The Hierarchy of Models in Simulation

Winsberg, Eric

doi:10.1007/978-1-4615-4813-3_16

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…For a fascinating recent discussion of the hierarchy of models account of scientific experimentation in the context of high-energy physics experiments see (Karaca, 2018). For discussion in the context of classical computer simulation see (Winsberg, 1999).…”

Section: System and Simulation Modelsmentioning

confidence: 99%

Introduction: A New Instrument of Science?

Hangleiter

Carolan

Thébault

2022

Analogue Quantum Simulation

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Section: System and Simulation Modelsmentioning

confidence: 99%

Introduction: A New Instrument of Science?

Hangleiter

Carolan

Thébault

2022

Analogue Quantum Simulation

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“…This fact has recently been recognised by philosophers for raising a number of epistemological challenges: Simulated data are often treated as equivalent to experimental data, and this requires an analysis as to the appropriateness of such treatments (Morrison, 2015;Massimi and Bhimji, 2015;Parker, 2017). Devising a CS presupposes a whole chain of models that includes manifold successive modifications, from simplifications and approximations to outright artificial constructions (Lenhard, 2007;Winsberg, 1999;Morrison, 2009;Winsberg, 2010;Boge and Zeitnitz, 2020). This introduces sources of potential error from incorrect modelling to malfunctioning hard-and software.…”

Section: Introductionmentioning

confidence: 99%

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

Boge¹

2024

The British Journal for the Philosophy of Science

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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.* 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.

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“…According to the HoM account, a scientific experiment is essentially a model-based activity, in the sense that it is performed by means of various types of models that relate to each other through a hierarchical structure. Over the years, the HoM account has been applied to various experimental contexts, including the case of electron microscopy (Harris 1999), the case of binary pulsar analysis (Mayo 2000), the case of the Collider Detector Experiment at Fermilab (Staley 2004), and the case of simulation studies (Winsberg 1999).…”

Section: Introductionmentioning

confidence: 99%

Lessons from the Large Hadron Collider for model-based experimentation: the concept of a model of data acquisition and the scope of the hierarchy of models

Karaca

2017

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According to the hierarchy of models (HoM) account of scientific experimentation developed by Patrick Suppes and elaborated by Deborah Mayo, theoretical considerations about the phenomena of interest are involved in an experiment through theoretical models that in turn relate to experimental data through data models, via the linkage of experimental models. In this paper, I dispute the HoM account in the context of present-day high-energy physics (HEP) experiments. I argue that even though the HoM account aims to characterize experimentation as a model-based activity, it does not involve a modeling concept for the process of data acquisition, and it thus fails to provide a model-based characterization of the theory-experiment relationship underlying this process. In order to characterize the foregoing relationship, I propose the concept of a model of data acquisition and illustrate it in the case of the ATLAS experiment at CERN's Large Hadron Collider, where the Higgs boson was discovered in 2012. I show that the process of data acquisition in the ATLAS experiment is performed according to a model of data acquisition that specifies and organizes the experimental procedures necessary to select the data according to a predetermined set of selection criteria. I also point out that this data acquisition model is theory-laden, in the sense that the underlying data selection criteria are determined by considering the testable predictions of the theoretical models that the ATLAS experiment is aimed to test. I take this sense of theory-ladenness to indicate that the relationship between the procedures of the ATLAS experiment and the theoretical models of the phenomena of interest is first established, prior to the formation of data models, through the data acquisition model of the experiment, thus not requiring the intermediary of other types of models as suggested by the HoM account. I therefore conclude that in the context of present-day HEP experiments, the HoM account does not consistently extend to the process of data acquisition so as to include models of data acquisition.

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The Hierarchy of Models in Simulation

Cited by 8 publications

References 6 publications

Introduction: A New Instrument of Science?

Introduction: A New Instrument of Science?

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

Lessons from the Large Hadron Collider for model-based experimentation: the concept of a model of data acquisition and the scope of the hierarchy of models

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