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. This course will discuss techniques that allow for the study of human behaviour from the perspective of the Complexity Sciences, specifically, Complex Adaptive Systems. Contrary to what the term “complex” might suggest, complexity research is often about finding simple models/explanations that are able to describe 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, in complex systems “everything is interacting with everything”. The idea behind many methods for studying the dynamics of complex systems is to exploit this fact and quantify the degree of interdependence, periodicity, nonlinearity, context-sensitivity or resistance to perturbation (resilience) of system behaviour. Applications in the behavioural sciences are very diverse and concern analyses of continuous-time or trial series data such as response times, heart rate variability or EEG to assess the proficiency of skills, or health and well-being. Complexity methods can also be used for the analysis of categorical data, such as behaviour observation of dyadic interactions (client-therapist, child-caregiver), daily experience sampling, and social and symptom networks. The complex systems approach to behavioural science often overlaps with the idiographic approach of “the science of the individual”, that is, the main 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 a specific case, in a specific context, at a specific moment in time.
The main focus of the course will be hands-on data analysis. Practical sessions will follow after lecture sessions in which specific techniques will be introduced.
After this course, you are able to
- Simulate linear, nonlinear and coupled dynamics using simple models.
- Conduct (multi-fractal) Detrended Fluctuation Analysis and related techniques to quantify global and local scaling relations.
- Conduct Recurrence Quantification Analysis and related techniques to quantify temporal patterns, synchronisation and coupling direction.
- Use idiographic analysis methods and (multiplex) Recurrence Networks to quantify the structure and dynamics of (multivariate) time series.
The course is designed for
All researchers who are interested in acquiring hands-on experience with applying research methods and analytic techniques to study human behaviour from the perspective of Complexity Science. Prior knowledge is not required, but some basic experience using R is highly recommended. Note: This is not a course in dynamic modelling (simulation), the focus is on data analysis.
Admission Requirements
During the course, we will mostly be using the R statistical software environment. Basic experience with R is highly recommended (e.g. installing packages, calling functions that run analyses, handling and plotting data).
Please install R or Jamovi on your computer beforehand. The specifications for your computer are simply this: You need to be able to connect to a wireless network (wifi) and you should be able to install and run the latest version of R. In addition, you might want to be able to use the latest versions of RStudio and Jamovi.
Admission Documents
A short bio which also includes your research interests
Course Fee
€600 for postdocs and professionals, €400 for students and PhDs
For more information click "LINK TO ORIGINAL" below.