Complexity methods for behavioural sciences: YouTube channel and resources

In 2019 I attended an exciting summer school; Complexity Methods for Behavioural Science: A Toolbox for Studying Change. Later, we – that is, the University of Helsinki Behaviour Change and Wellbeing Group – had the opportunity to invite Fred Hasselman, who devised the course, to Finland. He gave an overview talk as well as a 3-day workshop, which I recorded with varying success. This page collates resources regarding the course.

For all the recordings, see our YouTube channel. There are two playlists; one for short snippets and another one for full-length lectures. Here are some tweets on the course, with links to further resources. For additional slides, see here. See the end of the post for literature!

  • Lecture 0 (video, slides)Overview of complexity science and its applications in behavioural sciences. Also see shorter snippets on ergodicity, interaction- vs. component-dominant dynamics, and my interview with Fred.
  • Lecture 1 (video, slides [1-25])Introduction to Complexity Science: Dissipative systems, Self-Organization, Self-Organised Criticality (SOC), Phase transition, Interaction Dominant Dynamics, Emergence, Synchronisation.
  • Lecture 2 (video, slides [26-74])Introduction to the mathematics of change: Logistic Map, Return Plot, Attractors. [The beginning of the lecture was cut due to camera problems; please find a great introduction to the logistic map here.]
  • Lecture 3 (video, slides)Basic Time Series Analysis: Autocorrelation Function, Sample Entropy, Relative Roughness.
  • Lecture 4 (video, slides [34 onwards, see also this, this and this]) – Detecting (nonlinear) structure in time series: Fractal Dimension, Detrended Fluctuation Analysis, Standardised Dispersion Analysis.
  • Lecture 5 (video, slides [1-16])Quantifying temporal patterns in unordered categorical time series data: Categorical Auto-Recurrence Quantification Analysis (RQA).
  • Lecture 6 (video, slides [17-52])Quantifying temporal patterns in continuous time series data: Continuous Auto-Recurrence Quantification Analysis, Phase-space reconstruction.
  • Lecture 7 (video, slides [52-70]) – Recurrence Quantification Analysis in practice: Data preparation for RQA, “General recipe” (i.e. RQA workflow), lagged/windowed RQA, RQA in detecting cognitive phase transitions, RQA in neural imaging.
  • Lecture 8 (video, slides) – Multivariate Recurrence Quantification Analysis: Cross-Recurrence Quantification Analysis (CRQA), applications in interpersonal synchronisation dynamics (leader-follower behaviour), Diagonal Cross-Recurrence Profiles (DCRP).
  • Lecture 9 (video, slides) – Multivariate Time Series Analysis – Dynamic Complexity & Phase Transitions in Psychology: Self-ratings as a research tool, the importance of sampling frequency, dynamic complexity as an early warning signal in psychopathology.
  • Lecture 10 (video, slides [1-37]) – Introduction to graph theory, with applications of network science: Complex networks, hyperset theory, network-based complexity measures, small-world networks.
  • Lecture 11 (video, slides [38-80]) – Multiplex recurrence networks for non-linear multivariate time series analysis: Recurrence networks, change profiles of ecological momentary assessments as an alternative to raw scores. Also see this paper!

 

Matti spiral

Literature:

Three recent papers directly related to the course’s topics:

Hasselman, F., & Bosman, A. M. T. (2020). Studying Complex Adaptive Systems with Internal States: A Recurrence Network Approach to the Analysis of Multivariate Time Series Data Representing Self-Reports of Human Experience. Frontiers in Applied Mathematics and Statistics, 6. https://doi.org/10.3389/fams.2020.00009

Heino, M. T. J., Knittle, K. P., Noone, C., Hasselman, F., & Hankonen, N. (2020). Studying behaviour change mechanisms under complexity [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fxgw4

Olthof, M., Hasselman, F., & Lichtwarck-Aschoff, A. (2020). Complexity In Psychological Self-Ratings: Implications for research and practice [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fbta8

More resources on complexity:

  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. https://doi.org/10.1023/A:1016957424329 [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. https://doi.org/10.1080/10503307.2019.1633484 [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). [INTRO COMPLEXITY SCIENCE – Social and personality psychology]
  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. https://doi.org/10.3389/fpsyg.2018.02232 [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 http://www.saistmp.com/publications/spiegorqa.pdf [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. https://doi.org/10.1142/S0218127411029008 [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. https://doi.org/10.3390/systems4040037 [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. https://doi.org/10.1080/10407413.2013.753804 [MULTI-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. https://doi.org/10.1080/10407413.2017.1368355 [(MULTI-)FRACTAL ANALYSIS]
  15. Hawe, P. (2015). Lessons from Complex Interventions to Improve Health. Annual Review of Public Health, 36(1), 307–323. https://doi.org/10.1146/annurev-publhealth-031912-114421
  16. Rickles, D., Hawe, P., & Shiell, A. (2007). A simple guide to chaos and complexity. Journal of Epidemiology & Community Health, 61(11), 933–937. https://doi.org/10.1136/jech.2006.054254. [INTRO COMPLEXITY SCIENCE – Public health]
  17. Pincus, D., Kiefer, A. W., & Beyer, J. I. (2018). Nonlinear dynamical systems and humanistic psychology. Journal of Humanistic Psychology, 58(3), 343–366. https://doi.org/10.1177/0022167817741784.  [INTRO COMPLEXITY SCIENCE – Positive psychology]
  18. Gomersall, T. (2018). Complex adaptive systems: A new approach for understanding health practices. Health Psychology Review, 0(ja), 1 – 34. https://doi.org/10.1080/17437199.2018.1488603. [INTRO COMPLEXITY SCIENCE – Health psychology]
  19. Nowak, A., & Vallacher, R. R. (2019). Nonlinear societal change: The perspective of dynamical systems. British Journal of Social Psychology, 58(1), 105-128. https://doi.org/10.1111/bjso.12271. [INTRO COMPLEXITY SCIENCE – Societal change]
  20. Carello, C., & Moreno, M. (2005). Why nonlinear methods. In Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 1–25). Retrieved from https://nsf.gov/pubs/2005/nsf05057/nmbs/chap1.pdf [INTERACTION DOMINANCE, ERGODICITY]
  21. Liebovitch, L. S., & Shehadeh, L. A. (2005). Introduction to fractals. In Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 178–266). Retrieved from https://nsf.gov/pubs/2005/nsf05057/nmbs/chap5.pdf [FRACTAL ANALYSIS]