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Complexity research transcends the boundaries between the classical scientific disciplines and is a hot topic in physics, mathematics, biology, economy as well as psychology and the life sciences and is collectively referred to as the Complexity Sciences. This course will discuss techniques that allow for the study of human behaviour from the perspective of the Complexity Sciences, specifically, the study of complex physical systems that are alive and display complex adaptive behaviour such as learning and development.
Contrary to what the term “complex” might suggest, complexity research is often about finding simple models / explanations that are able to simulate a wide range of qualitatively different behavioural phenomena. “Complex” generally refers to the object of study: Complex systems are composed of many constituent parts that interact with one another across many different temporal and spatial scales to generate behaviour at the level of the system as a whole that can appear to be periodic, nonlinear, unstable or extremely persistent. The focus of many research designs and analyses is to quantify the degree of periodicity, nonlinearity, context sensitivity or resistance to perturbation by exploiting the fact that “everything is interacting” in complex systems.
This requires a mathematical formalism and rules of scientific inference that are very different from the mathematics underlying traditional statistical analyses that assume “everything is NOT interacting” in order to be able to validly infer statistical regularities in a dataset and generalise them to a population. The complex systems approach to behavioural science often overlaps with the idiographic approach of the science of the individual, that is, the goal is not to generalise properties or regularities to universal or statistical laws that hold at the level of infinitely large populations, but to apply general principles and universal laws that govern the adaptive behaviour of all complex systems to study specific facts, about specific systems observed in specific contexts at a specific instant.
The main focus of the course will be to discuss scientific papers that apply a complex systems approach to behavioural science, with a focus on idiographic methods. We offer guided and annotated video instructions of assignments in R that will provide a basic understanding of the methods used in the papers we discuss. The following topics will be covered:
- Behavioural science as an idiographic science
- Behavioural science as a science that studies complex adaptive systems and networks.
- Simple models of linear and nonlinear dynamical behaviour (Linear & logistic growth, Predator-Prey dynamics, Lorenz system, the chaos game);
- Analysis of long range dependence in time and trial series (Entropy, Relative roughness, Standardized Dispersion Analysis, Detrended Fluctuation Analysis).
- Quantification of temporal patterns in time and trial series including dyadic interactions and synchronization (Phase Space Reconstruction, [Cross] Recurrence Quantification Analysis).
- Early Warning Signals of behaviour change (e.g. in psychppathology, dyadic interactions, development, etc.)
- Network analyses (Estimating symptom networks, calculating network based complexity measures)
Teaching format
The lectures will focus on explaining theoretical backgrounds and methods used in the papers students have to read each week. Several meetings include a part where guest lecturers discuss the use of one or more complexity methods in their research. Questions about the weekly assignments can be posted on brightspace. These questions will serve as the basis for a Q&A session that will be recorded as a commentary video and posted on brightspace. If no questions are submitted, there will be no Q&A session.
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