Behaviour Dynamics in Social Networks

2019-2020

Course Objective

By the end of the course, students will be able to:
1) identify different types of mental and social processes; (Knowledge
and understanding)
2) understand how individual and social behaviour emerges from
mechanisms known from Cognitive, Affective and Social Neuroscience, and
from Cognitive and Social Sciences; (Knowledge and understanding)
3) construct network models for mental and social interaction processes;
(Applying knowledge and understanding) (Making judgements)
4) perform analysis based on these models using Network-Oriented
Modeling software tools and empirical data. (Applying knowledge and
understanding) (Lifelong learning skills)

Course Content

This is a multidisciplinary course, also accessible for students from
other disciplines such as Neuroscience, Psychology or Social Sciences.
Behaviour dynamics occurs in different forms, contexts and complexity.
Complexity can occur in the mental processes within persons, in social
interaction processes, or in both. Both types of processes can be
adaptive: mental processes can change due to learning, and social
interactions can also evolve over time. Theories and findings from
Cognitive, Affective and Social Neuroscience and also from Cognitive and
Social Sciences are presented and used to get insight in the underlying
mechanisms that form a solid scientific basis for modelling of these
processes. A Network-Oriented Modeling approach based on temporal-causal
networks is used to model both these internal mental processes (as
networks of mental states) and social interaction processes (as social
networks). During the course, several examples are presented. These
examples cover imagination and dreaming by internal simulation,
integration of emotions in all kinds of mental and social processes,
learning of emotion regulation, ownership and attribution of actions,
empathic social responses, empathic joint decision making, development
of shared understanding and collective action, and different principles
for evolving social networks. The dynamics of such processes is modeled,
simulated and analysed (including verification and validation) in this
course using dedicated and easy to use modelling environments for
Network-Oriented Modeling; no programming is needed. In the last few
weeks of the course, a more ambitious final assignment is addressed,
which can be worked out to a paper that may be submitted to an
international conference, where it could be presented and provide a
publication.

Teaching Methods

Combinations of lectures and practical assignments.

Method of Assessment

Practical assignments (30%) and a final assignment (70%, including
presentation of the final assignment).
No resit is possible for the practical assignments.

Literature

Treur, J., Network-Oriented Modeling: Addressing Complexity of
Cognitive, Affective and Social Interactions. Series on Understanding
Complex Systems, Springer Publishers, October 2016.
URL: http://www.springer.com/gp/book/9783319452111#aboutBook
Free downloadable from the VU at doi:
http://dx.doi.org/10.1007/978-3-319-45213-5
Table of Contents: http://www.few.vu.nl/~treur/cve/Papers/NOMToC.pdf

Target Audience

MSc Artificial Intelligence
MSc Management, Policy Analysis and Entrepreneurship in Health and Life
Sciences

Recommended background knowledge

None

General Information

Course Code X_400113
Credits 6 EC
Period P2
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator prof. dr. J. Treur
Examiner prof. dr. J. Treur
Teaching Staff prof. dr. J. Treur

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Practical
Target audiences

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