GESIS Training Courses
user_jsdisabled
Search

Scientific Coordination

Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Noemi Hartung

Agent-Based Computational Modeling

About
Location:
Mannheim, B6 4-5
 
Course duration:
9:00-16:00 CEST
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 550 €
Academics: 825 €
Commercial: 1650 €
 
Keywords
Additional links
Lecturer(s): Michael Mäs, Fabio Sartori

About the lecturer - Michael Mäs

About the lecturer - Fabio Sartori

Course description

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.
 
Organizational Structure of the Course
On Day 1, the seminar starts with an introduction to complexity science and examples of complex phenomena from fields as diverse as biology, physics, computer science, economics, mathematics, and, of course, the social sciences. Applying basic principles of complexity science, participants learn to implement the Sakoda-Schelling Segregation model in NetLogo.
On the remaining days, participants are introduced to a seminal model of a complex social phenomenon in the mornings. In this way, students get an overview over famous models from different social-scientific fields, and develop an understanding for the critical ingredients of social complexity and alternative ways to formally represent critical aspects of reality in a computer model.
In the afternoons, students implement and experiment with the respective model under the supervision of the lecturers. To this end, the seminar will be split into a lab group of starters working with NetLogo and a lab group of more experienced programmers who will work with Python. During the week, models of ethnic segregation, opinion polarization, the dissemination of fake news and social bots, and the emergence of social classes will be covered.


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.


Learning objectives

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.


Prerequisites

  • Strong interest in social phenomena
  • Motivation to expose oneself to mathematical methods.
 
Software and Hardware Requirements
You should bring your own laptop for use in the course. Participants who want to work with Python should have it installed on their own machines. We will inform these participants that they need to install Igraph. NetLogo can be installed in a few moments and does not require any planning in advance.


Schedule

Recommended readings