Abstract
Scientific models are used to predict and understand the target phenomena in the reality. The kind of epistemic relationship between the model and the reality is always regarded by most of the philosophers as a representational one. I argue that, complementary to this representational role, some of the scientific models have a constructive role to play in altering and reconstructing the reality in a physical way. I hold that the idealized model assumptions and elements bestow the constructive force of a model on the reality. By recognizing the physical constructive force of some scientific models, the merit of these models could be judged by how successful they are in the reality construction, rather than by the traditional criterion of model-world representation.
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Notes
The reality with which scientists engage in their research is always a fragment of the world, taking the form of data models and physical systems. I thank an anonymous reviewer for pointing this out.
The representationalist view of scientific models is common among scientists too. However, biologists do realize that it is not always the case that a good model is necessarily more accurate in terms of predictions and explanations of the phenomenon of interest. In a review article about modelling complex biological phenomena in silico, Jessica Yu and Neda Bagheri summarize that models with higher multiplexity, which are more favorable in modeling emergent and complex biological phenomena, “do not necessarily offer more accurate predictions or biological insight” due to the complexity of the reality and the limitations of the models (Yu and Bagheri 2016, p. 171).
I am grateful to an anonymous reviewer for helping me emphasize this point, and drawing my attention to Ian Hacking’s, Ibarra and Mormann’s, and Griesemer’s work.
By ‘constructive force’ I mean a realizable capacity of a model in altering and reconstructing the target phenomenon in the reality, in a physical rather than a conceptual way.
Genetically engineered bacteria serve various experimental, clinical, industrial, and pharmaceutical purposes. They have been used extensively in protein research (Merdanovic et al. 2011), host defense studies (Mishra et al. 2017), medical applications (Mishler et al. 2010), biofuel production (Wackett 2011), and drug discovery (Rock et al. 2017).
The Shine–Dalgarno sequence is a ribosomal binding site, at which protein translation is initiated in bacterial mRNAs (Chen et al. 1994).
As opposed to Knuuttila and Boon’s and Boldyrev and Ushakov’s conceptual models, as discussed in Sect. 2, that have no constructive force in terms of physically altering the target phenomenon in the reality.
I thank an anonymous reviewer for pressing me on my statement that the wind tunnel can be viewed as an example of “models-cum-instruments”. I hold that it is the wind tunnel itself (rather than the experiments that are performed within it) that can play the role of models-cum-instruments. There are two types of wind tunnel models-cum-instruments. A wind tunnel can be a physical model and instrument that occupies a physical space, such as one that described by Stathopoulos (1984), which “is of the open return circuit type and has a working section about 12 m (40 ft.) long with a cross-section 1.8 m \(\times \) 1.8 m (6 ft. \(\times \) 6 ft.).” (p. 361), consisting of various components such as fans and transition section (p. 363). This type of wind tunnel can be used as an instrument to measure, for example, the surface pressure. The second type of a wind tunnel model-cum-instrument is a digital model, which is a computer simulation of a wind tunnel that facilitates flow design for industrial use (e.g., Autodesk’s virtual wind tunnel testing simulation tools). Both types of wind tunnels can be used as a model to model the atmospheric phenomenon, and as an instrument to measure various aerodynamic variables.
An E. coli without the capability of horizontal gene transfer is an idealization, for horizontal gene transfer is pervasive in microorganisms.
I thank an anonymous reviewer for pressing on the representational link between the constructive model and the target phenomenon.
A constructive model need not always be a material model. In protein science, a computational model (which consists of algorithms and formalisms) of protein can be used as a constructive model to construct novel proteins for biotechnological and therapeutic use. This computational protein construct can be successfully expressed and purified in the laboratory. A computational model of protein not only serves a constructive purpose, it can also be used to represent the target protein. The constructive role of a computational protein model is complementing its representational role in protein science. Both roles are equally important in protein research—the constructive role guides the production of a protein; the representational role features in protein structure explanation and prediction.
It is contentious and metaphysically challenging, at least in the realm of biology if not in physics, that one could physicallyconstruct the past reality (e.g., the origin of the evolution of multicellularity) in the laboratory. I hold that any such ‘construction’ is a reconstruction instead. It is inconceivable that one could physically construct the earlier biological event in the later time. A reconstruction of an earlier biological event is similar to, but not identical with, the actual one.
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I would like to thank two anonymous reviewers for helpful comments and feedback.
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Tee, SH. Constructing reality with models. Synthese 196, 4605–4622 (2019). https://doi.org/10.1007/s11229-017-1673-8
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DOI: https://doi.org/10.1007/s11229-017-1673-8