Machine Learning Perspectives of Agent-Based Models : With Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia （2025. x, 405 S. X, 405 p. 251 illus., 220 illus. in color. 235 mm）

Herausgegeben:Campos, Pedro/ Rao, Anand/ Margarido, Joaquim

SPRINGER, BERLIN; SPRINGER NATURE SWITZERLAND; SPRING（2025発売）

• 製本 Hardcover:ハードカバー版／ページ数 400 p.
• 言語 ENG
• 商品コード 9783031733536

Full Description

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.

Contents

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.