# 1Course guide

This course discusses research methods and analysis techniques that allow for the study of human behaviour from a complex systems perspective. Complexity research transcends the boundaries the classical scientific disciplines in terms of explanatory goals (e.g. causal-mechanistic) and is a hot topic in physics, mathematics, biology, economy and psychology.

The main focus in the cognitive behavioural sciences is a description and explanation of behaviour based on interaction dominant dynamics: Many processes interact on many different (temporal and spatial) scales and observable behaviour emerges out of those interactions through a process of self-organization or soft-assembly. Contrary to what the term might suggest, complexity research is often about finding simple models that are able to simulate a wide range of complex behaviour.

This approach differs fundamentally from the more classical approaches where behaviour is caused by a system of many hidden (cognitive) components which interact in sequence as in a machine (component dominant dynamics). The most important difference is how ‘change’, and hence the time-evolution of a system, is studied.

The main focus of the course will be ‘hands-on’ data-analysis in R, or, in Matlab if student is already familiar with the scritping language.

Topics include: Analysis of fractal geometry (i.e. pink noise) in time series (Standardized Dispersion Analysis, Power Spectral Density Analysis, Detrended Fluctuation Analysis); Nonlinear and chaotic time series analysis (Phase Space Reconstruction, (Cross) Recurrence Quantification Analysis, Entropy Estimation); Growth Curve models; Potential Theory; and Catastrophe Theory (Cusp model), Complex Network Analysis.