游戏也是PLS完整的。我们为玩家人数恒定的拥塞游戏设计了FPTAS。特别是,对于任何∈> 0,我们建立了一个更强的结果,即(1 +∈)贪婪改进步骤的任何序列在多项式中都收敛到(1 +∈)近似平衡,其中∈ 〜(-1)和输入的大小。由于玩家的策略数量可以在输入的大小上成指数增长,因此我们的FPTAS结果假设(1 +∈)贪婪改进步骤(如果存在)可以在多项式时间内计算出来。该假设在先前研究的拥塞博弈模型中保持不变,包括网络拥塞博弈[9]和受限网络拥塞博弈[2]。对于有界跳博弈,其中资源的延迟函数中的跃迁以β为界,我们表明存在对于所有α≤β〜(o(n / log n)),具有n个贪婪最佳响应步长指数级序列且不收敛到α近似均衡的博弈,其中n是参与者数,游戏的大小是O(n)。因此,在最坏的情况下,纳什动力学可能无法在多项式时间内收敛到这种近似平衡。对于边界跳跃网络拥塞游戏,我们也证明了相同的结果。相反,我们观察到很容易证明在最多n个最佳响应步骤中达到了β〜(2n)近似平衡。

著录项

  • 来源
    《Algorithmic game theory》|2009年|P.196-207|共12页
  • 会议地点 Paphos(CY);Paphos(CY)
  • 作者单位

    Department of Computer and Information Science University of Pennsylvania;

    rnDepartment of Computer and Information Science University of Pennsylvania;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机软件;
  • 关键词

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