Hyde Graham, Fath Brian D, Zoller Hannah
Department of Physics, Astronomy and Geosciences, Towson University, Towson, MD, 21252, USA.
Department of Biological Sciences, Towson University, Towson, MD, 21252, USA.
Sci Rep. 2024 Jul 16;14(1):16451. doi: 10.1038/s41598-024-66654-1.
Quantifying the properties of complex, self-organizing systems is increasingly important for understanding the development and state of modern systems. Case studies have recommended sustainability frameworks predominately in literature, but little emphasis has been placed on methodological evaluation. Data availability is often an obstacle that constrains conventional flow-based network analysis, but a novel information-based technique (QtAC) developed by zu Castell and Schrenk overcomes these constraints by modelling interactions between agents as information transfers. This study compares the QtAC method to conventional flow analysis by applying both to the same 90-year dataset containing socio-economic data from the island of Samothraki, Greece. Resilience indicators, based on Ulanowicz's ascendency analysis, are derived on both the information- and flow-based networks. We observe that the resulting dynamics of the information-based networks align closer with complex system dynamics as theorized by the adaptive cycle model. Additionally, we discuss how QtAC offers different interpretations of network indicators when compared to usual interpretations of flow analysis. Ultimately, QtAC is shown to provide an alternative for complex systems analysis if the data situation does not allow for conventional flow-analysis. Furthermore, we show that the combination of both approaches can yield valuable new insights.
量化复杂的自组织系统的特性对于理解现代系统的发展和状态越来越重要。案例研究在文献中主要推荐了可持续性框架，但对方法学评估的重视程度较低。数据可用性往往是限制传统基于流量的网络分析的一个障碍，但祖·卡斯特尔和施伦克开发的一种新颖的基于信息的技术（QtAC）通过将主体之间的相互作用建模为信息传递，克服了这些限制。本研究通过将QtAC方法和传统流量分析应用于包含希腊萨莫色雷斯岛90年社会经济数据的同一数据集，对两者进行了比较。基于乌拉诺维茨的优势分析得出的弹性指标，应用于基于信息和流量的网络。我们观察到，基于信息的网络所产生的动态与自适应循环模型所理论化的复杂系统动态更为吻合。此外，我们还讨论了与流量分析的常见解释相比，QtAC如何对网络指标提供不同的解释。最终结果表明，如果数据情况不允许进行传统的流量分析，QtAC可为复杂系统分析提供一种替代方法。此外，我们还表明，两种方法的结合可以产生有价值的新见解。
