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.
The allure of computer simulation lies in its versatility and its capacity to unlock understanding of complex phenomena. Imagine being able to test the crashworthiness of a new car design dozens of times without ever building a physical prototype, or exploring the potential impacts of a new economic policy before implementing it in the real world. These are the kinds of possibilities that computer simulation opens up. The field is also inherently interdisciplinary, often requiring a blend of mathematical prowess, programming skills, and deep knowledge of the specific domain being modeled, making it an exciting area for those who enjoy tackling multifaceted challenges.
What is Computer Simulation?
At its core, computer simulation is the process of using a computer to execute a model of a particular system or phenomenon. This model is an abstraction, a simplified representation of reality, designed to capture the essential characteristics and behaviors of the system being studied. The computer then performs calculations based on this model, stepping through time or a sequence of events, to generate data that represents how the system evolves. This allows for the exploration of "what-if" scenarios, the testing of hypotheses, and the prediction of future behavior.
Definition and Scope of Computer Simulation
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Reading list
We've selected 24 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.
Provides a comprehensive overview of simulation modeling and analysis. It covers topics such as simulation languages, statistical analysis, and optimization. It is suitable for both beginners and advanced users.
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.
Provides a comprehensive overview of computer simulation in science and engineering. It covers topics such as numerical methods, optimization techniques, and simulation applications. It is suitable for both beginners and advanced users.
Provides a comprehensive overview of advanced computer simulation approaches for studying complex systems. It covers topics such as multi-scale modeling, agent-based modeling, and machine learning. It is suitable for both beginners and advanced users.
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 comprehensive overview of mathematical modeling and simulation. It covers topics such as differential equations, numerical methods, and simulation techniques. It is suitable for both beginners and advanced users.
Offers a comprehensive overview of modeling and simulation fundamentals, covering theoretical underpinnings and applications in various practical domains. It provides a solid introduction to the core concepts and techniques used in simulation studies across different fields.
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.
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 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.
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.
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.
Focusing on molecular simulation, this book explains the algorithms and underlying physics used in simulating molecular systems. It is highly relevant for those in computational physics, chemistry, and materials science. It provides a deep dive into the techniques used to simulate the behavior of molecules and materials, offering valuable insights for researchers and advanced students.
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 a comprehensive overview of numerical simulation techniques used in molecular dynamics. It covers topics such as Monte Carlo methods, molecular dynamics simulations, and free energy calculations. It is suitable for both beginners and advanced users.
Provides a comprehensive overview of simulation using Arena software. It covers topics such as model building, simulation techniques, and data analysis. It is suitable for both beginners and advanced users.
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.
Provides a deep mathematical and theoretical analysis of stochastic simulation algorithms. It is suitable for graduate students and researchers with a strong background in probability and statistics. It delves into the theoretical properties of simulation methods and the analysis of their output.
While not solely focused on simulation, this classic volume delves into seminumerical algorithms, including random number generation, which fundamental component of many simulation techniques. It provides a deep theoretical understanding of the algorithms used in computation. It foundational text for anyone serious about the mathematical and algorithmic underpinnings of computer science, including simulation.
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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