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Agent-Based Models with MATLAB
- 1st Edition - January 1, 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
As the world becomes more complicated and linked, many of our research questions can no longer be answered using straightforward models. Agent-Based Models with MATLAB intro… Read more
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Request a sales quoteAs the world becomes more complicated and linked, many of our research questions can no longer be answered using straightforward models. Agent-Based Models with MATLAB introduces one of the most important methodologies for complex systems modeling, called Agent-Based Modeling (ABM), using 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. Observing the implementation of a particular approach through code can be helpful for readers, even those with strong mathematical abilities, as it eliminates ambiguities and uncertainties, making the material easier to grasp and communicate. The book comes with a large amount of software code accompanying 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. For many readers, a more feasible goal is to grasp the concepts of these models through programming instead of mathematical formulations. Agent-Based Models with MATLAB enables computer scientists, mathematicians, researchers and engineers to apply ABM in a wide range of research and engineering applications. The book gradually advances from basic to more advanced methods, while reinforcing understanding of 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
1. Introduction
a. Concepts and definitions of complex systems
b. Principles of complex systems science
c. Main techniques applied to complex systems
d. What is a model?
i. Macroscopic models
ii. Microscopic models
2. Agents
a. Characteristics of agents
b. Agents as entities of complex systems
c. Agent’s behaviors
d. Collectivity and interaction between agents
3. Agent-based modeling
a. What is agent-based modeling?
b. Components of agent-based modeling
i. Agents
ii. Environments
iii. Interactions and rules
c. Limitations of agent-based modeling
4. Complex networks and graph theory
a. Fundamentals of networks and graph theory
i. Adjacency relation
ii. Associativity
iii. Degrees of correlation and distribution
iv. Size of networks, density, and percolation
b. Metric and topological structure of networks
i. Shortest path distance
ii. Centralities, Clustering, Diffusion
c. Dynamic networks
d. Watts-Strogatz model
5. Cellular Automata
a. Definition of cellular automata
b. Cellular Automata extensions
c. Cellular automaton analysis
6. Validation Framework
a. Accuracy of a model
b. Verification test
c. Validation macro validation vs. micro validation
d. Validation vs. empirical validation
e. Benefits of validation
f. Replication
7. Model applications
a. Game of life model
b. Heroes and cowards’ model
c. Simple economy model
d. Evacuation models with obstacles
e. Complex Networks Model
f. Segregation Model
g. Cyclist Decisions Model
h. Criminal Simulation Model
i. Diffusion model
j. Fire model
k. infectious disease transmission model
l. Influence and opinion change model
8. Differences between Cellular Automata and Agent-Based Models
a. Representation of the Systems
b. Dynamics and Interactions
c. Complexity and Heterogeneity
d. Emergent Properties
9. Implementations of agent-based models
a. Systems of urban security
b. Flood risk
c. Image processing applications
a. Concepts and definitions of complex systems
b. Principles of complex systems science
c. Main techniques applied to complex systems
d. What is a model?
i. Macroscopic models
ii. Microscopic models
2. Agents
a. Characteristics of agents
b. Agents as entities of complex systems
c. Agent’s behaviors
d. Collectivity and interaction between agents
3. Agent-based modeling
a. What is agent-based modeling?
b. Components of agent-based modeling
i. Agents
ii. Environments
iii. Interactions and rules
c. Limitations of agent-based modeling
4. Complex networks and graph theory
a. Fundamentals of networks and graph theory
i. Adjacency relation
ii. Associativity
iii. Degrees of correlation and distribution
iv. Size of networks, density, and percolation
b. Metric and topological structure of networks
i. Shortest path distance
ii. Centralities, Clustering, Diffusion
c. Dynamic networks
d. Watts-Strogatz model
5. Cellular Automata
a. Definition of cellular automata
b. Cellular Automata extensions
c. Cellular automaton analysis
6. Validation Framework
a. Accuracy of a model
b. Verification test
c. Validation macro validation vs. micro validation
d. Validation vs. empirical validation
e. Benefits of validation
f. Replication
7. Model applications
a. Game of life model
b. Heroes and cowards’ model
c. Simple economy model
d. Evacuation models with obstacles
e. Complex Networks Model
f. Segregation Model
g. Cyclist Decisions Model
h. Criminal Simulation Model
i. Diffusion model
j. Fire model
k. infectious disease transmission model
l. Influence and opinion change model
8. Differences between Cellular Automata and Agent-Based Models
a. Representation of the Systems
b. Dynamics and Interactions
c. Complexity and Heterogeneity
d. Emergent Properties
9. Implementations of agent-based models
a. Systems of urban security
b. Flood risk
c. Image processing applications
- No. of pages: 250
- Language: English
- Edition: 1
- Published: January 1, 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, Mexico