Off-campus access
Using PhilPapers from home?
Click here to configure this browser for off-campus access.
- Daniela M. Bailer-Jones (2003). When Scientific Models Represent. International Studies in the Philosophy of Science 17 (1):59 – 74.Scientific models represent aspects of the empirical world. I explore to what extent this representational relationship, given the specific properties of models, can be analysed in terms of propositions to which truth or falsity can be attributed. For example, models frequently entail false propositions despite the fact that they are intended to say something "truthful" about phenomena. I argue that the representational relationship is constituted by model users "agreeing" on the function of a model, on the fit with data and on the aspects of a phenomenon that are modelled. Model users weigh the propositions entailed by a model and from this decide which of these propositions are crucial to the acceptance and continued use of the model. Thus, models represent phenomena when certain propositions they entail are true, but this alone does not exhaust the representational relationship. Therefore, the constraints that produce the choice of the relevant propositions in a model must also be examined and their analysis contributes to understanding the relationship between models and phenomena.Reading list | Discuss | Edit | Categorize |My bibliography
| Export citation | Other links: informaworld.com tandfonline.com dx.doi.org | Scholar | At my library 
Similar books and articles
A role of models in scientific explanation and insufficiency of unification view of explanation are discussed. Model is constructed by abstracting some important elements from the real world. There are too many complicated interactions in the real world to calculate, but model makes calculations possible. However, that is only part of importance of model in scientific activity. At the first sight, we think that it is better for scientists to use models including more elements (it means the model is closer to the real world), than to use models with less elements. Nevertheless, even when scientists can calculate by using more complex models, they usually use more simple models. This fact gives us the key to clarify an important role of models in scientific explanation. In this presentation, I would like to show that using simple models as possible makes it possible to explain phenomena thus only achieving unification is insufficient for explanation.
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Scholar | More options ...
| | Scholar | More options ... Theoretical models are an important tool for many aspects of scientific activity. They are used, i.a., to structure data, to apply theories or even to construct new theories. But what exactly is a model? It turns out that there is no proper definition of the term "model" that covers all these aspects. Thus, I restrict myself here to evaluate the function of models in the research process while using "model" in the loose way physicists do. To this end, I distinguish four kinds of models. These are (1) models as special theories, (2) models as a substitute for a theory, (3) toy models and (4) developmental models. I argue that models of the types (3) and (4) are considerably useful in the process of theory construction. This will be demonstrated in an extended case-study from High-Energy Physics.
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Other links: hdl.handle.net | Scholar | At my library | More options ...
| | Other links: hdl.handle.net | Scholar | At my library | More options ... Although many philosophers of science have recognized the importance of modeling in contemporary science, relatively little work has been done in developing a general account of models. The most widely accepted account, put forth by advocates of the semantic conception of theories, misleadingly identifies scientific models with the models of mathematical logic. I present an alternative theory of scientific models in which models are defined by their representational relation to a physical system. I explore in some detail a particular sort of model called a ‘mechanical model’ I illustrate the applicability of my approach by applying it to a problem in contemporary speech perception research. The model of models is used to analyze how competing models of the mechanisms of vowel normalization are constructed, tested, and revised.
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Scholar | More options ...
| | Scholar | More options ... I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology of models. This whole approach is further supported by a brief exposition of some recent work in cognitive, or usage-based, linguistics. Finally, with all the above as background, I criticize the recently much discussed idea that claims involving scientific models are really fictions.
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Other links: philsci-archive.pitt.edu springerlink.com | Scholar | At my library | More options ...
| | Other links: philsci-archive.pitt.edu springerlink.com | Scholar | At my library | More options ... The translation of a mathematical model into a numerical one employs various modifications in order to make the model accessible for computation. Such modifications include discretizations, approximations, heuristic assumptions, and other methods. The paper investigates the divergent styles of mathematical and numerical models in the case of a specific piece of code in a current atmospheric model. Cognizance of these modifications means that the question of the role and function of scientific models has to be reworked. Neither are numerical models pure intermediaries between theory and data, nor are they autonomous tools of inquiry. Instead, theory and data are transformed into a new symbolic form of research due to the fact that computation has become an essential requirement for every scientific practice. Therefore the question is posed: What do numerical (climate) models really represent?
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Scholar | At my library | More options ...
| | Scholar | At my library | More options ... The use of models in the construction of scientific theories is as widespread as it is philosophically interesting (and, one might say, vexing).1 In neither philosophical nor scientific practice do we find a univocal concept of model.2 But there is one established usage to which we want to direct our particular attention in this paper, in which a model is constituted by the theorist’s idealizations and abstractions. Idealizations are expressed by statements known to be false. Abstractions are achieved by suppressing what is known to be true. Idealizations over-represent empirical phenomena. Abstractions underrepresent them. We might think of idealizations and abstractions as one another’s duals. Either way, they are purposeful distortions of phenomena on the ground.3..
No categories
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Scholar | More options ...
| | Scholar | More options ... Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations.
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Other links: journals.uchicago.edu dx.doi.org | Scholar | At my library | More options ...
| | Other links: journals.uchicago.edu dx.doi.org | Scholar | At my library | More options ... The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation.
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Scholar | At my library | More options ...
| | Scholar | At my library | More options ... Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as a means to select models based on their ability to answer specific scientific questions given the current understanding of the relevant aspects of the real world.
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Scholar | At my library | More options ...
| | Scholar | At my library | More options ... Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy analysis is proposed as a means to select models based on their ability to answer specific scientific questions given the current understanding of the relevant aspects of the real world.
Reading list
| Discuss | Edit | Categorize | My bibliography | Export citation | Other links: jstor.org | Scholar | At my library | More options ...
| | Other links: jstor.org | Scholar | At my library | More options ... Discussion of Daniela M. Bailer-Jones, When scientific models represent
 Back
All discussions
Start a new thread
|
There are no threads in this forum |
Nothing in this forum yet.
