Aims and Objectives

Agent-based models (ABM) are a powerful and increasingly essential tool in economics and across other social sciences for studying complex systems and social challenges. This summer school addresses the critical need for researchers to master not only the conceptual underpinnings of ABM but also the practical challenges of programming, adhering robust protocols and effectively communicating model design and results.

This intensive, full-time course equips participants with the advanced theoretical understanding and skills to design, implement, validate and analyze robust agent-based models.

The course will provide attendees with the technical and methodological skills required to successfully develop and analyse simulation models in a research project, such as a PhD thesis, a high-quality academic paper, or a policy report.

What you will learn

Upon completion, participants will be able to:

Understand the foundational principles and theoretical frontiers of ABMs, including their strengths and limitations in social science research.

Design and implement ABMs from scratch in Laboratory for Simulation and Development (LSD) and R.

Analyze model output rigorously and apply Machine Learning methods for advanced sensitivity analysis with R.

Apply these skills to develop ABMs in other leading platforms/languages such as Python (Mesa) and NetLogo.

Critically evaluate ABM methodology and apply it to their own research questions.

Conceptualize and prototype an original ABM project, laying a strong foundation for future development and publication.

Course Structure

The course combines expert-led lectures, hands-on coding exercises under the close supervision of dedicated teaching staff, interactive group discussions, and seminars showcasing advanced ABM applications by leading researchers. Participants will work on individual and group research projects throughout the week, culminating in presentations of their model prototype on the final day. Crucially, dedicated time each afternoon is reserved for individual consultations with faculty offering personalized feedback on your research ideas and projects.

Day 1-3: Introduction to ABMs, hands-on modeling in LSD and R.
Day 4: Hands-on sensitivity analysis using machine learning approaches in R.
Day 5: Group model development and presentations.
Day 1-5: Seminars on cutting-edge ABM research, 1-on-1 consultations with faculty and facilitated group discussions

Who Should Apply?

This course is designed for:

Ambitious graduate and PhD students in economics and other social sciences with a keen interest in advancing their research using computaKonal methods.

Early-career researchers and experienced modellers looking to enhance their skills and explore new frontiers in ABMs.

We particularly encourage applications from individuals eager to integrate computational modelling into their social science toolkit. Prior experience in programming is helpful but not required. The course welcomes and supports participants with limited or no prior programming experience.

Faculty 
Our expert faculty includes (more to be added):

• Dr. Tommaso Ciarli (UNU-MERIT)
• Prof. Robin Cowan (UNU-MERIT)
• Prof Paola D’Orazio (Chemnitz University of Technology)
• Prof. André Lorentz (University of Strasbourg)
• Dr. Önder Nomaler (UNU-MERIT)
• Dr. Francesco Pasimeni (Eindhoven University of Technology)
• Prof. Andrea Roventini (Scuola Superiore Sant'Anna)
• Dr. Tania Treibich (Maastricht University)
• Prof. Marco Valente (University of L’Aquila)
• Dr. Ben Vermeulen (UNU-MERIT)
• Prof. Bart Verspagen (UNU-MERIT)