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Complexity and Emergence in Economic and Social Systems

Data-Driven (AI) Agent-Based Models for Economics

Advanced Methods for Building and Calibrating Agent-Based Economic Models.

17.5h (5 days)

€1,399

Face-to-face

English

Program date: July 6-10, 2026

Early bird deadline: April 15, 2026

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Complexity and Emergence in Economic and Social Systems

Data-Driven (AI) Agent-Based Models for Economics

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Who is this Course for?

Complexity and Emergence in Economic and Social Systems

Data-Driven (AI) Agent-Based Models for Economics

Course overview

This advanced course introduces data-driven agent-based modeling (ABM) as a powerful framework for understanding complex economic systems. Moving beyond traditional toy models, we explore how modern ABMs integrate real-world data through calibration, initialization, and data assimilation techniques. The course covers state-of-the-art applications in labor markets, macroeconomics, and pandemic economics, and introduces multi-agent systems tools such as generative AI to model complex agent behavior.

Students will gain hands-on experience implementing data-driven ABMs using contemporary Python tools and libraries.

The learning objectives of the course include:

Understand the theoretical foundations and advantages of ABMs as a new framework for economic analysis.
Master advanced calibration, initialization, and data assimilation techniques for data-driven ABMs.
Implement and analyze contemporary ABM applications in labor markets, macroeconomics, and crisis scenarios.
Apply multi-agent systems tools such as generative AI to model complex agent behavior.

Faculty

Who is this course for?

This course has been designed for:

Graduate students, early-career researchers, and advanced undergraduates in economics, sociology, political science, and applied mathematics.

Learning outcomes

By the end of the course, students will be able to:

Construct data-driven ABMs that incorporate real-world empirical data
Calibrate and validate ABMs using advanced statistical and machine learning techniques
Implement use cases of ABMs for labor market dynamics, macroeconomic analysis, and crisis forecasting
Integrate AI methods into agent behavioral modeling
Conduct policy experiments using empirically-grounded models

Course Philosophy

This course emphasizes practical implementation of research-grade agent-based models that can inform real-world policy decisions. Rather than focusing on pedagogical toy models, we work with contemporary applications that demonstrate the power of ABMs to capture heterogeneity, network effects, and emergent phenomena in economic systems.

Key topics for Data-Driven (AI) Agent-Based Models for Economics course

This course will cover the following topics:

Day 1 - Foundations of Data-Driven Agent-Based Modeling

Morning (Theory)

Introduction to ABMs: Why we need a new framework
Limitations of representative-agent and equilibrium models
What makes an ABM ‘data-driven’: connecting micro-level data to macro outcomes
Heterogeneous agents, networks, and emergence in economic systems

Afternoon (Practicals)

Setting up Python environment for ABM development
Building a simple data-driven ABM from empirical data
Visualization and analysis of emergent patterns

Day 2 - Methods for Data-Driven ABMs

Morning (Theory)

Calibration techniques for ABMs
Agent initialization from real-world data
Data assimilation and model updating
Validation strategies and empirical benchmarking

Afternoon (Practicals)

Implementing calibration algorithms
Working with microdata to create agent populations
Model validation exercises

Day 3 - Applications: Labor Markets and Macroeconomic Models

Morning (Theory)

Labor market ABMs: search, matching, and occupational mobility
Modeling automation and technological change
Macroeconomic ABMs: production networks and supply chains
Case study: Economic impacts during the COVID-19 pandemic
Forecasting with ABMs

Afternoon (Practicals)

Implementing a labor market ABM
Building a production network model
Analyzing economic shocks and policy interventions

Day 4 - Multi-Agent Systems and AI Methods

Morning (Theory)

Introduction to multi-agent systems
Learning and adaptation in agent populations
Generative AI for agent behavior
From fixed rules to adaptive agents

Afternoon (Practicals)

Implementing learning algorithms in ABMs
Experimenting with AI-based agents
Comparing rule-based vs. learning approaches

Day 5 - Advanced Applications and Future Directions

Morning (Theory)

Integrating AI methods into ABMs
Hybrid approaches: combining data-driven rules with learning
Challenges: computational complexity, interpretability, validation
Frontiers: digital twins, real-time forecasting, policy design

Afternoon (Practicals)

Building an ABM with adaptive agents
Student project presentations
Course synthesis and discussion

List of References

Here is a list of texts that may help you to prepare for this course.

