|Abstract||Agent-based modelling is introduced as a bottom-up approach to understand the complex dynamics of social systems. |
The course focuses on four different application areas, (I) opinion dynamics, (II) cooperation and competition, (III) spatial interaction, and (IV) online social networks. Emphasis is on formal modelling, quantitative analysis and computer simulation tools.
|Objective||A successful participant of this course is able to|
* understand the rationale of actor-centered models of social systems
* choose appropriate model classes to characterise social systems
* understand the relation between rules implemented at the individual level and the emerging behaviour at the global level
* grasp the influence of agent heterogeneity on the model output
* efficiently implement agent-based models using Python and visualise the output data
|Content||Agent-based modelling provides a bottom-up approach to understand the complex dynamics of social systems. Agents have internal degrees of freedom (opinions, strategies), the ability to perceive, and to change, their environment, and to interact with other agents. Their (inter)actions result in collective dynamics with emergent properties that need to be analysed and understood quantitatively. As more, and more accurate, data about online and offline social systems become available, our formal understanding of these systems has to progress in the same manner. We focus on a parsimonious description of the agents' behaviour which relates individual interaction rules to the dynamics on the system's level and complements engineering and machine learning approaches to modelling. |
The course focuses on four different application areas of agent-based models, (I) opinion dynamics, (II) cooperation and competition, (III) spatial interaction, and (IV) online social networks.
Whilst the lectures focus on the theoretical foundations of agent-based modelling, they are illustrated on a more practical level in weekly exercise classes. Using the Python programming language, the participants implement agent-based models in guided and autonomous projects, which they present and jointly discuss.
|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.|
|Prerequisites / Notice||Participants of the course should have some background in mathematics and a dedicated interest in formal modelling and computer simulations, and should be motivated to learn about social systems from a quantitative perspective.|
Self-study tasks are provided as home work for small teams (3-5 members). Weekly exercises (45 min) are used to discuss the solutions, and guide the student. During the second half of the semester, teams have to complete a course project in which they will implement and discuss an agent-based model to characterise a system chosen jointly with the course organisers. This project will be evaluated, and its grade will count as 25% of the final grade.