SOW-BS044
Dynamics of Complex Systems
Course infoSchedule
Course moduleSOW-BS044
Credits (ECTS)4
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Behavioural Science;
Lecturer(s)
Coordinator
dr. F.W. Hasselman
Other course modules lecturer
Examiner
dr. F.W. Hasselman
Other course modules lecturer
Contactperson for the course
dr. F.W. Hasselman
Other course modules lecturer
Lecturer
dr. M.L. Wijnants
Other course modules lecturer
Academic year2018
Period
PER3-PER4  (04/02/2019 to 12/07/2019)
Starting block
PER3
Course mode
full-time
RemarksFor Behavioural Science RM students only, non-BSRM students interested in the course, please mail to rm@bsi.ru.nl
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
 
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 behavioural-, cognitive- and neurosciences.
 
Content
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 idiographical 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 hands-on data-analysis and the main analytical tool we will use is R (if you are an expert: It is also possible to use Matlab for most of the assignments, let us know in advance). Practical sessions will follow after a lecture session in which a specific technique will be introduced.
We will cover the following topics:
-    Theoretical background of phase transitions (self-organised criticality) and synchronisation (coupling dynamics) in complex dynamical 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 (Phase Space Reconstruction, [Cross] Recurrence Quantification Analysis).
-    Network analyses (Estimating symptom networks, calculating network based complexity measures)

Teaching format 
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. Several meetings include a part where guest lecturers discuss the use of one or more techniques in their recent research.

Exam information
Examination will be based on a final assignment and a check of participation in weekly discussions on Brightspace (the content of contributions will not be evaluated).
Specifically:
-    To prepare for each lecture students read a contemporary research paper in which a complex systems approach is used to a phenomenon studied in behavioural science. Students are required to formulate questions about each paper, and to initiate a discussion with their fellow-students on Brightspace. Each week at least one post by each student is expected in the discussion forum.
-    A final take-home assignment will be provided at the end of the course. Details will be discussed during the course. In general, the assignment will take about 2 days to complete, the time available to complete the assignment will be 1-2 weeks depending on the schedule.
Required materials
To be announced
A list of required and optional literature for each of the meetings will be announced online.

Instructional modes
Computer Practicals

Lectures

Tests
Participation in weekly discussion
Test weight0
Test typeParticipation
OpportunitiesBlock PER3

Take-home exam
Test weight1
Test typeExam
OpportunitiesBlock PER3, Block PER4