Introduction to the analytical toolbox of Complexity Science


In order to gain new insights on multicausal social and behavioural phenomena, it may be necessary to surmount the usual assumptions of traditional linear (e.g. multilevel or structural equation) models. This workshop will provide an introduction to some of the formal models, research methods, and analytical techniques that allow for the study of human behaviour from a complex systems perspective. No special background knowledge is required to participate.

Learning outcomes

At the end of this course, students have reached a level of understanding that will allow them to:

  • Study relevant scientific literature using a complex systems approach to behavioural science.
  • Getting help with using a complex systems approach in their own scientific inquiries, e.g. by being able to ask relevant questions to experts on a specific topic discussed during the course.
  • Work through tutorials on more advanced topics that were not discussed during the course.
  • Keep up with the continuous influx of new theoretical, methodological and empirical studies on applying the complex systems approach in the social and behavioural sciences.


Complexity research transcends the boundaries between the classical scientific disciplines and—having been a hot topic in physics, mathematics and biology—has recently gained much traction in the social sciences. It provides a rigorous, non-reductionist way to approach the context-dependence of social phenomena, as well as individual agentic human behaviour.

The focus is a description and explanation of behaviour based on interaction dominant dynamics: Many processes interact on different temporal and spatial scales (including across the micro-, meso and macro-levels), and behaviour emerges out of those interactions through physical processes such as self-organization or soft-assembly.

Contrary to what the name might suggest, complexity research is often about finding simple models or collective variables with which a wide range of different behavioural modes can be described. This approach differs fundamentally from the more classical approaches in which behaviour is considered the additive result of many independent, component processes (component dominant dynamics) and the goal of research is to identify efficient causes of behaviour.

Tentative program:

I. Introduction to the mathematics of change

  • Modelling (nonlinear) growth
  • Predator-Prey dynamics and Deterministic Chaos
  • Basic timeseries analysis
  • Scaling phenomena in time and trial series of human behaviour and physiology

II. Quantifying Recurrences in State Space

  • Takens’ Theorem and State-Space reconstruction
  • Recurrence Quantification Analysis of continuous and categorical data
  • Cross-Recurrence Quantification Analysis of dyadic interaction

III. Network Topology and Early Warning Signals

  • Early Warning Signals in clinical interventions
  • Representing time series as complex networks

Teaching and learning methods

Each meeting starts with a lecture addressing the theoretical and methodological backgrounds of the practical applications that will be used in hands-on assignments during the practical sessions.

  • Course site:

  • Sometimes there are links to websites that have additional literature, one such source we call “the course book”:

  • Additional literature will be made available during the course)

  • Most assignments about analyses were designed for R. You do not need to learn to script/code, you mainly need to be able to load data and run scripts. There will be instructions in lecture notes and assignment solutions that should give you enough information to work out what to do.

  • Modules have been developed for JAMOVI, download the modules here:

In order to get the 3 study credits, students reflect what they have learned by writing a learning diary (max. 3 pages), making use of the reading list (min. 10 articles) and referencing appropriately, and return the diary within 2 weeks from the end of the course to Nelli Hankonen,

Preliminary reading list

Choose 10 from the list below:

  1. Mathews, K. M., White, M. C., & Long, R. G. (1999). Why Study the Complexity Sciences in the Social Sciences? Human Relations, 52(4), 439–462. [INTRO COMPLEXITY SCIENCE]

  2. Richardson, M. J., Kallen, R. W., & Eiler, B. A. (2017). Interaction-Dominant Dynamics, Timescale Enslavement, and the Emergence of Social Behavior. In Computational Social Psychology (pp. 121–142). New York: Routledge. [INTERACTION-DOMINANCE]

  3. Molenaar, P. C., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current directions in psychological science, 18(2), 112-117. [ERGODICITY]

  4. Kello, C. T., Brown, G. D., Ferrer-i-Cancho, R., Holden, J. G., Linkenkaer-Hansen, K., Rhodes, T., & Van Orden, G. C. (2010). Scaling laws in cognitive sciences. Trends in cognitive sciences, 14(5), 223-232. [SCALING PHENOMENA]

  5. Lewis, M. D. (2000). The promise of dynamic systems approaches for an integrated account of human development. Child development, 71(1), 36-43. [STATE SPACE, DYNAMICS]

  6. Olthof, M., Hasselman, F., Strunk, G., van Rooij, M., Aas, B., Helmich, M. A., … Lichtwarck-Aschoff, A. (2019). Critical Fluctuations as an Early-Warning Signal for Sudden Gains and Losses in Patients Receiving Psychotherapy for Mood Disorders. Clinical Psychological Science, 2167702619865969. [DYNAMIC COMPLEXITY]

