Simulating Social Complexity pp 235-271 | Cite as

# Combining Mathematical and Simulation Approaches to Understand the Dynamics of Computer Models

### Why Read This Chapter?

To learn how to better understand the dynamics of computer models using both simulation and mathematical analysis. Our starting point is a computer model which is already implemented and ready to be run; the objective is to gain a thorough understanding of its dynamics. Combining computer simulation with mathematical analysis can help to provide a picture of the model dynamics that could not be drawn by using only one of the two techniques.

### Abstract

This chapter shows how computer simulation and mathematical analysis can be used together to understand the dynamics of computer models. For this purpose, we show that it is useful to see the computer model as a particular implementation of a formal model in a certain programming language. This formal model is the abstract entity which is defined by the input-output relation that the computer model executes, and can be seen as a function that transforms probability distributions over the set of possible inputs into probability distributions over the set of possible outputs.

It is shown here that both computer simulation and mathematical analysis are extremely useful tools to analyse this formal model, and they are certainly complementary in the sense that they can provide fundamentally different insights on the same model. Even more importantly, this chapter shows that there are plenty of synergies to be exploited by using the two techniques together.

The mathematical approach to analyse formal models consists in examining the rules that define the model directly. Its aim is to deduce the logical implications of these rules for any particular instance to which they can be applied. Our analysis of mathematical techniques to study formal models is focused on the theory of Markov Chains, which is particularly useful to characterise the dynamics of computer models.

In contrast with mathematical analysis, the computer simulation approach does not look at the rules that define the formal model directly, but instead tries to infer general properties of these rules by examining the outputs they produce when applied to particular instances of the input space. Thus, conclusions obtained with this approach may not be general. On a more positive note, computer simulation enables us to explore formal models beyond mathematical tractability, and we can achieve any arbitrary level of accuracy in our computational approximations by running the model sufficiently many times.

Bearing in mind the relative strengths and limitations of both approaches, this chapter explains three different ways in which mathematical analysis and computer simulation can be usefully combined to produce a better understanding of the dynamics of computer models. In doing so, it becomes clear that mathematical analysis and computer simulation should not be regarded as alternative – or even opposed – approaches to the formal study of social systems, but as complementary. Not only can they provide fundamentally different insights on the same model, but they can also produce hints for solutions for each other. In short, there are plenty of synergies to be exploited by using the two techniques together, so the full potential of each technique cannot be reached unless they are used in conjunction.

## Keywords

Computer Simulation Markov Chain Computer Model Formal Model Mathematical Analysis## Notes

### Acknowledgements

The authors have benefited from the financial support of the Spanish Ministry of Education and Science (projects DPI2004-06590, DPI2005-05676 and TIN2008-06464-C03-02), the Spanish Ministry for Science and Innovation (CSD2010-00034), and the JCyL (projects VA006B09, BU034A08 and GREX251-2009). We are also very grateful to Nick Gotts, Bruce Edmonds, Gary Polhill, and Cesáreo Hernández for many extremely useful discussions.

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