论文标题

小世界还不够:网络选择的标准和模拟的结论性

Small world is not enough: Criteria for network choice and conclusiveness of simulations

论文作者

Thiriot, Samuel

论文摘要

大多数基于代理的模型都包含一个描述人造人群中相互作用结构的社交网络。由于该结构对模拟动力学的巨大影响,建模者创建了该网络,以符合合理性的标准(例如,小世界属性)。网络实际上是由一个符合这些条件的一个网络生成器创建的,例如小世界网络的瓦茨 - 史特罗加兹算法。但是,这种做法是研究模型对一种算法产生的特定网络的动力学,而不是感兴趣的网络网络上的动态,可能会引起结果的巨大偏见。我们确定了与此偏见相关的三个问题:(i)网络生成器对一类网络的代表性,(ii)模拟在一类网络上的结论性,以及(iii)在完善网络选择标准时的结论增长。我们提出了一个实验协议,并将其实例化在小世界网络上,以实现流行病,意见和文化动态。我们表明(i)瓦特斯特罗盖茨网络并不代表小世界网络(II)小世界上的仿真结果,可以说是尚无定论的,而且(iii)甚至具有相同大小,密度,传递性和平均路径长度的小世界网络也不会导致相一致的结果。除了质疑从人工网络获得的仿真结果的相关性外,这项研究还构成了探索其他不仅专注于网络统计属性的其他方法的论点。

Most agent-based models include a social network that describes the structure of interactions within the artificial population. Because of the dramatic impact of this structure on the simulated dynamics, modelers create this network for it to match criteria of plausibility (e.g. the small-world property). Networks are actually created by one network generator compliant with these criteria, like the Watts-Strogatz algorithm in the case of small-world networks. However, this practice comes to study the model's dynamics over the specific networks generated by one algorithm instead of the dynamics over the class of networks of interest, possibly inducing a strong bias in results. We identify three problematics related to this bias: (i) representativity of a network generator to a class of networks, (ii) conclusiveness of simulations over a class of networks and (iii) the gain in conclusiveness when refining the criteria for network choice. We propose an experimental protocol and instantiate it on small-world networks for epidemics, opinion and culture dynamics. We show that (i) Watts-Strogatz networks are not representative of small-world networks (ii) simulation results over small-worlds are arguably inconclusive, and (iii) even small-world networks having the same size, density, transitivity and average path length do not lead to coherent results. Beyond questioning the relevance of simulation results obtained from artificial networks, this research also constitute one more argument for the exploration of other approaches that are not solely focused on networks' statistical properties.

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