Networks with nodes (variables) that are either on or off (Ising model) have been of great value to psychopathology. However, the dynamics of such networks (evolution over time) has been less investigated. We assert that one of the issues with the dynamics on networks is the difficulty in obtaining qualitative features, like what ratio of on and off nodes will the network end up with after some time. These questions are relevant to psychopathology since the sum of variables (often a proxy for symptoms) provides an indication of the severity of the disorder. Here, we propose a framework to establish long term features of networks. The framework allows for the translation of some phenomena of multiple variables to dynamical systems, which has been a very fruitful area for psychology. We show that the approximations of the framework (i.e., Markov chains and dynamical systems) are accurate. Furthermore, we illustrate with a time series from a patient diagnosed with depression some of the properties that can be investigated by reducing networks to dynamical systems. One of those properties, which we borrow from weather predictions, is statistical regularity for different initial conditions of the process to see where it ends up.
原文链接
分享
引用
