So far this book has offered appetizers. This chapter deals with the preparation of the main course by introducing concepts for linking the simple models discussed in this book to understand the complex processes that lead to pattern formation in geological settings. Geoscientists spend much of their time and effort identifying and explaining naturally occurring spatial and temporal patterns with the goal of interpreting those patterns to understand the processes and conditions that formed them. They are challenged by the need to identify meaningful patterns in situations that often appear to be chaotic (Crutchfield, 2012). Meaningful patterns are frequently subtle and recognizing them often requires clues provided by process models. Most pattern-forming systems are too complex to interpret in a holistic way so our strategy is to first parse them into simple processes, which can be accurately modeled using methods like those explained in this book. The resulting models of discrete processes are then linked to simulate the overall pattern-forming scenario. This strategy is widely used in science and technology. For example, engineers design processing plants by dividing the overall process into unit operations, each of which is responsible for a single chemical or physical transformation of a feedstock (Gupta and Yan, 2006; Hendricks, 2006; McCabe et al., 1993). These unit operations are modeled separately and the models are combined to simulate the entire processing plant. This strategy is especially effective when the unit operations occur in a linear array of steps so that the product of one step is the feed for the next. Natural processes are often more complicated because they can switch from one path to another in a stochastic manner or because there is one or more feedback loops in the overall process. Regardless of the complexity of the situation, the divide and analyze strategy is the most effective way to understand how observed patterns are related to unit processes. The challenge of interpreting pattern-forming processes is a very exciting scientific frontier (Ball, 1999; Nicolis and Prigogine, 1989).