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Chapter 10
Understanding Simulation Results

Andrew Evans, Alison Heppenstall, and Mark Birkin

Abstract Simulation modelling is concerned with the abstract representation of
entities within systems and their interrelationships; understanding and visualising
these results is often a significant challenge for the researcher. Within this chapter we
examine particular issues such as finding “important” patterns and interpreting what
they mean in terms of causality. We also discuss some of the problems with using
model results to enhance our understanding of the underlying social systems which
they represent, and we will assert that this is in large degree a problem of isolating
causal mechanisms within the model architecture. In particular, we highlight the
issues of equifinality and identifiability—that the same behaviour may be induced
within a simulation from a variety of different model representations or parameter
sets—and present recommendations for dealing with this problem. The chapter ends
with a discussion of avenues of future research.

Why Read This Chapter?
To help you understand the results that a simulation model produces, by suggesting
some ways to analyse and visualise them. The chapter concentrates on the internal
dynamics of the model rather than its relationship to the outside world.

10.1 Introduction

Simulation models may be constructed for a variety of purposes. Classically these
purposes tend to centre on either the capture of a set of knowledge or making
predictions. Knowledge capture has its own set of issues that are concerned
with structuring and verifying knowledge in the presence of contradiction and
uncertainty. The problems of prediction, closely associated with calibration and
validation, centre around comparisons with real data, for which the methods covered
in Chap. 9 (David et al. 2017) are appropriate. In this chapter, however, we look at

A. Evans ( ) • A. Heppenstall • M. Birkin
School of Geography, University of Leeds, Leeds, UK
e-mail: a.j.evans@leeds.ac.uk

© Springer International Publishing AG 2017 205
B. Edmonds, R. Meyer (eds.), Simulating Social Complexity,
Understanding Complex Systems, https://doi.org/10.1007/978-3-319-66948-9_10