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Philosophical modeling is as old as philosophy itself; examples range from Plato's Cave and the Divided Line to Rawls's original position. What is new are the astounding computational resources now available for philosophical modeling. Although the computer cannot offer a substitute for philosophical research, it can offer an important new environment for philosophical research. The authors present a series of exploratory examples of computer modeling, using a range of computational techniques to illuminate a variety of questions in philosophy and philosophical logic. Topics include self-reference and paradox in fuzzy logics, varieties of epistemic chaos, fractal images of formal systems, and cellular automata models in game theory. Examples in the last category include models for the evolution of generosity, possible causes and cures for discrimination, and the formal undecidability of patterns of social and biological interaction. The cross-platform CD-ROM provided with the book contains a variety of working examples, in color and often operating dynamically, embedded in a text that parallels that of the book. Source code of all major programs is included to facilitate further research.
Employing a practical, "learn by doing" approach, this first-rate text fosters the development of the skills beyond the pure mathematics needed to set up and manipulate mathematical models. The author draws on a diversity of fields — including science, engineering, and operations research — to provide over 100 reality-based examples. Students learn from the examples by applying mathematical methods to formulate, analyze, and criticize models. Extensive documentation, consisting of over 150 references, supplements the models, encouraging further research on models of particular interest. The lively and accessible text requires only minimal scientific background. Designed for senior college or beginning graduate-level students, it assumes only elementary calculus and basic probability theory for the first part, and ordinary differential equations and continuous probability for the second section. All problems require students to study and create models, encouraging their active participation rather than a mechanical approach. Beyond the classroom, this volume will prove interesting and rewarding to anyone concerned with the development of mathematical models or the application of modeling to problem solving in a wide array of applications.
Modeling and Simulation of Computer Networks and Systems: Methodologies and Applications introduces you to a broad array of modeling and simulation issues related to computer networks and systems. It focuses on the theories, tools, applications and uses of modeling and simulation in order to effectively optimize networks. It describes methodologies for modeling and simulation of new generations of wireless and mobiles networks and cloud and grid computing systems. Drawing upon years of practical experience and using numerous examples and illustrative applications recognized experts in both academia and industry, discuss: - Important and emerging topics in computer networks and systems including but not limited to; modeling, simulation, analysis and security of wireless and mobiles networks especially as they relate to next generation wireless networks - Methodologies, strategies and tools, and strategies needed to build computer networks and systems modeling and simulation from the bottom up - Different network performance metrics including, mobility, congestion, quality of service, security and more... Modeling and Simulation of Computer Networks and Systems is a must have resource for network architects, engineers and researchers who want to gain insight into optimizing network performance through the use of modeling and simulation. - Discusses important and emerging topics in computer networks and Systems including but not limited to; modeling, simulation, analysis and security of wireless and mobiles networks especially as they relate to next generation wireless networks - Provides the necessary methodologies, strategies and tools needed to build computer networks and systems modeling and simulation from the bottom up - Includes comprehensive review and evaluation of simulation tools and methodologies and different network performance metrics including mobility, congestion, quality of service, security and more
Daniel Maki and Maynard Thompson provide a conceptual framework for the process of building and using mathematical models, illustrating the uses of mathematical and computer models in a variety of situations.
Computer Performance Modeling Handbook
This book briefly discusses the main provisions of the theory of modeling. It also describes in detail the methodology for constructing computer models of dynamic systems using the Wolfram visual modeling environment, SystemModeler, and provides illustrative examples of solving problems of mechanics and hydraulics. Intended for students and professionals in the field, the book also serves as a supplement to university courses in modeling and simulation of dynamic systems.
Written with computer scientists and engineers in mind, this book brings queueing theory decisively back to computer science.
Computer simulation was first pioneered as a scientific tool in meteorology and nuclear physics in the period following World War II, but it has grown rapidly to become indispensible in a wide variety of scientific disciplines, including astrophysics, high-energy physics, climate science, engineering, ecology, and economics. Digital computer simulation helps study phenomena of great complexity, but how much do we know about the limits and possibilities of this new scientific practice? How do simulations compare to traditional experiments? And are they reliable? Eric Winsberg seeks to answer these questions in Science in the Age of Computer Simulation. Scrutinizing these issue with a philosophical lens, Winsberg explores the impact of simulation on such issues as the nature of scientific evidence; the role of values in science; the nature and role of fictions in science; and the relationship between simulation and experiment, theories and data, and theories at different levels of description. Science in the Age of Computer Simulation will transform many of the core issues in philosophy of science, as well as our basic understanding of the role of the digital computer in the sciences.
Computer simulations based on mathematical models have become ubiquitous across the engineering disciplines and throughout the physical sciences. Successful use of a simulation model, however, requires careful interrogation of the model through systematic computer experiments. While specific theoretical/mathematical examinations of computer experim