Creating Convincing Simulations in Astrophysics
Bibliographic Data
| ID | 5337528 |
|---|---|
| Authors | M Sundberg (0000-0001-7058-3094, Stockholm University, corresponding author) |
| Year | 2012 |
| Volume | 37 |
| Issue | 1 |
| Pages | 64-87 |
| Publication date | 2012-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Science Technology & Human Values (JOURNAL) |
| Journal identifiers | ISSN: 0162-2439 • E-ISSN: 1552-8251 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0162243910385417 |
| OpenAlex | W2161026209 |
| Language | EN |
| Citations received | 3 |
| References cited | 26 |
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
Authors of the Storm
Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences
Representing and Intervening
Inventing accuracy
The dynamics of coordinated comparisons
Seductive Simulations? Uncertainty Distribution Around Climate Models
Star Crushing
Radical Uncertainty in Scientific Discovery Work
The philosophy of simulation
The philosophical novelty of computer simulation methods
Models of Success Versus the Success of Models
A tale of two methods
Simulations, Models, and Theories
Computer Simulation
Simulated Experiments
The Everyday World of Simulation Modeling
Sanctioning Models
Experimenting on Theories
Epistemic Cultures
Reference Groups as Perspectives
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,21 |
| Citation span | 2012 - 2021 (10) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |