PhilSci Archive

Computer simulations, machine learning and the Laplacean demon: Opacity in the case of high energy physics∗

Boge, Florian J. and Grünke, Paul (2019) Computer simulations, machine learning and the Laplacean demon: Opacity in the case of high energy physics∗. [Preprint]

[img]
Preview
Text
LevelsOfOpacity.pdf

Download (592kB) | Preview

Abstract

In this paper, we pursue three general aims: (I) We will define a notion of fundamental opacity and ask whether it can be found in High Energy Physics (HEP), given the involvement of machine learning (ML) and computer simulations (CS) therein. (II) We identify two kinds of non-fundamental, contingent opacity associated with CS and ML in HEP respectively, and ask whether, and if so how, they may be overcome. (III) We raise the question of whether any kind of opacity, contingent or fundamental, is unique to ML or CS, or whether they stand in continuity to kinds of opacity associated with other scientific research.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Preprint
Creators:
CreatorsEmailORCID
Boge, Florian J.fjboge@uni-wuppertal.de0000-0002-1030-3393
Grünke, Paulpaul.gruenke@kit.edu
Additional Information: ∗This is a preprint of a paper forthcoming in Resch, Kaminski, and Gehring (Eds.), The Science and Art of Simulation II, Springer (expected 2020). The final version may contain various changes.
Keywords: machine learning, opacity, deep neural networks, computer simulation, high energy physics
Subjects: Specific Sciences > Computation/Information > Classical
General Issues > Computer Simulation
General Issues > Experimentation
Specific Sciences > Physics > Fields and Particles
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Technology
Depositing User: Mr Florian Boge
Date Deposited: 24 Jul 2020 03:30
Last Modified: 24 Jul 2020 03:30
Item ID: 17637
Subjects: Specific Sciences > Computation/Information > Classical
General Issues > Computer Simulation
General Issues > Experimentation
Specific Sciences > Physics > Fields and Particles
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Technology
Date: 2019
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/17637

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item