Light John M, Jason Leonard A, Stevens Edward B, Callahan Sarah, Stone Ariel
Oregon Research Institute.
De Paul University.
Group Dyn. 2016 Mar;20(1):51-64. doi: 10.1037/gdn0000040. Epub 2016 Jan 28.
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.
群体社会动力学的复杂系统概念通常不仅涉及个体特征的变化，还涉及群体内部关系的变化。随机动态网络建模的最新进展使得可以从数据中对这些相互依存关系进行建模。在其他已经或可能应用于研究中小型群体内部关系和行为随时间演变的数学和统计方法的背景下，对这种方法进行了讨论。基于对五个牛津之家康复之家的试点研究提出了一个示例模型，牛津之家是为急性药物滥用治疗后出院的个人提供清醒生活环境的地方。该模型展示了动态网络建模如何应用于此类系统，研究并讨论了几种汇总选项，并展示了如何根据复杂系统概念来解释结果。结果表明，这种方法（a）即使在数据有限的情况下也是研究群体动力学的可靠建模框架，（b）比最常见的替代方法有所改进，（c）特别适合复杂系统概念。随机模型和相关软件的持续改进最终可能会导致这些技术在群体动力学研究中得到主流应用，这一转变已经在行为科学的相关领域发生。
