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Agent-Based Models with MATLAB
- 1st Edition - January 20, 2025
- Authors: Erik Cuevas, Karla Avila, Miguel Islas Toski, Héctor Escobar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 4 0 0 4 - 1
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 4 0 0 5 - 8
Agent-Based Models with MATLAB introduces Agent-Based Modeling (ABM), one of the most important methodologies for complex systems modeling. The book explores computati… Read more
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Request a sales quoteAgent-Based Models with MATLAB introduces Agent-Based Modeling (ABM), one of the most important methodologies for complex systems modeling. The book explores computational implementations and accompanying MATLAB software code as a means of inspiring readers to apply agent-based models to solve a diverse range of problems. It comes with a large amount of software code that accompanies the main text, and the modeling systems described in the book are implemented using MATLAB as the programming language. Despite the heavy mathematical components of Agent-Based Models and complex systems, it is possible to utilize these models without in-depth understanding of their mathematical fundamentals.
This book enables computer scientists, mathematicians, researchers, and engineers to apply ABM in a wide range of research and engineering applications. It gradually advances from basic to more advanced methods while reinforcing complex systems through practical, hands-on applications of various computational models.
- Considers the most crucial methods and models of Agent-Based Models and complex systems
- Demonstrates how to use computational implementations in applying Agent-Based Models to solve a diverse range of scientific research and engineering problems
- Provides a wide range of hands-on implementation examples, along with MATLAB software code readers can experiment with and modify
Researchers in computational modelling, applied mathematicians, and computer scientists working with researchers in a wide range of scientific and engineering disciplines. Other interested audiences will be comprised of researchers, developers, and graduate students in computer science, mathematics, and interdisciplinary research fields interested in Agent-based modelling of complex systems
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- 1. Introduction
- 1 Introduction
- 2 Concepts and definitions of complex systems
- 3 Principles of complex systems science
- 3.1 Main techniques applied to complex systems
- 3.1.1 Game theory
- 3.1.2 Nonlinear dynamics and chaotic systems (nonlinear dynamics)
- 3.1.3 Systems theory
- 3.1.4 Modeling based on collective behavior and agents
- 3.1.5 Complex networks and graph theory (networks)
- 3.1.6 Evolution and adaptation
- 3.1.7 Pattern formation
- 3.2 What is a model?
- 4 Macroscopic models
- 4.1 Emergent behavior
- 4.2 Differential equations and rate laws
- 4.3 Conservation laws
- 4.4 Boundary conditions
- 4.5 Simplicity and calculation
- 4.6 Validity and limitations
- 4.7 Real-world applications
- 4.8 Interaction between macroscopic and microscopic models
- 4.9 Challenges of macroscopic modeling
- 4.10 The future of macroscopic models
- 4.11 Macroscopic model: Traffic flow on a freeway
- 4.12 Lighthill–Whitham–Richards model (LWR)
- 4.13 Greenshields model
- 4.14 Applications and implications
- 5 Microscopic models
- 5.1 Individual behavior and interactions
- 5.2 Computational simulation
- 5.3 Emerging phenomena
- 5.4 Realism and precision
- 5.5 Data-driven modeling
- 5.6 Validation of microscopic models
- 5.7 Interdisciplinary applications
- 5.8 Challenges and computational requirements
- 5.9 Hybrid modeling
- 5.10 The future of microscopic models
- 5.10.1 Microscopic model: Molecular dynamics simulation
- 5.10.2 Atoms and particles
- 5.10.3 Interatomic potentials
- 5.10.4 Newton's laws of motion
- 5.10.5 The molecular dynamics algorithm
- 5.10.6 Interdisciplinary applications
- 2. Agents
- 1 Agents as entities of a complex system
- 2 Interactive agents and ecosystems
- 3 Characteristics of the agents
- 4 Agent behavior
- 5 Collectivity and interaction between agents
- 3. Agent-based modeling
- 1 What is agent-based modeling?
