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|
It is much harder to see how models or simulations contribute to the understanding of phenomena in the social sciences than in the natural sciences, because in many cases social science models are highly stylized. Often the degree of idealization is so strong that the models are obviously unrealistic and it becomes hard to see how they represent their target system at all (see Hammerstein (2003) for an example of this problem). The textbook literature on economics, which is the social science in which the use of models is the most pervasive, defends the use of strongly simplified models with the standard argument that because reality is much too complex to be described directly, we would not be able to gain any understanding at all without strongly simplified models (Mankiw 2004, p.\ 22ff.). As it is at the same time obvious that not any arbitrary simplifications are permissible, the question inevitably arises what kind of simplifications are permissible and what kind of simplifications are not. Unfortunately, the economic textbook literature offers no satisfactory answer to this question. To rely on the predictive success of otherwise unrealistic simulations offers no solution, because the predicitive success of economic models is notoriously weak (Betz 2006). And if this is true for economics then the situation for models in other social sciences like sociology or political sciences must be expected to be even worse.
Not surprisingly, therefore, the ongoing debate about the proper use and the epistemic value of models in the social sciences is characterized by a wide diversity of different and often contradictory viewpoints. To an outsider it could almost appear as if mathematical models and computer simulations are used in the social sciences without a common understanding of what they are good for. Among the views taken in this debate are the following:
The basic rationale of this concept of models can be described as this: Models help us to understand the world by generating successful predictions. Other than that they do not need to be particularly realistic.
The bottom-line of the “models as experiments” conception is: Models and Simulations help us to understand the world much like experiments do, e.g. by representing a real-world target system in a controlled environment that allows us to test assumptions.
If models are understood as isolating tools then the important question is, if and how we can learn something from isolating models about the real world. As Nancy Cartwright argues, this depends on the scientific field. According to her, in comparison with physics very little can be learned from isolating models in economics. And one can probably safely generalize this finding to all social sciences. The possible explanation for this limitation that compared to the natural laws in physics economics has only few general principles (Cartwright 2009) links well with our previous contention that strong background theories provide a good ground for successful modeling.
Models help us to understand the world, because they allow us to study the functioning of causal mechanisms in isolation which in the real world are usually mixed up with other mechanisms.
This account of models raises more questions than it answers: In what sense can a world that is “counterfactual” still be credible? And what are the criteria by which the credibility of a “counterfactual” model must be judged?
Regardless of how these questions might be answered, the role of models according to this view can be characterized as follows: Models do not represent particular target systems but they constitute paralell counterfactual worlds. By being “credible” they allow us to draw inductive conclusions about the real world.
Though it does raise questions, this is an interesting robustness concept that certainly deserves further exploration. If it proves to be sound than it offers a strategy how a model can be hardened - up to a certain limit, if the substantive assumptions are not to be tautologies - by a pure model to model comparison.
The bottom line is: If a model is counterfactual or even incredible it can still help us to understand the world.
Although it is probably not appropriate for all types of models, Aydinonat's account is a very convincing one: It provides a clear and convincing idea of how and why models may contribute to explanations in the social sciences. And it can almost immediately be transformed into a research design.
Summarized, this account of models says: Models are partial explanations that describe possible causes of phenomena of a specific type. For providing a full explanation of a particular phenomenon, the model must be sufficiently robust and it must be chacked empirically against other possible causes of the phenomenon.
Just as the previous one this account has the merit of suggesting a research design where the use of models interacts with that of experiments. The kind of iterative research that results is described by Alexandrova with respect to the example of auction design (Alexandrova 2008, p.\ 384ff.).
Summary: Models serve as templates for the generation of hypotheses.
Bottom line: Models serve primarily theoretical functions like that of exploring the implications of concepts and theories.
The diversitiy of views on simulations, exemplified by the above list, is not simply a consequence of the fact that models and simulations are used for different purposes in the social sciences. For, some of the contradictory views like the “credible worlds” (Sugden 2009), “incredible worlds” (Kourikoski/Lethinen 2009), “isolation” (Cartwright 2009, Maeki 2009) and “partial explanation” account (Aydinonat 2007) have been proposed by their authors in the same discussion and under consideration of exactly the same examples.
The situation is somewhat embarrassing because as Sugden has observed “authors typically say very little about how their models relate to the real world” (Sugden 2009, p.\ 25). And it is not because the answer is so obvious that they remain silent. Otherwise, why would there be such a debate? One obvious approach to resolving the debate would be to go through the accounts that have been advanced one by one and either find an account that is the most adequate or to arrive at some kind of synthesis. But, apart from the fact that this procedure would be rather tedious, it is also not guaranteed that it leads to the desired result. For, it is unclear which criteria a good account of models ought to fulfill. Therefore, before entering into any discussion about epistemological accounts of models it might be advisable to ask for the reasons why the research logic of models and simulations in the social sciences is not at all obvious.
So, how is it possible that scientists use models, yet nobody seems really able to tell “how their models relate to the real world” (Sugden 2009, p.\ 25)?
 This procedure is somewhat similar to the “de-idealization” procedure proposed by Ernan McMullin 25 years earlier (Mcmullin 1985, Alexandrova 2008). Only that when “de-idealizing”, the auxiliary assumptions must gradually be replaced by more realistic assumptions. One can conjecture that when both alternatives are available “de-idealization” is the safer method. Derivational robustness analysis could then be understood as a kind of second-best alternative to “de-idealization” when the latter is not available.