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. This text helps students learn that model building is a dynamic process involving simplification, approximation, abstraction, analysis, computation, and comparison. Students begin the process of model building with a consideration of phenomena arising in another academic area or in the real world.

1. BASIC PRINCIPLES. Overview of the Uses of the Term Model. The Process of Constructing Mathematical Models. Types of Mathematical Models. A Classic Example. Axiom Systems and Models. Simulation Models. Practical Aspects of Model Building. 2. MODEL BUILDING: SELECTED CASE STUDIES. Introduction. Mendelian Genetics. Models for Growth Processes. Social Choice. Moving Mobile Homes. A Stratified Population Model. Selected Simulations. Waiting in Line Again! Estimating Parameters and Testing Hypotheses. 3. MARKOV CHAINS. Introduction. The Setting and Some Examples. Basic Properties of Markov Chains. Regular Markov Chains. Absorbing Chains and Applications. 4. SIMULATION MODELS. Introduction. The Simulation Process. Discrete Random Variables. Discrete Event Simulation. Continuous Random Variables. Applications. 5. LINEAR PROGRAMMING MODELS. Introduction. Formulation of Linear Programming Problems. Linear Programming Problems and Duality. Duality, Sensitivity, and Uncertainty. Job Assignment. Networks and Flows. Appendix A: Projects and Presentations. Introduction. Types of Projects. Examples of Projects. Reports and Presentations. Evaluating Project Reports. Sources of Projects.

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