- 2 Components of agent-based modeling
- 3 Agents
- 3.1 Characteristics
- 3.2 Shares
- 4 Ecosystems
- 5 Interactions and rules
- 6 Evaluation of agent-based modeling
- 4. Complex networks and graph theory
- 1 Fundamentals of networks and graph theory
- 1.1 Adjacency relation
- 1.2 Node's properties
- 1.3 Degrees of correlation and distribution
- 2 Metric and topological structure of networks
- 2.1 Shortest path distance
- 2.2 Centralities, clustering, and diffusion
- 3 Dynamic networks
- 4 An implementation of complex network in artificial intelligence
- 4.1 A metaheuristic approach
- 4.2 Computational implementation
- 4.3 Numerical and statistical validation
- 5. Cellular automatons
- 1 Definition of cellular automaton
- 2 Extensions of cellular automata
- 3 Analysis of cellular automata
- 3.1 Rule space and phase space sizes
- 3.2 Phase space visualization
- 3.3 Middle field approach
- 3.4 Renormalization group analysis to predict percolation thresholds
- 6. Validation framework
- 1 Verification
- 2 Validation
- 3 Micro- and macrovalidation
- 4 Validation based on the level of detail
- 4.1 Statistical validation
- 4.1.1 T-test
- 4.1.2 Kolmogorov–Smirnov test
- 4.1.3 Wilcoxon Test
- 5 Replication
- 6 Successful replication
- 7 Challenges and advantages of replication
- 7. Basic models
- 1 Introduction
- 2 Preliminary concepts of agent-based models
- 2.1 Model dimension and states
- 2.2 Neighborhood
- 2.3 Evolution of the agent-based model and rules
- 2.4 Boundary conditions and start configuration
- 3 Game of life
- 3.1 Implementation of the game of life
- 3.1.1 Setup grid and parameters
- 3.1.2 Visualization setup
- 3.1.3 Main simulation loop
- 3.1.4 Creating a new grid for the next generation
- 3.1.5 Cell update loop
- 3.1.6 Counting live neighbors
- 3.1.7 Applying game of life rules
- 3.1.8 Updating the grid for the next generation
- 3.1.9 End of the loop
- 4 The fire model
- 4.1 Model description
- 4.2 Implementation steps
- 4.3 MATLAB code
- 5 Heroes and Cowards model
- 5.1 Model description
- 5.1.1 Stage 1: Cowards
- 5.1.2 Stage 2: Heroes
- 5.2 MATLAB code
- 6 Simple economy
- 6.1 Model description
- 6.2 Implementation in MATLAB
- 7 Segregation model
- 7.1 Model description
- 7.2 Implementation in MATLAB
- 8 Diffusion
- 8.1 Model description
- 8.2 Implementation
- 8.2.1 One-dimensional diffusion
- 8.2.2 Two-dimensional diffusion
- 9 Predator-prey model
- 9.1 Interaction phase
- 9.2 Development phase
- 9.3 Formulation of the model
- 9.4 Implementation
- 10 Complex networks
- 11 Agent-based models and complex networks
- 11.1 MATLAB tools
- 11.2 Zachary's karate club
- 11.3 Voter model
- 11.4 Epidemic model
- 11.5 Diffusion model
- 8. Differences between cellular automata and agent-based model
- 1 Differences between cellular automata and agent-based models
- 2 Representation of the systems
- 3 Dynamics and interactions
- 4 Complexity and heterogeneity
- 5 Emerging properties
- 9. Implementations of agent-based models
- 1 ABM for public security strategies
- 1.1 Agent-based modeling
- 1.2 The proposed agent-based model
- 1.2.1 Initialization
- 1.2.2 Behavioral rules
- 1.3 Experimental results
- 2 Heroes and cowards optimization approach
- 2.1 Heroes and cowards
- 2.2 Metaheuristic approach based on heroes and cowards
- 2.3 Experimental results
- 3 Agent-based model for image segmentation
- 3.1 Otsu criterion in image segmentation
- 3.2 ABM as image segmentation approach
- 3.3 Experiments and results
- 4 ABM for COVID-19 transmission risk
- 4.1 Agent-based model to evaluate the COVID-19 transmission risk
- 4.2 Experimental results
- Index
- No. of pages: 336
- Language: English
- Edition: 1
- Published: January 20, 2025
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780443240041
- eBook ISBN: 9780443240058
EC
Erik Cuevas
Dr. Erik Cuevas received his Ph.D. degree from Freie Universität Berlin, Germany in 2006. Since 2006 he has been with the University of Guadalajara, where he is currently a full-time Professor in the Department of Computer Science. Since 2008, he has been a member of the Mexican National Research System (SNI III). He is the author of several books, including Metaheuristic Computation with MATLAB, Taylor and Francis/CRC Press; Recent Metaheuristic Computation Schemes in Engineering, Springer; Recent Metaheuristic Algorithms for Parameter Identification, Springer; and Metaheuristic Computation: A Performance Perspective, Springer, among others. His current research interests include meta-heuristics, computer vision, and mathematical methods. He serves as an Editor for the journals Expert System with Applications, ISA Transactions, Applied Soft Computing, Applied Mathematical Modeling, and Mathematics and Computers in Simulation.
Affiliations and expertise
Professor, Department of Computer Science, University of Guadalajara, MexicoKA
Karla Avila
Karla Avila received her M.Sc. degree in Electronics and Computer Engineering from University of Guadalajara in 2020. She is currently enrolled in the Ph.D. program in Electronics and Computer Science at University of Guadalajara, and her current research interests include artificial intelligence, modeling complex systems, and metaheuristic algorithms.
Affiliations and expertise
Research Associate, Department of Computer Science, University of Guadalajara, MexicoMT
Miguel Islas Toski
Miguel Islas Toski received his M.Sc. in Electronics and Computer Engineering from University of Guadalajara in 2020. He completed an internship at Arizona State University in 2017. He is currently enrolled in the Ph.D. program in Electronics and Computer Science at University of Guadalajara, and his current research interests include artificial intelligence, evolutionary algorithms, agent-based modeling, and smart and sustainable cities.
Affiliations and expertise
Research Associate, Department of Computer Science, University of Guadalajara, MexicoHE
Héctor Escobar
Héctor Escobar received his M.Sc. degree in Electronics and Computer Engineering from the University of Guadalajara, Mexico, in 2021. He is currently enrolled in the Ph.D. program in Electronics and Computer Science at University of Guadalajara, and his current research interests include meta-heuristics, computer vision, artificial intelligence, and Agent-Based Modeling.
Affiliations and expertise
Research Associate, Department of Computer Science, University of Guadalajara, MexicoRead Agent-Based Models with MATLAB on ScienceDirect