Creating Convincing Simulations in Astrophysics

Dados Bibliográficos

ID5337528
AutoresM Sundberg (0000-0001-7058-3094, Stockholm University, autor correspondente)
Ano2012
Volume37
Fascículo1
Páginas64-87
Data de publicação2012-01-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoScience Technology & Human Values (JOURNAL)
Identificadores do periódicoISSN: 0162-2439 • E-ISSN: 1552-8251
EditoraSAGE Publications Inc (PUBLISHER)
DOI10.1177/0162243910385417
OpenAlexW2161026209
IdiomaEN
Citações recebidas3
Referências citadas26

Numerical simulations have come to be widely used in scientific work. Like experiments, simulations generate large quantities of numbers (output data) that require analysis and constant concern with uncertainty and error. How do simulationists convince themselves, and others, about the credibility of output? The present analysis reconstructs the perspectives related to performing numerical simulations, in general, and the situations in which simulationists deal with uncertain output, in particular. Starting from a distinction between idealized and realistic simulations, the paper presents the principal methods of evaluation in relation to these practices and how different audiences expect different methods. One major challenge in interpreting output data is to distinguish between "real" and "numerical" effects. Within the practice of idealized simulations, simulationists hold the underlying model accountable for results that manifest "real" effects, but because "numerical" and "real" effects cannot be distinguished on the basis of what they derive from, attempted causal explanations are rather justifications for their conclusions. At the same time, simulationists' explanations are part and parcel of their contradictory perspectives, according to which they believe in simulations largely due to the underlying model, while painfully recognizing everything they have to add to make computations doable on the basis of this model

Algorithm · Computation · Credibility · Data mining · Econometrics · Epistemology · Physics · Statistical physics · Computer Science · Mathematics · Philosophy and History of Science · Quantum Mechanics and Applications · Scientific Computing and Data Management