Skip to main content
SpringerLink
Log in

Abstraction as an Autonomous Process in Scientific Modeling

  • Published:
Philosophia Aims and scope Submit manuscript

Abstract

Abstraction is one of the important processes in scientific modeling. It has always been implied that abstraction is an agent-centric activity that involves the cognitive processes of scientists in model building. I contend that there is an autonomous aspect of abstraction in many modeling activities. I argue that the autonomous process of abstraction is continuous with the agent-centric abstraction but capable of evolving independently from the modeler’s abstraction activity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. Leonelli (2008) distinguishes between abstraction as an activity and abstraction as an attribute of models. I focus on abstraction as an activity/process in this paper.

  2. This view is consistent with Giere’s (2010) and Levins’s (2006) view that abstractions as a process are neither true nor false but reflecting the choice of scientists in modeling. Though scientists have no intention to make a false description or explanation of the target phenomenon in the process of abstraction, there is no guarantee that they are able to truthfully and accurately model the phenomenon of interest.

  3. A theoretical model is non-material and always characterized with equations, formalisms or fictional objects. In contrast, a material model consists of material objects as its parts. A material model can be a living thing (e.g., model organisms) or a non-living physical object (e.g., a scale model).

  4. My broad definition of a theoretical model (and of a material model below) will not affect my argument that an autonomous process of abstraction does have an epistemic role to play in scientific modeling.

  5. The moving-out of a discontented individual of race X will change the state of the racial constitution of the neighborhood, reducing the number of race X in that neighborhood. The other Xs in that neighborhood are more likely to become discontented (according to the rule of movement) following the movement of an individual X, which will lead to more Xs to leave and change the state of the racial constitution that may result in a sharp racial segregation. Similarly, the movement of an individual into a new neighborhood will change the state of the racial constitution of that new neighborhood and the psychological state of the existing individuals.

  6. Material models are widely used in teaching. Students who are manipulating a ball-and-stick model of molecules can learn an abstraction process through removing certain parts from the model. For instance, students learn that a hydrogen atom can be abstracted away from a molecule by removing a white plastic sphere (which represents a hydrogen atom) from the model (see Toon 2011).

  7. Due to space constraint, I leave it for other occasions.

References

  • Ankeny, R. A., & Leonelli, S. (2011). What’s so special about model organisms? Studies in History and Philosophy of Science, 42, 313–323.

    Article  Google Scholar 

  • Batterman, R. W. (2009). Idealization and modeling. Synthese., 169, 427–446.

    Article  Google Scholar 

  • Blümel, M., Dally, N., & Jung, C. (2015). Flowering time regulation in crops—What did we learn from Arabidopsis? Current Opinion in Biotechnology, 32, 121–129.

    Article  Google Scholar 

  • Bokulich, A. (2011). How scientific models can explain. Synthese., 180, 33–45.

    Article  Google Scholar 

  • Bokulich, A. (2016). Fiction as a vehicle for truth: Moving beyond the ontic conception. The Monist, 99, 260–279.

    Article  Google Scholar 

  • Cartwright, N. (1989). Nature’s capacities and their measurement. Oxford: Clarendon Press.

    Google Scholar 

  • Colyvan, M. (2014). The undeniable effectiveness of mathematics in the special sciences. In M. C. Galavotti, D. Dieks, W. J. Gonzalez., S. Hartmann., T. Uebel., & M. Weber. (Eds.). New directions in the philosophy of science (pp. 63–73). Switzerland: Springer.

  • de Donato Rodríguez, X., & Bonilla, J. Z. (2009). Credibility, idealisation, and model building: An inferential approach. Erkenntnis., 70(1), 101–118.

    Article  Google Scholar 

  • Fagan, M. B. (2012). Waddington redux: Models and explanation in stem cell and systems biology. Biology and Philosophy, 27, 179–213.

    Article  Google Scholar 

  • Fagan, M. B. (2016). Generative models: Human embryonic stem cells and multiple modeling relations. Studies in History and Philosophy of Science., 56, 122–134.

    Article  Google Scholar 

  • Frigg, R., and Hartmann, S. (2012). Models in science. The Stanford Encyclopedia of Philosophy. E.N. Zalta (Ed.), https://plato.stanford.edu/entries/models-science/. Accessed 31 Oct 2017.

  • Giere, R. N. (2010). An agent-based conception of models and scientific representation. Synthese., 172, 269–281.

    Article  Google Scholar 

  • Gildenhuys, P. (2011). Righteous modeling: The competence of classical population genetics. Biology and Philosophy, 26, 813–835.

    Article  Google Scholar 

  • Godfrey-Smith, P. (2009). Abstractions, idealizations, and evolutionary biology. In A. Barberousse, M. Morange, & T. Pradeu (Eds.). Mapping the future of biology: evolving concepts and theories (pp. 47–55). Netherlands: Springer.

  • Hindriks, F. (2013). Explanation, understanding, and unrealistic models. Studies in History and Philosophy of Science., 44, 523–531.

    Article  Google Scholar 

  • Huber, L., & Keuck, L. K. (2013). Mutant mice: Experimental organisms as materialised models in biomedicine. Studies in History and Philosophy of Biological and Biomedical Sciences., 44, 385–391.

    Article  Google Scholar 

  • Isaac, A. M. C. (2014). Model uncertainty and policy choice: A plea for integrated subjectivism. Studies in History and Philosophy of Science., 47, 42–50.

