Abstract
Certain myths have emerged about computer technology in general, such as the almighty electronic brain that outperforms humans in every discipline or legends about the capability of artificial intelligence. Some of these myths find echoes in the field of computer simulation, like simulation being pure number-crunching on supercomputers. This article reflects on myths about computer simulation and tries to oppose them. At the beginning of the paper, simulation is defined. Then, some central myths about computer simulation will are identified from a general computer science perspective. The first central myth is that simulation is a virtual experiment. This view is contradicted by the argument, that computer simulation is located in between theory and experiment. Furthermore, access to reality is possible indirectly via representation. The second myth is that simulation is said to be exact. This myth can be falsified by examining technical and conceptual limitations of computer technology. Moreover, arguments are presented as to why ideal exactness is neither possible nor necessary. A third myth emerges from the general overstatement of computer technology: Everything can be simulated. It will be shown that simulation can only solve problems that can be formalized and calculated—and can only produce results that are within the scope of the models they are based on.
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- 1.
For myths and critique on artificial intelligence, see Dreyfus (1992).
- 2.
It seems that nowadays, myths are no longer relevant, due to the technical and scientific progress. However, this does not mean that science and logic are protected from falling back into myths, when logic and science are set as the absolute methods to gain knowledge in an epistemological process. Moreover, this means that enlightenment does not necessarily imply progress. Horkheimer and Adorno discuss this in Horkheimer and Adorno (1972).
- 3.
As Paul Humphreys (2009) states, humans are not able to oversee and reproduce the simulation process (and especially the calculations on the lower levels of computation) since it is too complex and the processing speed is too high. Consequently, computer simulation must be seen as a black box with lots of abstraction that a human is not able to retrace.
- 4.
The example Winsberg gives of a physicist studying “the interaction of a pair of fluids at supersonic speeds”, relies on the following background knowledge: The theory of fluids and the physicist’s intuition regarding the physical assumptions are arguments for the external validity of the study. As a third, the tricks physicists apply to make the simulations work, which are in turn derived from the historicity of the simulation process, stand for the specific model building principle.
- 5.
Quantum computing could change all of this since it is able to break down complexity classes so that problems with an exponential time complexity could become feasible. However, this is out of the scope of this paper. Furthermore, if-then argumentations lead to speculative discussions that are no longer scientific or productive, such as in the discussions on nanotechnology (Nordmann 2007).
- 6.
One could now argue that machine learning could change the whole picture. Nevertheless, in terms of the algorithms, there is still strong transparency: All the steps of a computer program are determined, but the opaqueness of the paths of the steps as well as of the results, is increased so that they become not traceable. However, a closer look at machine learning is beyond the scope of this paper.
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Acknowledgments
I thank Dr. Andreas Kaminski for encouraging me to do this paper and philosophical advice. I’d also like to thank Dr. Juan Manuel Durán for helpful comments and remarks on the draft. Moreover I want to thank Wanda Spahn for proofreading.
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Schembera, B. (2017). Myths of Simulation. In: Resch, M., Kaminski, A., Gehring, P. (eds) The Science and Art of Simulation I . Springer, Cham. https://doi.org/10.1007/978-3-319-55762-5_5
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