Santa Fe Institute, New Mexico 87501, USA.
Chaos. 2011 Sep;21(3):037108. doi: 10.1063/1.3643063.
We review an empirically grounded approach to studying the emergence of collective properties from individual interactions in social dynamics. When individual decision-making rules, strategies, can be extracted from the time-series data, these can be used to construct adaptive social circuits. Social circuits provide a compact description of collective effects by mapping rules at the individual level to statistical properties of aggregates. This defines a simple form of social computation. We consider the properties that complexity measures would need to have to best capture regularities at different level of analysis, from individual rules to circuits to population statistics. One obvious benefit of using the properties and structure of biological and social systems to guide the development of complexity measures is that it is more likely to produce measures that can be applied to data. Principled but pragmatic measures would allow for a rigorous investigation of the relationship between adaptive features at the micro, meso, and macro scales, a long standing goal of evolutionary theory. A second benefit is that empirically grounded complexity measures would facilitate quantitative comparisons of strategies, circuits, and aggregate properties across social systems.
我们回顾了一种从社会动力学中个体相互作用中研究集体属性出现的经验基础方法。当个体决策规则和策略可以从时间序列数据中提取出来时，可以将其用于构建自适应社会电路。社会电路通过将个体水平的规则映射到聚集的统计特性上来提供对集体效应的紧凑描述。这定义了一种简单形式的社会计算。我们考虑了复杂性度量需要具有的特性，以便在不同的分析层次（从个体规则到电路再到群体统计）上最好地捕捉规律性。使用生物和社会系统的属性和结构来指导复杂性度量的发展的一个明显好处是，它更有可能产生可以应用于数据的度量。有原则但务实的度量将允许在微观、中观和宏观尺度上对适应性特征之间的关系进行严格的研究，这是进化理论的一个长期目标。第二个好处是，基于经验的复杂性度量将有助于在不同的社会系统中对策略、电路和聚合属性进行定量比较。
