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4 Different Modelling Purposes 57

Table 4.1 A brief summary of the discussed modelling purposes
Particular risks (apart from that
Modelling purpose Essential features of lacking the essential features)
Prediction Anticipates unknown data Conditions of application unclear
Explanation Uses plausible mechanisms to Model is brittle, so minor changes
match outcome data in a in the set-up result in bad fit to
well-defined manner explained data
Theoretical exposition Systematically maps out or Bugs in the code; inadequate
establishes the consequences coverage of possibilities
of some mechanisms
Description Relates directly to evidence Unclear documentation; over
for a small set of cases generalisation from cases
described
Illustration Shows an idea clearly Over interpretation to make
theoretical or empirical claims

Acknowledgements Many thanks to all those with whom I have discussed these matters,
including Scott Moss, David Hales, Bridget Rosewell and all those who attended the workshop
on validation held in Manchester.

Further Reading

Epstein, J. M. (2008). Why model? Journal of Artificial Societies and Social
Simulation, 11(4). 12. http://jasss.soc.surrey.ac.uk/11/4/12.html
This gives a brief tour of some of the reasons to simulate other than that of
prediction.
Edmonds, B., Lucas, P., Rouchier, J., & Taylor, R. (2017). Understanding human
societies. doi:https://doi.org/10.1007/978-3-319-66948-9_28.
In this chapter, some modelling purposes that are specific to human social phenom-
ena are examined in more detail giving examples from the literature.

References

Axelrod, R. (1984). The evolution of cooperation. New York, NY: Basic Books.
Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science, 211, 1390–1396.
Cartwright, N. (1983). How the laws of physics lie. Oxford: Oxford University Press.
Cohen, P. R. (1984a). Heuristic reasoning about uncertainty: an artificial intelligence approach.
International Journal of Approximate Reasoning, 1(2), 243–245.
Cohen, P. R. (1984b). Heuristic reasoning about uncertainty: an artificial intelligence approach.
Marshfield, MA: Pitman Publishing.
Edmonds, B. (2001). The use of models - making MABS actually work. In S. Moss & P.
Davidsson (Eds.), Multi agent based simulation, Lecture notes in artificial intelligence (Vol.
1979, pp. 15–32). Berlin: Springer-Verlag.