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Why trust a simulation? Models, parameters, and robustness in simulation-infected experiments

Boge, Florian J. (2021) Why trust a simulation? Models, parameters, and robustness in simulation-infected experiments. [Preprint]

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Abstract

Computer simulations are nowadays often directly involved in the generation of experimental results. Given this dependency of experiments on computer simulations, that of simulations on models, and that of the models on free parameters, how do researchers establish trust in their experimental results? Using high energy physics (HEP) as a case study, I will identify three different types of robustness that I call conceptual, methodological, and parametric robustness, and show how they can sanction this trust. However, as I will also show, simulation models in HEP themselves fail to exhibit a type of robustness I call inverse parametric robustness. This combination of robustness and failures thereof is best understood by differentiating different epistemic capacities of simulations and different senses of trust: Trusting simulations in their capacity to facilitate credible experimental results can mean accepting them as means for generating belief in these results, while this need not imply believing the models themselves in their capacity to represent an underlying reality.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Boge, Florian J.fjboge@uni-wuppertal.de0000-0002-1030-3393
Additional Information: This is a preprint version of a paper accepted for publication in British Journal for the Philosophy of Science. The final version includes minor changes.
Keywords: computer simulation; robustness; measurement; models; high energy physics
Subjects: General Issues > Computer Simulation
General Issues > Experimentation
General Issues > Models and Idealization
Specific Sciences > Physics
Depositing User: Mr Florian Boge
Date Deposited: 17 Jul 2021 03:42
Last Modified: 17 Jul 2021 03:42
Item ID: 19318
Subjects: General Issues > Computer Simulation
General Issues > Experimentation
General Issues > Models and Idealization
Specific Sciences > Physics
Date: 2021
URI: http://philsci-archive.pitt.edu/id/eprint/19318

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