Introduction to Mathematical Modelling: (Dover Books on Computer Science)

Accessible text features over 100 reality-based examples pulled from the science, engineering and operations research fields. Prerequisites: ordinary differential equations, continuous probability. Numerous references. 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.

Table of Contents:
1. What is Modeling 1.1 Models and Reality 1.2 Properties of Models 1.3 Building a Model 1.4 An Example 1.5 Another Example; Problems 1.6 Why Study Modeling? Part I. Elementary methods 2. Arguments from Scale 2.1 Effects of Size; Costs of Packaging; Speed of Racing Shells; Size Effects in Animals; Problems 2.2 Dimensional Analysis; Theoretical Background; The Period of a Perfect Pendulum; Scale Models of Structures; Problems 3. Graphical Methods 3.1 Using Graphs in Modeling 3.2 Comparative Statics; The Nuclear Missile Arms Race; Biogeography: Diversity of Species on Islands; Theory of the Firm; Problems 3.3 Stability Questions; Cobweb Models in Economics; Small Group Dynamics; Problems 4. Basic Optimization 4.1 Optimization by Differentiation; Maintaining Inventories; Geometry of Blood vessels; Fighting forest Fires; Problems 4.2 Graphical Methods; A Bartering Model; Changing Environment and Optimal Phenotype; Problems 5. Basic Probability 5.1 Analytic Models; Sex Preference and Sex Ratio; Making Simple Choices; Problems 5.2 Monte Carlo Simulation; A Doctor's Waiting Room; Sediment Volume; Stream Networks; Problems; A Table of 3000 Random Digits 6. Potpourri; Desert Lizards and Radiant Energy; Are Fair Election Procedures Possible?; Impaired Carbon Dioxide Elimination; Problems Part 2. More Advanced Methods 7. Approaches to Differential Equations 7.1 General Discussion 7.2 Limitations of Analytic Solutions 7.3 Alternative Approaches 7.4 Topics Not Discussed 8. Quantitative Differential Equations 8.1 Analytical Methods; Pollution of the Great Lakes; The Left Turn Squeeze; Long Chain Polymers; Problems 8.2 Numerical Methods; Towing a Water Skier; A Ballistics Problem; Problems; The Heun Method 9. Local Stability Theory 9.1 Autonomous systems 9.2 Differential Equations; Theoretical Background; Frictional Damping of a Pendulum; Species Interaction and Population Size; Keynesian Economics; More Complicated Situations; Problems 9.3 Differential-Difference Equations; The Dynamics of Car Following; Problems 9.4 Comments on Global Methods; Problem 10. More Probability; Radioactive Decay; Optimal Facility Location; Distribution of Particle Sizes; Problems Appendix. Some probabilistic Background A.1 The Notion of Probability A.2 Random Variables A.3 Bernoulli Trials A.4 Infinite Events Sets A.5 The Normal Distribution A.6 Generating Random Numbers A.7 Least Squares A.8 The Poisson and Exponential Distributions References; A Guide to Model Topics; Index