Machine Learning Perspectives of Agent-Based Models: Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia - Image 1

£105.76

Springer Machine Learning Perspectives of Agent-Based Models: Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia

Name: Machine Learning Perspectives of Agent-Based Models: Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia
Brand: Springer
Price: 105.76 GBP
Availability: InStock

Managers' Guides to Computing

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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.

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 3031733533
Category: Books > Subjects > Business, Finance & Law > E-Commerce > Managers' Guides to Computing
Domain: Amazon UK
Release Date: 19 August 2025
Listed Since: 21 August 2024

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Springer Machine Learning Perspectives of Agent-Based Models: Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia

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