Books and Articles

Dawid, H., & Delli Gatti, D. (2018). “Agent-based macroeconomics.” Handbook of Computational Economics, Vol. 4
Del Rio Chanona, R. M., et al. (2020). “Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective.” Oxford Review of Economic Policy
Del Rio Chanona, R. M., et al. (2021). “Occupational mobility and automation: A data-driven network model.” Journal of the Royal Society Interface
Delli Gatti, D., Fagiolo, G., Gallegati, M., Richiardi, M., Russo, A. “Agent-based models in economics: A toolkit.”
Farmer, J. D., & Foley, D. (2009). “The economy needs agent-based modelling.” Nature
Pangallo, M., & Del Rio Chanona, R. M. “Data-Driven Agent-Based Models.” In Handbook of Agent-Based Models
Pangallo, M., & Del Rio Chanona, R. M. “Generative AI agents in economic agent-based models.” Working paper
Pangallo, M., et al. (2022). “The unequal effects of the health–economy trade-off during the COVID-19 pandemic.” Nature Human Behaviour
Pichler, A., Farmer, J. D., et al. (2020). “Production networks and epidemic spreading: How to restart the UK economy?”
Poledna, S., et al. (2023). “Economic forecasting with an agent-based model.” European Economic Review
Poledna, S., et al. (2024). “Deep reinforcement learning for economic policy in agent-based models.”
Wiese, S., Kaszowska-Mojsa, J., Dyer, J., Moran, J., et al. “Forecasting macroeconomic dynamics using a calibrated data-driven agent-based model.”
Wilensky, U., & Rand, W. (2015). An Introduction to Agent-Based Modeling. MIT Press

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers

2

Study in vibrant Barcelona

3

Learn from world-renowned faculty

Admissions and Requirements

All BSE Summer School applicants must meet the entrance requirements.

Program date: July 6-10, 2026

Early bird deadline: April 15, 2026

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.)
Postgraduate degree or current Master’s/PhD studies related to the course topic
Relevant professional experience

Requirements for Data-Driven (AI) Agent-Based Models for Economics course

Participants are expected to have prior programming experience (Python preferred), along with a solid understanding of basic probability and statistics, economic modeling, and fundamental machine learning concepts

Schedule

Here is your schedule for this edition of the BSE Data-Driven (AI) Agent-Based Models for Economics course.

Time

6

mon

7

tue

8

wed

9

thu

10

fri

09:00-11:00

Lecture

14:30-16:00

Practical

Credit Transfers (ECTS)

To be eligible for credit transfer, students must complete a final project.

Students will deliver a short final project one week after the summer school finishes. It will consist in solving a final problem that will include the practical and empirical issues worked on in class.

Consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Fees

Multiple course discounts are available; see more information about available discounts. Fees for other courses listed in other Summer School programs may vary.

Course

Agent-Based Modeling for Economics and the Social Sciences

Networks and Emergence in Complex Systems

Modality

Face-to-face

Total Hours

17.5

ECTS

1

Regular Fee

1,399€

Reduced Fee*

799€

* Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

FAQ

Here are some commonly asked questions by participants. Any further queries, please contact our Admissions Team.

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

Is accommodation included in the course fee?

Accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will NOT be recorded; however, the materials provided by the professor will be available for a month after the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts or request a personalized discount quote by email.

Can I take more than one course?

Yes! you can combine any of the Summer School courses (schedule permitting). See the full course calendar.

Cancelation and Refund Policy

Please consult BSE Summer School policies for more information.

Are there any evening activities during the course?

Yes, a social dinner is held once a week for all participants, it is free to attend.

Contact our Admissions Team

Mix and match your summer courses!

Remember that you can combine this program with courses in any of the other BSE Summer School programs (schedule permitting). Maximise your learning this summer and take advantage of our multiple-course discount.

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