  7. Olthof, M., Hasselman, F., Strunk, G., Aas, B., Schiepek, G., & Lichtwarck-Aschoff, A. (2019). Destabilization in self-ratings of the psychotherapeutic process is associated with better treatment outcome in patients with mood disorders. Psychotherapy Research, 0(0), 1–12. [DYNAMIC COMPLEXITY]

  8. Richardson, M., Dale, R., & Marsh, K. (2014). Complex dynamical systems in social and personality psychology: Theory, modeling and analysis. In Handbook of Research Methods in Social and Personality Psychology (pp. 251–280).

  9. Wallot, S., & Leonardi, G. (2018). Analyzing Multivariate Dynamics Using Cross-Recurrence Quantification Analysis (CRQA), Diagonal-Cross-Recurrence Profiles (DCRP), and Multidimensional Recurrence Quantification Analysis (MdRQA) – A Tutorial in R. Frontiers in Psychology, 9. [MULTIDEMINSIONAL RQA]

  10. Webber Jr, C. L., & Zbilut, J. P. (2005). Recurrence quantification analysis of nonlinear dynamical systems. In Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 26–94). Retrieved from [RQA]

  11. Marwan, N. (2011). How to avoid potential pitfalls in recurrence plot based data analysis. International Journal of Bifurcation and Chaos, 21(04), 1003–1017. [RQA parameter estimation]

  12. Boeing, G. (2016). Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction. Systems, 4(4), 37. [LOGISTIC MAP, DERTERMINISTIC CHAOS]

  13. Kelty-Stephen, D. G., Palatinus, K., Saltzman, E., & Dixon, J. A. (2013). A Tutorial on Multifractality, Cascades, and Interactivity for Empirical Time Series in Ecological Science. Ecological Psychology, 25(1), 1–62. [MULIT-FRACTAL ANALYSIS]

  14. Kelty-Stephen, D. G., & Wallot, S. (2017). Multifractality Versus (Mono-) Fractality as Evidence of Nonlinear Interactions Across Timescales: Disentangling the Belief in Nonlinearity From the Diagnosis of Nonlinearity in Empirical Data. Ecological Psychology, 29(4), 259–299. [(MULIT-)FRACTAL ANALYSIS]

  15. Hawe, P. (2015). Lessons from Complex Interventions to Improve Health. Annual Review of Public Health, 36(1), 307–323.

  16. Rickles, D., Hawe, P., & Shiell, A. (2007). A simple guide to chaos and complexity. Journal of Epidemiology & Community Health, 61(11), 933–937.

  17. Pincus, D., Kiefer, A. W., & Beyer, J. I. (2018). Nonlinear dynamical systems and humanistic psychology. Journal of Humanistic Psychology, 58(3), 343–366.

  18. Gomersall, T. (2018). Complex adaptive systems: A new approach for understanding health practices. Health Psychology Review, 0(ja), 1–34.

  19. Nowak, A., & Vallacher, R. R. (2019). Nonlinear societal change: The perspective of dynamical systems. British Journal of Social Psychology, 58(1), 105–128.

  20. Carello, C., & Moreno, M. (2005). Why nonlinear methods. In Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 1–25). Retrieved from [INTERACTION DOMINANCE, ERGODICITY]

  21. Liebovitch, L. S., & Shehadeh, L. A. (n.d.). Introduction to fractals. In Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 178–266). Retrieved from [FRACTAL ANALYSIS]

Location of lectures

The location for lectures on Tuesday and Thursday is:

Athena, Siltavuorenpenger 3A, room 360 (Tuesday), room 168 (Thursday).

The location for lectures on Wednesday is:

Aurora, Siltavuorenpenger 10, room 224.


Fred Hasselman


My research and teaching centers on the development and application of theoretical frameworks and research methods to study of human behaviour, form the perspective of Complexity Science. The main focus is on developing a so-called “person-specific” (idiographic) research paradigm in which modeling and quantification of intra-individual variability (parameters of a complex dynamical system) is more useful than modeling inter-individual variability (estimating parameters of a static population, or true value).

From this perspective the current ‘Replicability Crisis’ in the social sciences could be a problem of operating within the wrong theoretical paradigm, rather than the wrong application of research methods and inferential statistics.

Complex Systems Group at the Behavioural Science Institute of the Radboud University Nijmegen