Validation of Computer Simulations from a Kuhnian Perspective

von Eckhart Arnold

1 Introduction
2 Kuhn's philosophy of science
3 A revolution, but not a Kuhnian revolution: Computer simulations in science
4 Validation of Simulations from a Kuhnian perspective
    4.1 Do computer simulations require a new paradigm of validation?
    4.2 Validation of simulations and the Duhem-Quine-thesis
    4.3 Validation of social simulations
        4.3.1 Where social simulations differ
        4.3.2 Are social simulations still in a pre-scientific stage?
5 Summary and Conclusions

4.3.1 Where social simulations differ

In the context of validation of social simulations two features of the social sciences become relevant that distinguish them from most natural sciences: Firstly, the social sciences are multi-paradigm-sciences. It is the normal state of these sciences that there exist multiple more or less mutually incommensurable paradigms at the same time. This multi-paradigm-character is well described in the textbook by Moses/Knutsen (2012). For Kuhn such a state of affairs was a sign of a pre-scientific phase. But given that the social sciences are - within inevitable confinements - nonetheless able to produce convincing explanations at least for some social phenomena, the qualification as pre-scientific seems inadequate. Also, if considered in isolation, most of these paradigms expose typical features of normal science, like a textbook-literature, role models and exemplars etc.

Deviating from Kuhn, I therefore suggest, that the qualification as pre-scientific should be reserved to those sciences or branches of a science that - given their state of development - have not yet been able at all to produce results that can be validated or confirmed by some reasonable procedure. The qualification as pre-scientific is in so far justified as without a common understanding and practice of validation one can never be sure whether the results are indeed reliable.

Secondly, the social sciences include qualitative paradigms, including paradigms that rely on hermeneutical methods. It is safe to assume that these can neither be completely ignored nor always be resolved to quantitative or otherwise formal methods and paradigms.[8] As computer simulations are quantitative, the decision to use computer simulations is also a decision for a quantitative paradigm.

Here, I understand the term “quantitative” in a wide sense, including anything that is described in a formal language. This can be formal logic, mathematics, or a programming language. This wide sense of using the term “quantitative” is motivated by the fact all formal descriptions share the same epistemic risks of either losing important information, because the expressive power of formal languages is limited in comparison to natural language, or adding arbitrary assumptions in form of modeling decisions. A simulation model forces its author to provide detailed mechanics of all processes that are included in the model, because otherwise the model would not run. However, if the mechanics are not known, this amounts to theoretical speculation. A purely verbal description, in contrast, allows its author to remain silent or at least adequately vague about underlying mechanics the details of which are not known. On the other hand, because of their strict specification, formal models cannot as easily be misunderstood as verbal descriptions. And they enforce logical consistency.

Both of these features affect the validation of social simulations. Because, when trying to validate a simulation study, say, on the evolution of cooperation, it might become necessary to compare its findings with those of biological field research or, depending on the envisaged application cases, those of cultural history. Thus, different scientific disciplines with different paradigms might be affected. And, it might become necessary to translate between a qualitative descriptive language used in empirical research and the formal languages used in simulation research.

One possible objection when discussing social simulations in the connection with Kuhn, is that it is not a scientific discipline, but a field that runs across several disciplines. However, since this field is shaped by shared attitudes, well-known exemplars (Axelrod 1984, Axtell et al. 2002, Epstein/Axtell 1996, Schelling 1971) and an emerging textbook-literature (Railsback/Grimm 2012, Gilbert/Troitzsch 2005), looking at it from a Kuhnian perspective does not seem too far-fetched.

[8] There are scientists who deny even this and who also believe that without formal models no explanation of any sort is possible in history or social science. I am a bit at loss for giving proper references for this point of view, because I have mostly been confronted with it either in discussions with scientists or by anonymous referees of journals of analytic philosophy. The published source I know of that comes closest to this stance is the keynote “Why model?” by Joshua Epstein (2008), which I have discussed in Arnold (2014).

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