Why Trust a Simulation? Models, Parameters, and Robustness in Simulation-Infected Experiments

British Journal for the Philosophy of Science 75 (4):843-870 (2024)
  Copy   BIBTEX

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 distinguishing 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.

Other Versions

No versions found

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 107,191

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Two Senses of Experimental Robustness: Result Robustness and Procedure Robustness.Koray Karaca - 2022 - British Journal for the Philosophy of Science 73 (1):279-298.
Computer models and the evidence of anthropogenic climate change: An epistemology of variety-of-evidence inferences and robustness analysis.Martin Vezer - 2016 - Computer Models and the Evidence of Anthropogenic Climate Change: An Epistemology of Variety-of-Evidence Inferences and Robustness Analysis MA Vezér Studies in History and Philosophy of Science 56:95-102.
Computer simulation and the features of novel empirical data.Greg Lusk - 2016 - Studies in History and Philosophy of Science Part A 56:145-152.
Observations, Simulations, and Reasoning in Astrophysics.Melissa Jacquart - 2020 - Philosophy of Science 87 (5):1209-1220.
Robustness and sensitivity of biological models.Jani Raerinne - 2013 - Philosophical Studies 166 (2):285-303.
What is a Simulation Model?Juan M. Durán - 2020 - Minds and Machines 30 (3):301-323.
Mechanisms for Robust Cognition.Matthew M. Walsh & Kevin A. Gluck - 2015 - Cognitive Science 39 (6):1131-1171.
Simulation and Calibration: Mitigating Uncertainty.Deborah Haar - 2021 - Philosophy of Science 88 (5):985-996.

Analytics

Added to PP
2021-08-03

Downloads
154 (#158,704)

6 months
32 (#123,383)

Historical graph of downloads
How can I increase my downloads?

Author's Profile

Florian J. Boge
Bergische Universität Wuppertal

Citations of this work

Convergence strategies for theory assessment.Elena Castellani - 2024 - Studies in History and Philosophy of Science Part A 104 (C):78-87.
The Positive Argument Against Scientific Realism.Florian J. Boge - 2023 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 54 (4):535-566.

View all 6 citations / Add more citations

References found in this work

Model Evaluation: An Adequacy-for-Purpose View.Wendy S. Parker - 2020 - Philosophy of Science 87 (3):457-477.
Robustness Analysis as Explanatory Reasoning.Jonah N. Schupbach - 2018 - British Journal for the Philosophy of Science 69 (1):275-300.
Robustness Analysis.Michael Weisberg - 2006 - Philosophy of Science 73 (5):730-742.

View all 32 references / Add more references