Abstract
This contribution investigates the functions that visualizations fulfill in simulation modeling. The essential point is that visualization supports interaction between modeler and model during the iterative process of model building and adaptation. I argue for a differential perspective, meaning it is the differences between images that play a major role in this process. These differences are pivotal for comparing variants of a model according to their relative performances. This highlights the function not of single images, but of series of them. A couple of illustrative examples cover imagery used in particle physics, computational fluid dynamics engineering, and nanoscale tribology. The discussion shows how image-based simulation methods gear the sciences toward a mode that is well-known from engineering. In epistemic respects, this mode is oriented at a type of knowledge tailor-made for interventions and design. The explanatory capacity, on the other side, seems to be less favored.
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Notes
- 1.
- 2.
About the development of nanoscale research, see Davis Baird, Alfred Nordmann, and Joachim Schummer (2004).
- 3.
Charles S. Peirce has pointed out, however, that mathematical reasoning always contains an iconic part. He has highlighted the potential for surprises that comes with visual qualities, even if a particular figure might have been constructed according to controlled operations. One can “see” what is also the case given the assumptions, without one being able to logically derive the properties. “Diagrammatic reasoning” is an essential part of Peirce’s epistemology, cf. Murray G. Murphey (1961).
- 4.
Mandelbrot was a master in writing for a general audience (cf. Mandelbrot 1983).
- 5.
Matt Spencer has presented as study on the skeptical mindset towards images common among scientists, especially in computational physics (Spencer 2012).
- 6.
According to our perspective on visualizations, they are systematically produced for reasons of comparison. This perspective connects and contributes to a method common in art history and known as “comparative vision” (Bader et al. 2010).
- 7.
One million nanometers make up a millimeter.
- 8.
Richard Feynman, by the way, has published a more elegant version of this consideration. Early on he pointed out the potential for surprises when investigating the behavior of objects on a very fine scale (see Feynman 1960).
- 9.
In another place, I argue that these expectations, namely, expecting the surprise, are characteristic for simulation in nanoscience (Lenhard 2006).
- 10.
I would like to thank also Terry Shinn and Anne Marcovich who provided rich interview material with Uzi Landman. Their much more comprehensive study has appeared as a monograph (Marcovich and Shinn 2014).
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Lenhard, J. (2017). License to Explore: How Images Work in Simulation Modeling. In: Ammon, S., Capdevila-Werning, R. (eds) The Active Image. Philosophy of Engineering and Technology, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-56466-1_10
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