Over the last decades, the rapid development of computer simulations has increasingly shaped and changed the face of scientific experimentation and theorizing as well as engineering research and design. Moreover, computer simulations play an ever more important role in assessing the impact of human actions on the environment.

This raises the central question of how and under what conditions computer simulations can generate reliable predictions or enable understanding. Starting from the current debate on the epistemology of computer simulation, my PhD project aims to address the epistemic challenges that arise from scientists’ use of computer simulations from a philosophical perspective. The PhD project is associated with the DFG Research Training Group “Tailored Scale-Bridging Approaches to Computational Nanoscience” (RTG 2450) and uses the materials sciences as a case study to examine the epistemic characteristics of computer simulations. Collaboration with scientists who use the different types of computer simulations in the course of their research is an integral part of the dissertation project.

In the materials sciences, there is a broad variety of (technological) applications, ranging from catalysts to batteries. Moreover, their use of computer simulations raises a series of philosophical questions that are also highly relevant in other fields of scientific research:

• Can the validation and verification of computer simulations be decoupled?
• Are computer simulations epistemically opaque? If so, what are the sources of opacity, and how can we deal with it?
• What role do computer simulations play in understanding physical processes on different time and length scales?
• Understanding, explaining, describing? What epistemic goals are related to the use of computer simulations? And how can these goals be achieved? What is, e.g., the epistemic role of visualizations?

Another interesting point about computer simulations in the materials sciences is that, despite their numerous successful applications, simulation methods usually involve a variety of heuristic or phenomenological assumptions and are often limited to a certain time and length scale. As, however, many interesting research problems involve a broad range of scales, it is sometimes necessary to combine different established simulation methods or to use innovative multiscale simulation techniques. I am particularly interested in the epistemic peculiarities of such multiscale approaches, e.g. in the way in which the strategies for bridging different scales can be rationalized.

Furthermore, the philosopher Eric Winsberg has pointed out that multiscale models raise a number of interesting philosophical questions about the relationship between different theories: Some research questions in computational nanoscience seem to require the linking of different theoretical frameworks by means of multiscale approaches. But how can we build reliable multiscale models from different, perhaps even conflicting theoretical frameworks? (cf. Winsberg 2006) What kinds of relationships are possible between the different theoretical frameworks involved in the modelling and simulation process? What role do idealizations, abstractions, and perhaps even fictions play in the development of successful computer simulations? (ibid.)

Besides investigating the philosophical peculiarities of multiscale simulations and the questions they raise with respect to intertheoretic relations, I will focus in particular on the identification and characterization of epistemic risks (cf. Biddle and Kukla 2017) that may play a role in the definition and operationalization of concepts, data preparation, and, for example, in the choice of models in computational nanoscience. How do these risks relate to the often-asserted epistemic opacity of computer simulations? How should scientists deal with epistemic risks?

Moreover, I am interested in how collective scientific practices can produce new knowledge in computational nanoscience. Assuming that the way knowledge is generated and institutionalized in epistemic communities plays an important role in characterizing the peculiarities of computer simulations, I intend to take into account ideas from social epistemology as well as STS here.

References

Biddle, Justin B., and Rebecca Kukla. “The geography of epistemic risk.” In Elliott, K. and T. Richards (Eds.): Exploring inductive risk: Case studies of values in science. Oxford University Press, 2017. 215-237.

Winsberg, Eric. “Handshaking your way to the top: Simulation at the nanoscale.” Simulation. Springer, 2006. 139-151.

Winsberg, Eric. Science in the age of computer simulation. University of Chicago Press, 2010.