This book assumes that you have learned introductory algebra. You have probably studied geometry and have learned how to interpret charts and tables. These are the basic prerequisites. The book does not rely on any prior knowledge of calculus or differential equations. You will read in Chapter 3 that these mathematical tools provide the foundation for "classical physics" and have been used to help environmental scientists study ecological systems. But Chapter 3 also explains that these classical methods suffer from major limitations. You'll read that system dynamics models use numerical methods to deal with the nonlinearities in real systems. The computer performs the tedious calculations; we are free to think about the best way to design the model to represent our system.

If you've had a course in calculus, you'll find that your knowledge of differentiation and integration will come in handy as you read the book. You'll recognize, for example, that stocks act to integrate the effects of the flows over time. And if you've had a course in differential equations, you'll recognize that a system dynamics model may be viewed as a coupled set of first order differential equations. These insights will be helpful, but they are certainly not essential to build and test useful models. Indeed, many students are learning system dynamics in K-12 classrooms with the same "stock-and-flow software" used in this book (Draper and Swanson 1990; Kreith 1997; Zaraza and Fisher 1997).

You've learned algebra, and you've probably been exposed to the "algebra of units." The previous appendix will help refresh your memory on units. This appendix will help refresh your memory of

exponential growth and

exponential decay

arguably the two most important topics covered in your previous study of mathematics. Naturally growing systems often grow in an exponential manner. And when they are in decline, their pattern of decline is often exponential decay. A review of these fundamental patterns of nature is one of the best ways to prepare for the study of modeling. For example, if we can work through the implications of exponential growth in our head, we will be in a much stronger position to judge the plausibility of model simulations of naturally growing systems.