Machine learning perspectives of agent-based models : practical applications to economic crises and pandemics with Python, R, Netlogo and Julia / Pedro Campos, Anand Rao, Joaquim Margarido, editors

Mitwirkende(r):

9783031733543

Schlagwörter:

Andere physische Formen: 9783031733536 | 9783031733550 | 9783031733567 | Erscheint auch als: Machine learning perspectives of agent-based models. Druck-Ausgabe. Cham : Springer, 2025. xx, 377 SeitenDDC-Klassifikation:

519.5 23

DOI: DOI: 10.1007/978-3-031-73354-3Online-Ressourcen:

Zugang im Netz des KIT

Inhalte:

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.

Zusammenfassung: 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.PPN: PPN: 1934731439Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SMA | ZDB-2-SXMS

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