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 main parts a theoretical one and an implementation one.
In the theoretical part, we will first introduce several fundamental Agent-Based Models and discuss how they shed light on interesting emerging phenomena in complex economic, financial, and social systems. Indicatively we will introduce the Shelling Segregation Model, the El Farol Problem (Minority game), the Santa Fe artificial stock exchange model, and macroeconomic models. These models will allow us to discuss topics such as inductive thinking and economic outcomes, emergent phenomena, and agents' self-organization. Furthermore, in many interesting situations in which agent-based models are useful, interactions among agents are constrained on social networks. For this reason, we will dedicate a few lectures to the study of graph topologies and their functional implications.
In the practical lectures, we will instead learn how to develop an agent-based model using NetLogo. The first lectures will introduce general topics of programming (basic elements of programming in NetLogo, and more complex network interaction structures), and we will then use them to build agent-based models.
The actual topics that will be discussed might change a bit depending on the students' knowledge and interests.

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).

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

English

written and oral

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