The complex system conception of group social dynamics often involves not only changing individual characteristics, but also changing within-group relationships. Recent advances in stochastic dynamic network modeling allow these interdependencies to be modeled from data. This methodology is discussed within a context of other mathematical and statistical approaches that have been or could be applied to study the temporal evolution of relationships and behaviors within small- to medium-sized groups. An example model is presented, based on a pilot study of five Oxford House recovery homes, sober living environments for individuals following release from acute substance abuse treatment. This model demonstrates how dynamic network modeling can be applied to such systems, examines and discusses several options for pooling, and shows how results are interpreted in line with complex system concepts. Results suggest that this approach (a) is a credible modeling framework for studying group dynamics even with limited data, (b) improves upon the most common alternatives, and (c) is especially well-suited to complex system conceptions. Continuing improvements in stochastic models and associated software may finally lead to mainstream use of these techniques for the study of group dynamics, a shift already occurring in related fields of behavioral science.

中文翻译：

---

群体动力学复杂系统方法的数学框架：康复之家社会融合案例。


群体社会动态的复杂系统概念通常不仅涉及改变个体特征，还涉及改变群体内关系。随机动态网络建模的最新进展允许根据数据对这些相互依赖性进行建模。该方法是在其他数学和统计方法的背景下讨论的，这些方法已经或可能应用于研究中小型群体内关系和行为的时间演变。提出了一个示例模型，该模型基于对五个牛津之家康复之家的试点研究，为从急性药物滥用治疗中释放出来的个人提供清醒的生活环境。该模型演示了如何将动态网络建模应用于此类系统，检查和讨论池化的几种选项，并展示如何根据复杂的系统概念解释结果。结果表明，该方法 (a) 是一种可靠的建模框架，即使数据有限，也可用于研究群体动力学；(b) 改进了最常见的替代方案；(c) 特别适合复杂的系统概念。随机模型和相关软件的持续改进可能最终导致这些技术在群体动力学研究中的主流使用，这是行为科学相关领域已经发生的转变。