具有局部协调和全局拥塞效应的网络游戏动力学

Gianluca Brero, G. Como, F. Fagnani
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引用次数: 0

摘要

社交网络上的一些战略互动同时表现出消极和积极的外部性。例如,参与一个资源有限的社交媒体网站,你的朋友越多越有吸引力,而大量的参与者可能会减慢网站的速度(因为拥塞效应),从而降低它的吸引力。同样地,虽然经常有动机与你交往最频繁的朋友和亲戚选择同一家电话公司,但市场份额集中在一家公司手中通常会导致成本上升,因为缺乏竞争。在这项工作中,我们研究了网络游戏中的进化动力学,其中每个玩家的收益既受网络中邻居的行为影响,也受网络中所有玩家行为的总和影响。特别是,我们考虑的情况是,选择相同行动的邻居数量增加(局部协调效应),选择相同行动的玩家总数减少(全局拥堵效应)。我们研究了两个完全图的并集网络中的噪声最优响应动力学,并证明了不变概率分布的渐近行为是关于一个度量局部协调相对于全局拥塞效应的相对强度的参数的两个相变。通过仿真研究了强社区结构随机网络的扩展问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamics in network games with local coordination and global congestion effects
Several strategic interactions over social networks display both negative and positive externalities at the same time. E.g., participation to a social media website with limited resources is more appealing the more of your friends participate, while a large total number of participants may slow down the website (because of congestion effects) thus making it less appealing. Similarly, while there are often incentives to choose the same telephone company as the friends and relatives with whom you interact the most frequently, concentration of the market share in the hands of a single firm typically leads to higher costs because of the lack of competition. In this work, we study evolutionary dynamics in network games where the payoff of each player is influenced both by the actions of her neighbors in the network, and by the aggregate of the actions of all the players in the network. In particular, we consider cases where the payoff increases in the number of neighbors who choose the same action (local coordination effect) and decreases in the total number of players choosing the same action (global congestion effect). We study noisy best-response dynamics in networks which are the union of two complete graphs, and prove that the asymptotic behavior of the invariant probability distribution is characterized by two phase transitions with respect to a parameter measuring the relative strength of the local coordination with respect to the global congestion effects. Extensions to random networks with strong community structure are studied through simulations.
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