# Complex Systems Modeling

**Abstract **Modeling is a necessary mechanism for understanding complex phenomena such as the messes this book is designed to help with. This chapter compares methods available for complex systems modeling. A method is then recommended for use in addressing messes. A framework for the development and use of such a model and an accompanying simulation is then presented. This framework is demonstrated on an example problem, with an eye toward using this approach to first think about, then act on, and finally observe our mess systemically.

## Introduction

We use models to gain understanding about complex phenomena; indeed, modeling is a “purposeful abstraction of reality” (Hester & Tolk, 2010, p. 18). Maria (1997) offers, “a model should be a close approximation to the real system and incorporate most of its salient features. On the other hand, it should not be so complex that it is impossible to understand and experiment with it. A good model is a judicious tradeoff between realism and simplicity” (p. 7). It is a necessary simplification of the real-world system it models.

Figure 5.1 illustrates the methods available for modeling a system. If the option is available and it is feasible (i.e., it is not too dangerous, timely, or costly), we would prefer to experiment with the actual system to improve our understanding. Given the messes this book is intended to address, this is not realistic. These systems are too complex and unwieldy for full-scale experimentation to be undertaken (i.e., imagine experimenting with a nuclear missile attack or catastrophic flood in order to test potential mitigation strategies). For similar scale-driven reasons, a physical model is unobtainable for experimentation purposes. The underlying complexity and divergent perspectives associated with the associated systems make closed-form analytical solutions problematic as well. This leaves us with a simulation in order to gain understanding about our mess.

Mechanics regarding the simulation of a real-world system are not trivial. The decision to create a simulation carries with it the burden of choosing an appropriate © Springer International Publishing AG 2017

P.T. Hester and K.M. Adams, *Systemic Decision Making,* Topics in Safety, Risk, Reliability and Quality 33, DOI 10.1007/978-3-319-54672-8_5

**Fig. 5.1 ****Modeling methods (adapted from Law & Kelton, 2000)**

mathematical framework on which to build it. This chapter compares the methods available for complex systems modeling, it outlines the choice of a method considering mess characteristics discussed in previous chapters, and it presents a framework for developing such a model and an accompanying simulation. This framework is then demonstrated on an example problem, with an eye toward using this approach to first think about, then act on, and finally observe our mess systemically.