    Article  Google Scholar 

  • Jones, M. R. (2005). Idealization and abstraction: A framework. In M. R. Jones & N. Cartwright (Eds.), Idealization XII: Correcting the model. Idealization and abstraction in the sciences (pp. 173–217). New York: Rodopi.

    Chapter  Google Scholar 

  • Kuorikoski, J., Lehtinen, A., & Marchionni, C. (2010). Economic modelling as robustness analysis. British Journal for the Philosophy of Science., 61(3), 541–567.

    Article  Google Scholar 

  • Leonelli, S. (2008). Performing abstraction: Two ways of modelling Arabidopsis thaliana. Biology and Philosophy, 23, 509–528.

    Article  Google Scholar 

  • Levins, R. (2006). Strategies of abstraction. Biology and Philosophy, 21, 741–755.

    Article  Google Scholar 

  • Levy, A., & Bechtel, W. (2013). Abstraction and the organization of mechanisms. Philosophy of Science, 80, 241–261.

    Article  Google Scholar 

  • Lízal, P., & Relichová, J. (2001). The effect of day length, vernalization and DNA demethylation on the flowering time in Arabidopsis thaliana. Physiologia Plantarum, 113, 121–127.

    Article  Google Scholar 

  • Mäki, U. (1994). Reorienting the assumptions issue. In R. E. Backhouse (Ed.), New directions in economic methodology (pp. 237–256). London: Routledge.

    Google Scholar 

  • Martínez, S. F., & Huang, X. (2011). Epistemic groundings of abstraction and their cognitive dimension. Philosophy of Science, 78, 490–511.

    Article  Google Scholar 

  • Matthiessen, D. (2017). Mechanistic explanation in systems biology: Cellular networks. The British Journal for the Philosophy of Science., 68(1), 1–25.

    Article  Google Scholar 

  • McCoy, C. D., & Massimi, M. (2018). Simplified models: A different perspective on models as mediators. European Journal for Philosophy of Science., 8(1), 99–123.

    Article  Google Scholar 

  • Morgan, M. S. (2012). The world in the model: How economists work and think. New York: Cambridge University Press.

    Book  Google Scholar 

  • Morrison, M. (2007). Where have all the theories gone? Philosophy of Science, 74(2), 195–228.

    Article  Google Scholar 

  • Morrison, M. (2011). One phenomenon, many models: Inconsistency and complementarity. Studies in History and Philosophy of Science., 42, 342–351.

    Article  Google Scholar 

  • Morrison, M. (2015). Reconstructing reality: Models, mathematics, and simulations. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Nersessian, N. J. (2005). Abstraction via generic modeling in concept formation in science. In M. R. Jones & N. Cartwright (Eds.), Idealization XII: Correcting the model (pp. 117–143). Amsterdam: Rodopi.

    Google Scholar 

  • Nielsen, A. V., Gade, A. L., Juul, J., & Strandkvist, C. (2015). Schelling model of cell segregation based only on local information. Physical Review E., 92(5), 052705.

    Article  Google Scholar 

  • Ordorica, S. (2016). The explanatory role of abstraction processes in models: The case of aggregations. Studies in History and Philosophy of Science., 56, 161–167.

    Article  Google Scholar 

  • Pingoud, A., & Jeltsch, A. (2001). Structure and function of type II restriction endonucleases. Nucleic Acids Research., 29(18), 3705–3727.

    Article  Google Scholar 

  • Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology., 1, 143–186.

    Article  Google Scholar 

  • Schelling, T. C. (1978). Micromotives and Macrobehaviour. New York: Norton.

    Google Scholar 

  • Schindler, S. (2008). Model, theory, and evidence in the discovery of the DNA structure. British Journal for the Philosophy of Science., 59(4), 619–658.

    Article  Google Scholar 

  • Strandkvist, C., Juul, J., Baum, B., Kabla, A. J., & Duke, T. (2014). A kinetic mechanism for cell sorting based on local variations in cell motility. Interface Focus., 4(6), 20140013.

    Article  Google Scholar 

  • Toon, A. (2011). Playing with molecules. Studies in History and Philosophy of Science., 42, 580–589.

    Article  Google Scholar 

  • Vinković, D., & Kirman, A. (2006). A physical analogue of the Schelling model. Proceedings of the National Academy of Sciences of the United States of America., 103(51), 19261–19265.

    Article  Google Scholar 

  • Weber, M. (2014). Experimental modeling in biology: In vivo representation and stand-ins as modeling strategies. Philosophy of Science, 81, 756–769.

    Article  Google Scholar 

  • Weisberg, M. (2007). Who is a modeler? British Journal for the Philosophy of Science., 58(2), 207–233.

    Article  Google Scholar 

  • Wimsatt, W. C. (2002). Using false models to elaborate constraints on processes: Blending inheritance in organic and cultural evolution. Philosophy of Science, 69, S12–S24.

    Article  Google Scholar 

  • Xu, Q. S., Roberts, R. J., & Guo, H.-C. (2005). Two crystal forms of the restriction enzyme MspI-DNA complex show the same novel structure. Protein Science., 14(10), 2590–2600.

    Article  Google Scholar 

  • Zhang, J. (2004). Residential segregation in an all-integrationist world. Journal of Economic Behavior & Organization., 54, 533–550.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia

    Sim-Hui Tee

Authors
  1. Sim-Hui Tee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sim-Hui Tee.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tee, SH. Abstraction as an Autonomous Process in Scientific Modeling. Philosophia 48, 789–801 (2020). https://doi.org/10.1007/s11406-019-00092-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11406-019-00092-6

Keywords

Search

Navigation