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13.0 MB
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075BC8ED0A1D269FF5E9F3773BC83BF2E19E88AE
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Aug. 21, 2025, 3:27 p.m.
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(Last updated: Aug. 21, 2025, 3:34 p.m.)
| File | Size |
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| Campos P. Machine Learning Perspectives of Agent-Based Models...2025.pdf | 13.0 MB |
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13.0 MB
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2025-08-21
| Uploaded by andryold1 | Size 13.0 MB | Health [ 57 /22 ] | Added 2025-08-21 |
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SOURCE: Campos P. Machine Learning Perspectives of Agent-Based Models...2025
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MEDIAINFO
Textbook in PDF format 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 (AI), highlighting the advantages of each in modeling economic phenomena. Python is the main language in the book, due to its popularity, but R, NetLogo and Julia are also used. Python has become a very popular programming language in the last few years, especially in the area of data science and has been used in very important projects like Google, Quora, Youtube, Reddit and Dropbox, to name a few. Some packages exist in Python for Agent-Based Modeling, such as Janus for Land Use and Land Cover Change, and MESA, a modular framework for building, analyzing and visualizing the outputs. Furthermore, Python is also very suitable for implementing Machine Learning models and some people even argue that Python is the most popular language for all Data Scientists. Machine Learning (ML) has been primarily concerned about learning patterns from large amounts of data and predicting outcomes or classifying information. For example, early applications of ML included decision tree algorithms used in medical diagnosis. Agent-based models (ABM), on the other hand, have been primarily used to model and simulate the behavior of autonomous entities called agents and how they interact with each other. Agent-based models have been used extensively for capturing and analyzing emergent behavior. Machine Learning has grown significantly over the past three decades and has found a broad range of applications across several sectors like financial services, healthcare, media and entertainment, retail, manufacturing, agriculture, etc. Advances in Deep Learning, specifically around embeddings, attention mechanisms, and quantization have revolutionized the development of multi-modal foundation models. The number of ML algorithms and foundation models has expanded with literally hundreds or even thousands of distinct algorithms and hundreds of proprietary and open-source foundation models. The basis of ML algorithms has also evolved from/ Preface Introduction Part I Agent-Based Modelling and Machine Learning Agent-Based Modeling and Learning in Economics: An Overview Part II Agent-Based Models in the Context of COVID19 Epidemiology Modelling Agent-Based Behavioral Models: Modeling COVID19 Behavior COVID-19 Epidemiological, Behavioral, and Economic Model Part III Creating Agent-Based Models of Crisis in Python, and R MyWealth: A Simple Model of Economic Exchange in Python The Ultimatum Game as a Paradigm for Learning Agents: A Python Adventure Alternative Machine Learning Approaches for an Agent-Based Model of the Ultimatum Game Using R Part IV Case Studies: Agent-Based Learning and Crisis Using R, Netlogo, and Julia An Agent-Based Epidemic Modeling in Julia Portfolio Management and Crises: A Multi-Armed Bandit Approach Organizational Learning from Crises with Machine Learning and Agent-Based Models Strategic Alliances in NetLogo: A Flocking Algorithm with Reinforcement Learning Exploring the Efficiency vs. Fairness Behavioural Spectrum in Multi-Agent Deep Reinforcement Learning Resilient Agent-Based Networks in the Automotive Industry
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