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arXiv physics/0512045physics.soc-phcond-mat.stat-mechcs.GTcs.MA
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语言游戏中的拓扑诱导粗化

Topology Induced Coarsening in Language Games

A. Baronchelli, L. Dall'Asta, A. Barrat, V. Loreto

摘要:

我们调查如何非常大的人口能够达到全球共识,当地的“微观”的互动规则,在最近推出的一类模型的符号动力学的框架,所谓的博弈。我们特别是相互作用的代理商嵌入在一个低维的晶格相对于平均场的情况下的收敛机制进行比较。我们强调,在低维度的共识是通过粗化过程,需要较少的认知努力的代理,相对于平均场的情况下,但需要更长的时间才能完成。在1-D的动态的边界映射到截断马尔可夫过程,我们分析计算的扩散系数。更一般地,我们表明,收敛过程需要每个代理的记忆缩放为N,并持续时间N^{1+2/d}在维度d<5(d=4是上临界维度),而在平均场的记忆和时间尺度为N^{3/2},对于N个代理的人口。我们提出了支持这一观点的分析和数值证据。

英文摘要:

We investigate how very large populations are able to reach a global consensus, out of local "microscopic" interaction rules, in the framework of a recently introduced class of models of semiotic dynamics, the so-called Naming Game. We compare in particular the convergence mechanism for interacting agents embedded in a low-dimensional lattice with respect to the mean-field case. We highlight that in low-dimensions consensus is reached through a coarsening process which requires less cognitive effort of the agents, with respect to the mean-field case, but takes longer to complete. In 1-d the dynamics of the boundaries is mapped onto a truncated Markov process from which we analytically computed the diffusion coefficient. More generally we show that the convergence process requires a memory per agent scaling as N and lasts a time N^{1+2/d} in dimension d<5 (d=4 being the upper critical dimension), while in mean-field both memory and time scale as N^{3/2}, for a population of N agents. We present analytical and numerical evidences supporting this picture.

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