Data analysis using circular causality in networks

Abstract

Complex systems in causal relationships are known to be circular rather than linear; this means that a particular result is not produced by a single cause, but rather that both positive and negative feedback processes are involved. However, although interpreting systemic interrelationships requires a language formed by circles, this has only been developed at the diagram level, and not from an axiomatic point of view. The first difficulty encountered when analysing any complex system is that usually the only data available relate to the various variables, so the first objective was to transform these data into cause-and-effect relationships. Once this initial step was taken, our discrete chaos theory could be applied by finding the causal circles that will form part of the system attractor and allow their behavior to be interpreted. As an application of the technique presented, we analyzed the system associated with the transcription factors of inflammatory diseases.

Causal Analysis of the Spanish Industrial Sector Through Smarta

“…In 2014, Lloret and Nescolarde [20] developed a technique to convert large amounts of data into a directed graph of cause-effect relationships which was then applied to a qualitative version of Chaos Theory. The technique aimed at finding the complex system's attractor sets or trends.…”

Causal Analysis of Databases Concerning Electromagnetism and Health

“…Respondents were asked the following questions: "Please indicate to what extent you think each of the following affects your health," where the elements were An analytical definition of attractors can be found in [9][10][11][12][13]. Attractors are areas that define the seemingly disorganized behavior of the variables and thus are essential for predicting the behavior or trend that a complex system will follow over time.…”