AGENT-BASED MODELLING

Why do some markets generate substantial inequality even when all individuals start from the same initial conditions? Why can small differences among households or firms produce large differences in aggregate macroeconomic outcomes? How do financial crises, speculative bubbles, urban segregation, or waves of technological innovation emerge?

Many of these phenomena arise from interactions among heterogeneous individuals and cannot be fully understood by assuming that all agents are identical or perfectly rational.

Agent-Based Models (ABMs) provide an alternative approach to the study of economic and social systems. Rather than starting from aggregate equations, ABMs construct artificial economies populated by households, firms, banks, investors, and consumers who interact according to simple behavioral rules. Through computational simulation, it becomes possible to observe how individual behavior and interaction structures generate complex collective outcomes.
This course introduces students to the theoretical foundations and practical applications of Agent-Based Modelling in economics and finance. Through lectures, hands-on exercises, and a modelling project, students will learn how to design, implement, and analyze simulations that connect microeconomic behavior to emergent macroeconomic outcomes.

Particular attention will be devoted to the study of economic inequality, financial markets, innovation, technological diffusion, collective behavior, systemic risk, and macroeconomic dynamics. The course will also demonstrate how ABMs can reproduce important empirical regularities observed in real-world data, including persistent volatility, fat-tailed distributions, and wealth concentration processes.

The objective is to provide students with theoretical, computational, and quantitative tools for understanding and analyzing economic phenomena characterized by heterogeneity, interaction, and complexity.

Upon successful completion of the course, students will be able to:

- identify situations in which agent-based modelling provides useful insights into complex economic, financial, and social systems;
- design and develop simple agent-based models to investigate economic, financial, and social phenomena;
- use computational simulation tools to analyze the relationship between individual behavior and aggregate outcomes;
- understand the role of interaction networks in the diffusion of information, innovation, behaviors, and economic shocks;
- interpret emergent phenomena such as inequality, financial instability, coordination, segregation, and technological change;
- critically evaluate the strengths and limitations of agent-based models relative to traditional economic approaches.

Elements of Mathematics, analysis and linear algebra (typically provided by university-level modules). Knowledge of Matlab and/or programming languages are useful but not necessary.

The course combines economic theory, computational simulation, and model development through a learning-by-doing approach.

The course is organized around a set of fundamental questions:

- Why can tolerant individuals generate highly segregated cities? (Schelling’s Segregation Model)
- How do coordination, imitation, and collective behavior emerge? (The El Farol Bar Problem and coordination models)
- Are financial markets always efficient? How do speculative bubbles and persistent volatility arise? (The Santa Fe Artificial Stock Market)
- Can markets generate inequality even when all individuals start from identical conditions? (Econophysics models of wealth exchange and distribution)
- Does heterogeneity among households and firms affect aggregate macroeconomic outcomes? (Agent-based macroeconomic models)
- How do social and economic networks shape the diffusion of innovation, information, and crises?

Alongside the theoretical component, students will learn how to build agent-based models using NetLogo. The course introduces the fundamentals of ABM programming and explores increasingly sophisticated interaction structures, including social networks, economic networks, and decentralized coordination mechanisms.

The course culminates in the development of an original project in which students extend an existing model or build a new one to investigate an economic or financial question of their choice.

Original articles, handouts, and notes.

Assessment is available through two alternative pathways.

- Pathway A consists of a written examination (60 minutes) covering the theoretical and applied material discussed during the course, followed by an oral discussion (approximately 15 minutes) aimed at assessing the student's understanding of the models, their assumptions, mechanisms, and economic implications.

- Pathway B consists of the development of an original extension of one of the models studied during the course, accompanied by a short written examination (approximately 30 minutes) on the fundamental concepts of the course and an oral defence (approximately 15 minutes) focused primarily on the project. Students choosing this pathway must submit a report and the corresponding source code at least five days before the examination date.

Students must communicate their chosen pathway to the instructor at least two weeks before the examination date.

written and oral

The instructor is responsible for ensuring the authenticity and originality of all examinations and coursework. In cases of suspected academic misconduct, an additional on-site assessment may be required during the exams, which may differ from the standard format.

18–23: satisfactory knowledge of the fundamental concepts of the course and ability to describe the main models studied, although with limited critical autonomy and depth of analysis.

24–27: good understanding of the models and theoretical concepts, with the ability to interpret their results, limitations, and economic implications, demonstrating an adequate degree of independent reasoning.

28–30: excellent command of the course material, including the ability to connect different models, critically discuss the literature, and analyze complex phenomena using the tools of Agent-Based Modelling.

30 cum laude: reserved for students choosing Pathway B who demonstrate an outstanding level of autonomy, originality, and rigor in the development of their project, combined with a particularly mature and insightful discussion of the theoretical and methodological aspects of the course.

Frontal lectures, model development tutorial sessions (actual format depending on the number of students).
Active participation is fundamental to fully achieve the learning outcomes.

This subject deals with topics related to the macro-area "Poverty and inequalities" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development