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363-0543-00L 3 Credits MSC D-PHYS , D-MTEC , D-MATH
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Agent-Based Modelling of Social Systems

Lecturers & Examiners: Dr. Giacomo Vaccario
VVZ CR n/a

Last Updated: 2026-02-05 16:23:41

Abstract

Agent-based modeling is introduced as a bottom-up approach to understand the complex dynamics of social systems. The course is based on formal models of agents and their interactions. Computer simulations using Python allow the quantitative analysis of a wide range of social phenomena, e.g. cooperation and competition, opinion dynamics, spatial interactions and behaviour in social networks.

Objective

A successful participant of this course is able to - understand the rationale of agent-based models of social systems - understand the relation between rules implemented at the individual level and the emerging behavior at the global level - learn to choose appropriate model classes to characterize different social systems - grasp the influence of agent heterogeneity on the model output - efficiently implement agent-based models using Python and visualize the output

Content

This full-featured course on agent-based modeling (ABM) allows participants with no prior expertise to understand concepts, methods and tools of ABM, to apply them in their master or doctoral thesis. We focus on a formal description of agents and their interactions, to allow for a suitable implementation in computer simulations. Given certain rules for the agents, we are interested to model their collective dynamics on the systemic level. Agent-based modeling is introduced as a bottom-up approach to understand the complex dynamics of social systems. Agents represent the basic constituents of such systems. The are described by internal states or degrees of freedom (opinions, strategies, etc.), the ability to perceive and change their environment, and the ability to interact with other agents. Their individual (microscopic) actions and interactions with other agents, result in macroscopic (collective, system) dynamics with emergent properties, which we want to understand and to analyze. The course is structured in three main parts. The first two parts introduce two main agent concepts - Boolean agents and Brownian agents, which differ in how the internal dynamics of agents is represented. Boolean agents are characterized by binary internal states, e.g. yes/no opinion, while Brownian agents can have a continuous spectrum of internal states, e.g. preferences and attitudes. The last part introduces models in which agents interact in physical space, e.g. migrate or move collectively. Throughout the course, we will discuss a wide variety of application areas, such as: - opinion dynamics and social influence, - cooperation and competition, - online social networks, - systemic risk - emotional influence and communication - swarming behavior - spatial competition While the lectures focus on the theoretical foundations of agent-based modeling, weekly exercise classes provide practical skills. Using the Python programming language, the participants implement agent-based models in guided and in self-chosen projects, which they present and jointly discuss.

Resources

Lecture Notes

The lecture slides will be available on the Moodle platform, for registered students only.

Literature

See handouts. Specific literature is provided for download, for registered students only.

General Information

Language
English
Levels
MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 90 minutes
Aids
keine.
Digital
The exam takes place on devices provided by ETH Zurich.
The examination will account for 70% of the final grade and will be conducted electronically. The "closed book" rule applies: no books, no summaries, no lecture materials. The exam questions and answers will be only in English. The use of a paper-based dictionary is permitted. The course project will be graded and counts with 30% to the final grade.

Course Components

Type Title Time & Place Hours
lecture Agent-Based Modelling of Social Systems
  • Thu 14:15-16:00 (HG E 33.3)
2 h weekly
exercise Agent-Based Modelling of Social Systems
  • Thu 18:15-19:00 (HG E 33.3)
1 h weekly

Offered In

  • Management, Technology and Economics Master
  • Mathematics Master
    • Application Area (Only necessary and eligible for the Master degree in Applied Mathematics. One of the application areas specified must be selected for the category Application Area for the Master degree in Applied Mathematics. At least 8 credits are required in the chosen application area. Credits from other application areas cannot be recognised for further application areas.)
  • Physics Master
    • Electives
      • General Electives (Students may choose General Electives from the entire course programme of ETH Zurich - with the following restrictions: courses that belong to the first or second year of a Bachelor curriculum at ETH Zurich as well as courses from GESS "Science in Perspective" are not eligible here. The following courses are explicitly recommended to physics students by their lecturers. (Courses in this list may be assigned to the category "General Electives" directly in myStudies. For the category assignment of other eligible courses keep the choice "no category" and take contact with the Study Administration ( ) after having received the credits.))