Validation of Computer Simulations from a Kuhnian Perspective

Contents

4 Validation of Simulations from a Kuhnian perspective

Can Kuhn's concept of paradigm illuminate the validation of computer simulations? And, if so, how? In the following, I am going to state several questions that can be raised in this context and then try to give answers to these questions based on the current discussion on computer simulations in the philosophy of science. The questions that in my opinion deserve consideration are:

1. Notwithstanding the question (discussed earlier) to what extent computer simulations have prompted paradigm shifts in science, another question is, whether computer simulations have lead to, or require new paradigms in the logic of scientific discovery. Classical research logic assumes a clear distinction between theoretical research based on deductive inference and empirical research based on experiment and (potentially theory-laden) observation.[3] Most importantly, there is a hierarchy between the theoretical and empirical realm. Theoretical assumptions are confirmed or disconfirmed by empirical tests - not the other way round. Computer simulations are sometimes depicted as being located somewhere between empirical and theoretical research, and - as the common metaphor of “computer experiments” suggests[4] - blurring the lines between the two (Morrison 2009).
    
2. In a similar vein, computer simulations often rely on a rich mixture of assumptions and technicalities that are drawn from diverse sources. In the philosophical literature on simulations this has been described as their being “motley” (Winsberg 2015) and not simply falling from theory. This can raise worries concerning the prospects of empirical validation of computer simulations. In particular, the question can be asked if the sort of problems associated with the Duhem-Quine-thesis increase with computer simulations: You may know that your simulation contains many abstractions, simplifications and presumptions, but you cannot be sure which of these are potentially dangerous.
    
3. Finally, some thoughts shall be given to the validation of simulations in the social sciences. Because the social sciences are multi-paradigm-sciences the validation of simulations raises specific problems in this area. Given that it is still not common practice to validate simulations, one can even ask whether the field of social simulations has already emerged from a pre-scientific state.

[3] Because theory-ladenness of observation is an often misunderstood topic, two remarks are in order: 1) Theory-ladeness of observation as such does not blur the distinction between theory and observation. At worst we have a distinction between pure theory (without any observational component) and theory-laden observation. 2) Theory-ladeness of observation does not lead to a vicious circle when confirming theories by empirical observation. This is true, as long as the observations are not laden with the particular theories for the confirmation of which they are used. - There are areas in science where no sharp distinction between theoretical reasoning and reporting of observations is made. However, as far as computer simulations are concerned, it is clear that because Turing Machines do not make observations, a computer program is always a theoretical entity - notwithstanding the fact that a computer program may represent an empirical setting or make use of empirical data. In the latter respect it can be compared with a physical theory that may in fact represent empirical reality as well as contain natural constants (i.e. empirical data).

[4] See also chapter 37 (Beisbart 2019) in this volume.

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