Machine-Learning Perspectives of Agent-Based Models

Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia
2025
ISBN: 978-3-031-73353-6
Verlag: Springer

Buch, Englisch, 400 Seiten, Format (B × H): 155 mm x 235 mm

This book provides an overview of agent-based modeling (ABM) and multi-agent systems (MAS), emphasizing their significance in understanding complex economic systems, with a special focus on machine learning algorithms that allow agents to learn. ABM is highlighted as a powerful tool for studying economics, especially in the context of financial crises and pandemics, where traditional models, such as dynamic stochastic general equilibrium (DSGE) models, have proven inadequate.

Autoren/Hrsg.

Campos, Pedro

Rao, Anand

Joaquim, Margarido

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Wirtschaftswissenschaften Wirtschaftswissenschaften Wirtschaftswissenschaften: Allgemeines

Inhaltsverzeichnis

Agent-Based Models and the Economics of Crisis.- The Machine Learning perspective.- Setting up Agent-Based Models of Crisis (Microeconomic Model of Crisis; Virus on a Network Spread Model).- Developing models with Python and R.

Über Autor(innen)

Anand Rao is a Distinguished Services Professor of Applied Data Science and AI in the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He received his PhD from the University of Sydney (with a University Postgraduate Research Award-UPRA) in 1988 and an MBA (with Award of Distinction) from Melbourne Business School in 1997. He boasts a 35-year career spanning AI, data, and analytics, serving as PwC's Global AI Leader. His research focuses on operationalizing AI, responsible AI, and agent-based models. Recognized globally, he has received accolades such as the Most Influential Paper Award and distinctions in AI and InsureTech. Prior to joining management consulting, he was the Chief Research Scientist at the Australian Artificial Intelligence Institute, where he built agent-based models and simulation systems and conducted research in the theory and practice of multi-agent systems.

Pedro Campos, holding a PhD in Business Sciences (2008), with a thesis on Agent-Based Models in Collaborative Networks for R&D, Pedro has a background in Mathematics and Statistics, and is Associate Professor of the School of Economics and Management, University of Porto. He conducts his research at LIAAD, the Artificial Intelligence and Decision Support Laboratory of INESC TEC. He currently serves as the Director of Methodology Services at Statistics Portugal. He specializes in Statistics, Data Science, Network Mining, and Marketing Research. Some of his research contributions delve into Innovation and Employment, Collaborative Networks, and Data Visualization. He has more than 50 publications, including articles in specialized journals and book chapters, and has edited 3 books. Pedro is also Deputy Director of the ISLP (International Statistical Literacy Project).

Joaquim Margarido, an ISEP (Superior Institute of Engineering of Porto) graduate, holds a master's degree in multi-agent systems. With expertise in IT, he imparts knowledge in programming using Java, Python, C#, SQL, and web technologies. Dedicated to practical solutions, Joaquim has developed software for various companies, addressing common challenges. His commitment to innovative software solutions reflects his extensive training and proficiency in diverse programming languages, contributing to both education and industry.