Agent-Based Computational Modeling

About

Deliberate actions can lead to unintended and even undesired consequences. For example, cities may exhibit ethnic segregation in their neighborhoods, despite high levels of citizen tolerance. Polarization of opinions amongst political actors as well as citizens can occur even when individuals do not actively promote differences. Online social bots with numerous followers might be less successful in disseminating content than those with only a few followers. Grassroot social movements can arise spontaneously and gain immense political power although they are much less organized, centralized, and coherent than political parties and interest groups. Arising from complex interactions between individuals, these intriguing collective phenomena are emergent and often go unnoticed by individuals. Explaining them is an intriguing scientific challenge. This seminar aims to familiarize participants with agent-based modeling, a rigorous methodology for investigating emergent phenomena. Participants will delve into the principles of complexity science, a multidisciplinary field exploring similar phenomena across physics, computer science, and biology. The focus will be on seminal agent-based models from the social sciences, teaching students to implement models using NetLogo or Python, depending on their programming experience. Special emphasis will be placed on employing simulation methods to analyze agent-based models, identifying the underlying mechanisms driving emergence, and developing tests to validate the responsible mechanisms. Participants are given the opportunity to present their agent-based modeling projects and receive feedback.

Course leader

Michael Mäs, Karlsruhe Institute of Technology, Germany Fabio Sartori, Karlsruhe Institute of Technology, Germany.

Target group

- Participants with diverse disciplinary backgrounds are welcome to join but participation always requires a strong interest in social phenomena.
- Participants with programming experiences as well as starters are welcome.

Course aim

By the end of the course you will:

- Understand the core concepts of complexity science
- Know and understand emerging phenomena from various scientific disciplines
- Know seminal agent-based models of opinion polarization, social order, the dissemination of fake news, and the emergence of classes
- Be able to reflect on the strengths and weaknesses of agent-based modeling.
- Be able to develop agent-based models.
- Be able to reflect on the complementary advantages of toy models and “realistic” models (e.g. digital twins)
- Have practiced implementing and analyzing agent-based models either in NetLogo or Python
- Will have learned how to develop expectations about the mechanism generating emergent phenomena and how to challenge these expectations with agent-based models.

Fee info