Tools or Toys?
|Table of Contents|
|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 Multiple and varying causes for the same effect|
|6.4 “Wholistic” nature of many phenomena in the social sciences|
|6.5 Difficulties of measurement|
|6.6 Pluralism of scientific styles|
Social sciences are typically characterized by a pluralism of paradigms and a multitude of competing theories about the same domain. This is a fact that it is often lamented about, but it can also be seen as a chance, because the limits of one paradigm often become apparent only in the light of other paradigms. This can also nicely be illustrated by the previously quoted study on the Cuban missile crises (Allison/Zelikow 1999), because each of the three therein discussed paradigms (rational actor, organizational behaviour, governmental politics) lends itself to a comprehensive story about the Cuban missile crisis. And it is only by considering the other paradigms that one really becomes aware that there is more to it.
What consequences does the pluralism of paradigms in the social sciences have for modeling and simulating? First of all, there is a danger of exclusively paying attention to only those paradigms that allow for mathematical modeling. Now, as there is no reason a priori why these paradigms should be any better than other paradigms, choosing a paradigm merely on the basis of the relatively irrelevant criterion of technical implementability bears the danger of getting a seriously distorted image of reality. Many of the associated problems have become apparent in the heated debate about rational choice explanations in political science that started in the midst nineties. They are most clearly pointed out in Shapiro's “The Flight from Reality in the Human Sciences” (Shapiro 2005).
Thus, before a model is accepted as the proper description or explanation, other alternatives, and this includes also non-mathematical depictions of the object under study, should be considered, too. In this respect, theoretical models (i.e. models that mostly rely on some background theory or paradigm or, worst of all, on “plausible assumptions”) are probably much more dangerous than models of phenomena or data. For, if one is directly working with the empirical subject matter, one becomes easier aware of the insufficiencies of theoretical assumptions.