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Machine learning perspectives of agent-based modelspractical applications to economic crises and pandemics with Python, R, Netlogo and Julia /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning perspectives of agent-based modelsedited by Pedro Campos, Anand Rao, Joaquim Margarido.
Reminder of title:	practical applications to economic crises and pandemics with Python, R, Netlogo and Julia /
other author:	Campos, Pedro.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xx, 377 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Multiagent systems.
Online resource:	https://doi.org/10.1007/978-3-031-73354-3
ISBN:	9783031733543$q(electronic bk.)

Machine learning perspectives of agent-based modelspractical applications to economic crises and pandemics with Python, R, Netlogo and Julia /
Machine learning perspectives of agent-based modelspractical applications to economic crises and pandemics with Python, R, Netlogo and Julia /[electronic resource] :edited by Pedro Campos, Anand Rao, Joaquim Margarido. - Cham :Springer Nature Switzerland :2025. - xx, 377 p. :ill., digital ;24 cm.

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.

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 the emerging properties of heterogeneous agents that cannot be deduced from the characteristics of individual agents. 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. Containing numerous practical examples and applications with R, Python, Julia and Netlogo, the book explores how learning, particularly machine learning, can be integrated into multi-agent systems to enhance the adaptation and behavior of agents in dynamic environments. It compares different learning approaches, including game theory and artificial intelligence, highlighting the advantages of each in modeling economic phenomena.

ISBN: 9783031733543$q(electronic bk.)

Standard No.: 10.1007/978-3-031-73354-3doiSubjects--Topical Terms:

484868
Multiagent systems.

LC Class. No.: QA76.76.I58

Dewey Class. No.: 006.30285436

Machine learning perspectives of agent-based modelspractical applications to economic crises and pandemics with Python, R, Netlogo and Julia /
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