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 Common obstacles for modeling in the social sciences

A possible explanation why it is so difficult to grasp the research logic behind social simulations is that the models and simulations do themselves face much stronger obstacles in the social sciences than in most of the natural sciences. If it proves hard to justify models and simulations in the social sciences then the simple reason may be that very often there is no justification for using these models. In the following a number of common obstacles for modeling in the social sciences will be examined and the possible epistemological consequences for modeling will be discussed.

It is not claimed that these obstacles are exclusive for the social sciences. Some of them may to a lesser or greater degree also plague some simulations in the natural sciences as well. In this case presumably also the epistemological consequences will be the same. If one is aware of these obstacles it becomes easier to understand the specific epistemological conditions of models and simulations in the social sciences.

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