Tools or Toys?
On Specific Challenges for Modeling and the Epistemology of Models in the Social Sciences

Eckhart Arnold

1 Introduction
2 The role of models in science
3 Why computer simulations are merely models and not experiments
4 The epistemology of simulations at work: How simulations are used to study chemical reactions in the ribosome
5 How do models explain in the social sciences?
6 Common obstacles for modeling in the social sciences
    6.1 Lack of universal background theories
    6.2 Pluralism of Paradigms
    6.3 Why parsimony is a vice and not a virtue
    6.4 “Wholistic” nature of many phenomena in the social sciences
    6.5 Difficulties of measurement
    6.6 Pluralism of scientific styles
7 Conclusions

6.1 Lack of universal background theories

In the social sciences there exist hardly any empirically well confirmed background theories that fully cover the phenomena in their domain. There simply is nothing in the social sciences that compares to Newtonion mechanics or quantum theory in physics. If a physicist wants to explain some mechanical phenomenon it is no question that she must apply the theory of mechanics. In the case of the simulation of the ribosome discussed earlier, there was no question that quantum mechanics is the right theory for the problem. The challenge consisted in how to apply this theory to the problem at hand.

In contrast to that, the first challenge in the social science is to pick the right theory or the right set of theories. One usually has a whole range of theories to chose from. In their book on the Cuban misile crisis, Allison/Zelikow (1999) present three different paradigms, each of which encompasses a host of different theories and scientific approaches, partly overlapping, partly contradicting and partly complementing each other. The way, how social scientists deal with this situation is to pragmatically select from the theoretical supply whatever deems them appropriate, then to look at the question at hand from different angles suggested by different theories and, finally, to assemble this patchwork to a reasonably comprehensive picture.

The best candidate for a universal theory in the social sciences would probably be utility theory in economics. But even if we take this prime example of an axiomatized and highly universal theory, we will not have a theory that could rival the importance and success of Newtonian mechanics in physics. The difference is obvious: Newtonian mechanics can be confirmed empirically in many different constellations and it has at least for a certain well defined range of phenomena (i.e. macroscopic phenomena where the velocities involved are much smaller than the speed of light) never been disconfirmed. Therefore, we can safely draw the inductive conclusion that Newtonian mechanics remains true even in those constellations that have not or cannot be tested directly. Utility theory on the other hand can at best roughly be confirmed in some select scenarios and its general truth or at least its empirical applicability in other cases remains doubtful. One important reason for this state of affairs is that reliable measurement procedures for (cardinal) utility do not exist. It is hard to confirm a theory without being able to measure its central magnitudes. A further reason is the scarcity of principles (i.e. the analogues of natural laws in economics) that come with utility theory, which means that a great part of the explanatory work of models based upon this theory is in effect be done by auxiliary assumptions and situation-specific rules. (See also Cartwright (2009, p.\ 48/49) and Cartwright (1999).)

What are the epistemological consequences then? The most important consequence is that reliance on theoretical validation (i.e. proven or tested compliance with a well-confirmed background theory) remains insufficient, because there are no sufficiently credible background theories to rely on. The more important, therefore, becomes the direct empirical validation of models and simulations in the social sciences.

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