AGENT-BASED MODELLING

This course offers an introduction to agent-based modeling (ABM) as a computational methodology for studying complex economic and financial systems. Through a combination of theoretical discussions, practical exercises, and project work, students will explore the principles, techniques, and applications of ABM in economics and finance. Emphasis will be placed on understanding agent behavior, interactions, emergence of macroeconomic phenomena, and the practical implementation of ABM simulations to address real-world economic and financial issues. In particular, ABMs are able to reproduce surprising stylized facts observed in financial markets (e.g., fat-tailed return distribution, autocorrelation of returns) and economic phenomena like Income Distribution and Wealth Inequality, Innovation Diffusion and Technological Change and Financial Instability and Systemic Risk.

On successful completion of the module, a student will be able to:
- recognize situations in which agent-based models are useful to gain insights about social and economic complex systems.
- conceptualize and develop simple agent-based models to study problems in economics, finance and social sciences.
- know the basic theoretical concepts related to the study of large-scale complex networks.
- characterize and explore the behaviour of social complex systems featuring emerging phenomena and self-organization of many interacting entities.

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 module comprises two parallel strands—a theoretical strand and a programming strand—delivered according to a “learning by doing” philosophy.

In the theoretical strand, we will first introduce the general features of agent-based models and then examine selected models from the literature, discussing their aptitude for capturing complex economic, financial, and social phenomena. Among the models we shall cover are:

1. Schelling’s segregation model
2. The “El Farol” dilemma (coordination and decision-making models)
3. The Santa Fe Artificial Stock Market
4. Econophysics models of wealth exchange and distribution
5. Macroeconomic agent-based models

These case studies will allow us to explore concepts such as inductive reasoning, emergent (bottom-up) phenomena, and the self-organization of complex systems.

In the practical strand, we will demonstrate how to build agent-based models using the NetLogo platform. You will learn the fundamentals of NetLogo programming and then progress to more elaborate interaction structures—such as network topologies and other mechanisms linking agents.

Please note that course topics may be subject to minor modifications in response to the curricular background and interests of enrolled students.

Original articles, handouts, and notes.

The student will:
- extend a model presented during the module. This include some coding.
- Analyze the results of the simulations in a short presentaton, in written form.
- Discuss the presentation and the theory of the course in an oral exam.
A simple extension of the model, along with demonstrating an understanding of the fundamental concepts of the course, will enable one to pass the exam with a satisfactory grade (18-23). More complex modifications, involving a literature review phase on the chosen model, along with demonstrating critical thinking in presenting the fundamental concepts of the course, will lead to a higher grade (27-30).

written and oral

A straightforward extension of the model, together with clear evidence of mastery of the course’s fundamental concepts, will enable you to achieve a passing grade (18 – 23). More elaborate modifications—entailing a focused literature review of the chosen model and a demonstration of critical insight in presenting the course’s core ideas—will permit you to attain a higher grade (24 – 27). Finally, the development of new models from scratch, or elaborations that contribute conceptual innovations and novel software implementations, can earn you the maximum mark (30 cum laude).

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