May 1, 2024
Updated June 21, 2025
19 minute read
Harnessing the Virtual: An Introduction to Computer Simulation
Computer simulation is a powerful technique that employs computers to mimic the behavior of real-world or theoretical systems. Essentially, it involves creating a mathematical model, a set of equations and rules, that represents the system in question. This model is then translated into a computer program, and running this program allows us to observe the system's dynamics, predict outcomes, and gain insights that might be difficult, expensive, or impossible to obtain through direct experimentation. From forecasting weather patterns to designing complex machinery and understanding intricate biological processes, computer simulation has become an indispensable tool across a vast spectrum of human endeavor.
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Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Computer Simulation.
A well-regarded textbook focusing on discrete-event simulation, this book covers the fundamental concepts, modeling techniques, and statistical analysis necessary for simulating systems where changes occur at specific points in time. It is commonly used in undergraduate and graduate programs in engineering, computer science, and operations research. It provides a solid foundation for understanding how to model and analyze systems using discrete-event approaches.
Offers a broad introduction to simulation, covering various techniques including Monte Carlo simulation and discrete-event simulation. It provides a strong theoretical foundation with a focus on probability and statistics. It valuable resource for students and professionals seeking a comprehensive overview of simulation methods and their applications in diverse fields.
Focuses on the practical aspects of conducting a simulation study, covering the entire process from conceptual modeling to implementation and analysis. It valuable resource for practitioners and students who want to understand the workflow of a simulation project and best practices in model development and use. The third edition includes contemporary advancements such as hybrid simulation and digital twins.
Provides a detailed exploration of Monte Carlo methods and their application in simulation. It covers theoretical aspects and practical implementation, including techniques for variance reduction and rare-event estimation. It is suitable for those who want to deepen their understanding of Monte Carlo simulation and its use in various domains, particularly in areas involving stochastic processes.
Provides a practical introduction to agent-based modeling (ABM), a simulation paradigm where the behavior of individual agents and their interactions drive the system dynamics. It is suitable for researchers and students across various disciplines interested in modeling complex adaptive systems. It offers a hands-on approach to building and analyzing agent-based models.
Serves as an excellent introduction to agent-based modeling using the NetLogo platform. It is highly practical and suitable for beginners, guiding readers through building and exploring ABMs for various complex systems. It great resource for gaining hands-on experience with agent-based simulation.
Provides a practitioner-oriented approach to discrete-event modeling and simulation. It focuses on the practical aspects of building and implementing simulation models. It valuable resource for those who want to apply discrete-event simulation techniques in real-world settings.
This specialized book focuses on the application of Monte Carlo methods in financial engineering. It covers the theoretical underpinnings and practical implementation of simulation techniques for pricing financial derivatives and managing risk. It is highly relevant for graduate students and professionals in quantitative finance.
Presents a theoretical framework for modeling and simulation, particularly focusing on the Discrete Event System Specification (DEVS) formalism. It rigorous treatment of the subject, suitable for graduate students and researchers interested in the formal foundations of simulation. It provides a deep theoretical understanding of how to model complex systems.
Provides an accessible introduction to a variety of models, including simulation models, and emphasizes their utility in understanding complex systems. It focuses on the power of different modeling approaches to gain insights into various phenomena. It good starting point for those interested in the broader context of modeling and simulation as tools for problem-solving.
Focuses on computational physics and problem-solving using Python, including simulation techniques relevant to physics. It provides a practical approach to implementing simulations for physical systems using a popular programming language. It is suitable for students and researchers in physics and computational science.
While not exclusively about simulation, this book is crucial for understanding the numerical stability and accuracy of the algorithms used in computer simulations, particularly those involving numerical methods. It detailed and rigorous treatment of finite precision arithmetic and its effects on computations. It is essential for anyone developing or using simulations that rely heavily on numerical algorithms.
This handbook focuses on computational statistics using MATLAB, covering various simulation-based statistical methods. It practical guide for applying computational techniques, including simulation, to statistical problems. It is useful for students and practitioners who need to perform statistical analysis using simulation.
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