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arXiv 0812.3313physics.soc-phcond-mat.stat-mech
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随机语言动力学中共识形成的一般模式

Generic modes of consensus formation in stochastic language dynamics

R. A. Blythe

摘要:

我们介绍了一类随机模型的动态两种语言的变体,竞争成为单一的,共享的公约内的非结构化社区的发言者。模型的不同实例通过代理处理语言中的可变性的方式来区分(即,同一意义的多种形式)。这类模型包括两个以前研究过的语言动力学模型的特殊情况下,代理商往往会在他们最经常遇到的变体上进行选择,以及话语选择模型,代理商往往会通过对话语池进行统一采样来保持可变性。我们减少了完整的复杂性的动态的单坐标随机模型,它允许的概率和时间的发言人达成共识,一个单一的变量来计算大型社区。这一分析表明,在广泛的一类模型考虑,共识是形成在三个通用的方式之一,根据代理人是否倾向于消除,强调或样本中立的语言的变化。这些不同的制度中观察到的模拟的完整的动态,并在某些情况下,简化的模型,使良好的定量预测。我们使用这些结果,以及与相关模型的比较,推测更一般的模型的可能行为,并进一步利用经验数据来论证,在现实中,偏离中性抽样行为的偏差可能很小。

英文摘要:

We introduce a class of stochastic models for the dynamics of two linguistic variants that are competing to become the single, shared convention within an unstructured community of speakers. Different instances of the model are distinguished by the way agents handle variability in the language (i.e., multiple forms for the same meaning). The class of models includes as special cases two previously-studied models of language dynamics, the Naming Game, in which agents tend to standardise on variants they have encountered most frequently, and the Utterance Selection Model, in which agents tend to preserve variability by uniform sampling of a pool of utterances. We reduce the full complexities of the dynamics to a single-coordinate stochastic model which allows the probability and time taken for speakers to reach consensus on a single variant to be calculated for large communities. This analysis suggests that in the broad class of models considered, consensus is formed in one of three generic ways, according to whether agents tend to eliminate, accentuate or sample neutrally the variability in the language. These different regimes are observed in simulations of the full dynamics, and for which the simplified model in some cases makes good quantitative predictions. We use these results, along with comparisons with related models, to conjecture the likely behaviour of more general models, and further make use of empirical data to argue that in reality, biases away from neutral sampling behaviour are likely to be small.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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