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Department of Informatics Blockchain and Distributed Ledger Technologies


Agent-based modelling is a methodology with ample applications in data-intensive fields. Its main focus is not about constructing regressive models to fit observed data. Instead it is about understanding the link between micro-level dynamics (local rules of interaction, behaviour) and (emergent) macro-properties. Therefore, it revolts around the conceptualisation and analysis of stylised - and minimalistic - models that capture specific mechanisms at work. 


The course covers topics as variegated as: Product adoption, diffusion of opinions, virus diffusion (in social and computer networks), segregation in society, consensus formation (again in social and computer networks), agent behaviour in financial markets. Interestingly, the techniques described are not only valid for the specific systems under consideration, but they can be easily applied to other focal areas of interest. 


The course is highly interactive. All the lectures have first a theoretical part, then, the students must develop (in small groups and always supported by the instructors) the models themselves. This allows them to gain direct experience and familiarity with the concepts taught and the techniques involved. In this participatory environment, multiple exercises and the creation of visualisations play an important role


Prof. Dr. Claudio J. Tessone (theory and practice)

Dr. Manuel Sebastian Mariani (theory and practice)

Jian Hong Lin (practice)

Semester course (seminar-like: highly interactive)

Target Audience:
Master students assigned to “Wahlpflichtbereich" BWL 4

Each spring semester


Work load statement:

Part Workload Total Time

Course attendance


12 lectures à 2h
(12 sessions)


Course attendance


12 lectures à 3h
(12 sessions)


Home works 3h per session 36h
Literature study Preparation before class 30h
Assignment Preparation and Final Work 54h
Total   180h


Topics typically comprised in the course contents include (but are not limited to):

  • Diffusion of innovations and product adoption
  • Innovators and imitators
  • Models of epidemics (from illnesses to computer viri)
  • Consensus (in social groups and computer networks)
  • Imitation, herding
  • Cellular automata
  • Traffic and human dynamics
  • Self-organised criticality
  • Social Segregation
  • Evolution of Culture and Languages

The complete list is in the Syllabus


Basic Python and/or R programming skills (or the willingness to develop this knowledge prior to the course) are a necessary requirement. Basic probability theory, linear Algebra

Suggested reading:

  • N. Gilbert. Agent-Based Models (2007, Sage Publications, London)
  • Miller, John H., and Scott E. Page. Complex adaptive systems: An introduction to computational models of social life (2009, Princeton University Press)
  • T.C. Schelling. Micromotives and Macrobehavior (1978, Norton, New York)
  • M. Granovetter. Society and Economy: Framework and Principles (2017, Harvard University Press)


Weiterführende Informationen


The Syllabus for this course can be downloaded here. Please read it as it contains detailed information on the course