machine learning perspectives agent based (11 résultats)

Etat : New.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This bookprovides 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 ofheterogeneous 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 bookexplores 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.

Hardcover. Etat : Brand New. 400 pages. 9.25x6.10x9.21 inches. In Stock.

Etat : new. Questo è un articolo print on demand.

Hardcover. Etat : new. Hardcover. 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. mso-fareast-language: EN-US;">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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This bookprovides 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 ofheterogeneous 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 bookexplores 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. 377 pp. Englisch.

Hardcover. Etat : new. Hardcover. 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. mso-fareast-language: EN-US;">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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Hardcover. Etat : new. Hardcover. 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. mso-fareast-language: EN-US;">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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Etat : Neu. Machine Learning Perspectives of Agent-Based Models | Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia | Pedro Campos (u. a.) | Buch | xx | Englisch | 2025 | Springer | EAN 9783031733536 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 400 pp. Englisch.