Epistemic Entitlements and the Practice of Computer Simulation

Symons, John; Alvarado, Rafael

doi:10.1007/s11023-018-9487-0

Cited by 28 publications

(29 citation statements)

References 44 publications

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“…This article supports the claim that our general understanding of computer simulations can be significantly advanced if we shift away from the study of simulations as branches of mathematics and physics, and focus more on computer science and software engineering as basic disciplines. This is the position of authors such asSymons and Alvarado (2019),Durán (2018), and Boyer-Kassem (2014).…”

mentioning

confidence: 88%

“…I am, of course, not alone in this enterprise. As mentioned, Humphreys (1990) already attempted to distinguish mathematical models from computational methods, and authors sensitive to the methodology of computer simulations have followed a similar path (Winsberg 2010;Morrison 2015;Varenne 2018;Symons and Alvarado 2019).…”

Section: Final Remarksmentioning

confidence: 99%

“…In recent years, philosophers have turned their attention to computer simulations and their epistemological value in scientific modeling and scientific practice. When faced with these issues, authors employ one of several strategies: they compare the epistemological power of computer simulations to laboratory experimentation (Morgan 2003;Parker 2009;Symons and Alvarado 2019;Ionescu 2018;Boge 2019); they analyze different forms of inferring knowledge from simulations (Winsberg 2001;Beisbart 2012); they hold extensive discussions on the notion of simulated data as different in kind from experimental and observational data (Barberousse and Marion 2013;Humphreys 2013); or they show how computer simulations are at the center of methodological, conceptual, and industrial changes in scientific research (Lenhard 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The primary aim of this article is to articulate the richness and complexity of simulation models, that is, the type of models at the basis of computer simulations. To this end, I examine the general architecture for simulation models with the objective of recognizing practices, structures, and relations commonly found in computational practice and which differ from other forms of modeling (see Symons and Alvarado 2019;Durán 2018). In particular, I argue that simulation models require new, arguably unprecedented techniques for their design and construction.…”

Section: Introductionmentioning

confidence: 99%

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What is a Simulation Model?

Durán

2020

Minds & Machines

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Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right, that they depart from known forms of scientific models in significant ways, and that a proper understanding of the type of model simulations are fundamental for their philosophical assessment. I argue that simulation models can be distinguished from other forms of models by the many algorithmic structures, representation relations, and new semantic connections involved in their architecture. In this article, I reconstruct a general architecture for a simulation model, one that faithfully captures the complexities involved in most scientific research with computer simulations. Furthermore, I submit that a new methodology capable of conforming such architecture into a fully functional, computationally tractable computer simulation must be in place. I discuss this methodology-what I call recasting-and argue for its philosophical novelty. If these efforts are heading towards the right interpretation of simulation models, then one can show that computer simulations shed new light on the philosophy of science. To illustrate the potential of my interpretation of simulation models, I briefly discuss simulation-based explanations as a novel approach to questions about scientific explanation.

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mentioning

confidence: 88%

Section: Final Remarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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What is a Simulation Model?

Durán

2020

Minds & Machines

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“…This emphasis on the transparency of the interpretive process does not imply that complex or opaque models could not produce reliable data from text materials. As philosophers have argued in the epistemology of simulation literature (Winsberg 2019), modelers can rely on complex and epistemically opaque (Humphreys 2009) computational processes for data generation, given that their reliability and performance have been sufficiently tested by the relevant expert communities (Barberousse and Vorms 2014;Symons and Alvarado 2019). Similarly, data modeling and exploration techniques (e.g., instrumentalist uses of topic modeling to obtain information about word patterns) can be relied on to produce optimal outcomes even when their users do not comprehensively understand the statistical details of the method.…”

Section: Objectivity Through Transparencymentioning

confidence: 99%

Humanistic interpretation and machine learning

Pääkkönen

Ylikoski

2020

Synthese

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This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not eliminate the researchers’ judgments from the process of producing evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes toward topic modeling, i.e., topic realism and topic instrumentalism. Under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the unsupervised text analysis cannot improve the objectivity of interpretation by alleviating the problem of underdetermination in interpretive debate. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work.

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Revisiting ‘The New 4CT Problem’

McEvoy

2022

Handbook of the History and Philosophy of Mathematical Practice

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Epistemic Entitlements and the Practice of Computer Simulation

Cited by 28 publications

References 44 publications

What is a Simulation Model?

What is a Simulation Model?

Humanistic interpretation and machine learning

Revisiting ‘The New 4CT Problem’

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