Skip to main content
SpringerLink
Log in

Exploratory modeling and indeterminacy in the search for life

  • Paper in General Philosophy of Science
  • Published:
European Journal for Philosophy of Science Aims and scope Submit manuscript

Abstract

The aim of this article is to use a model from the origin of life studies to provide some depth and detail to our understanding of exploratory models by suggesting that some of these models should be understood as indeterminate. Models that are indeterminate are a type of exploratory model and therefore have extensive potential and can prompt new lines of research. They are distinctive in that, given the current state of scientific understanding, we cannot specify how and where the model will be useful in understanding the natural world: in this case, the origin of life on Earth. The purpose of introducing indeterminacy is to emphasize the epistemic uncertainty associated with modeling, a feature of this practice that has been under emphasized in the literature in favor of attempts to understand the more specific epistemic successes afforded by models.

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.

Institutional subscriptions

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

Notes

  1. A number of authors discuss models without targets and how such models relate to various accounts of modeling is intensely complex (Contessa, 2011; French, 2003; French, 2014; Frigg & Nguyen, 2016; Gelfert, 2018; Giere, 2010; Knuuttila, 2005; Massimi, 2018; Massimi, 2019; Poznic, 2016; Suárez, 2003)

  2. See Neveu et al., 2013 for an overview of the advantages of the view

  3. See Bernhardt, 2012 for an overview of some of the problems with the RNA-first hypothesis.

  4. Whether and to what extent it provides this proof is open to question. See Lancet et al., 2018 for some discussion of the criticisms of the model and how those criticisms might be ameliorated.

  5. There are many ways to adjust this equation and many versions of it. Choice of version depends in part on what assumptions and what levels of precision researchers are interested in exploring. This particular version is used by Segré et al. (1998a). I discuss it here because it is the original and the first demonstration of a compositional genome. For a brief history of the model, see Lancet et al. (2019). A more recent version can be found in Lancet et al. (2018), a version which links the model dynamics more closely with entropy, a topic beyond the scope of this article.

  6. The model is not heuristic if by the term we mean a tool that is merely convenient for achieving some task, but which we think has no truth or real-world significance behind it. However, heuristic models are not well defined, so there is scope for taking a different position on this matter if one were to take an alternative stance toward what it means to be heuristic.

  7. Reydon and the other authors here are working within a deductive-nomological framework (Hempel & Oppenheim, 1948). In this framework, one uses initial conditions and covering laws to generate predictions. One achieves an explanation for a phenomena when one is able to use those initial conditions and laws to accurately predict that phenomenon.

References

  • Armstrong, D., Markovitch, O., Zidovetzki, R., & Lancet, D. (2011). Replication of simulated prebiotic amphiphile vesicles controlled by experimental lipid physicochemical properties. Physical Biology, 8(6), 66001.

    Article  Google Scholar 

  • Bernhardt, H. (2012). The RNA world hypothesis: The worst theory of the early evolution of life (except for all the others). Biology Direct, 7(23), 23.

    Article  Google Scholar 

  • Bokulich, A. (2011). How scientific models can explain. Synthese, 180(1), 33–45.

    Article  Google Scholar 

  • Bokulich, A. (2012). Distinguishing explanatory from nonexplanatory fictions. Philosophy of Science, 79(5), 725–737.

    Article  Google Scholar 

  • Bokulich, A. (2014). How the Tiger bush got its stripes: How Possibly’ vs. how actually model explanations. Monist, 97(3), 321–338.

    Article  Google Scholar 

  • Brandon, R. (1990). Adaptation and environment. Princeton University Press.

    Google Scholar 

  • Contessa, G. (2011). Scientific models and representation. In S. French & J. Saatsi (Eds.), The continuum companion to the philosophy of science. Continuum international publishing group.

    Google Scholar 

  • Fisher, G. (2006). The autonomy of models and explanation: Anomalous molecular rearrangements in early twentieth-century physical organic chemistry. Studies in History and Philosophy of Science Part A, 37(4), 562–584.

    Article  Google Scholar 

  • Forber, P. (2010). Confirmation and explaining how possible. Studies in History and Philosophy of Science Part C :Studies in History and Philosophy of Biological and Biomedical Sciences, 41(1), 32–40.

    Article  Google Scholar 

  • French, S. (2003). A model-theoretic account of representation (or, I don't know much about art but I know it involves isomorphism). Philosophy of Science, 70(5), 1472–1483.

    Article  Google Scholar 

  • French, S. (2014). The structure of the world: Metaphysics and representation. Oxford University Press.

    Book  Google Scholar 

  • Frigg, R. (2010). Fiction and scientific representation. In R. Frigg & M. Hunter (Eds.), Beyond mimesis and convention: Representation in art and science. 97–138. Springer.

    Chapter  Google Scholar 

  • Frigg, R., & Nguyen, J. (2016). The fiction view of models reloaded. The Monist, 99(3), 225–242.

    Article  Google Scholar 

  • Fumagalli, R. (2015). No learning from minimal models. Philosophy of Science, 82(5), 798–809.

    Article  Google Scholar 

  • Fumagalli, R. (2016). Why we cannot learn from minimal models. Erkenntnis, 81(3), 433–455.

    Article  Google Scholar 

  • Gelfert, A. (2018). Models in search of targets: Exploratory modelling and the case of turing patterns. In A. Christian, D. Hommen, N. Retzlaff, & G. Schurz (Eds.), Philosophy of science. European studies in philosophy of science (vol. 9). Springer. https://doi.org/10.1007/978-3-319-72577-2_14

  • Giere, R. (2004). How models are used to represent reality. Philosophy of Science, 71(5), 742–752.

    Article  Google Scholar 

  • Giere, R. (2010). An agent-based conception of models and scientific representation. Synthese, 172(2), 269–281.

    Article  Google Scholar 

  • Godfrey-Smith, P. (2006). The strategy of model-based science. Biology and Philosophy, 21(5), 725–740.

    Article  Google Scholar 

  • Godfrey-Smith, P. (2009). Models and fictions in science. Philosophical Studies, 143(1), 101–116.

    Article  Google Scholar 

  • Gould, S. (1978). Sociobiology: The art of storytelling. New Scientist, 80(1129), 530–533.

    Google Scholar 

  • Hempel, C., & Oppenheim, P. (1948). Studies in the logic of explanation. Philosophy of Science, 15(2), 135–175.

    Article  Google Scholar 

  • Kahana, A., Schmitt-Kopplin, P., & Lancet, D. (2019). Enceladus: First observed primordial soup could arbitrate origin-of-life debate. Astrobiology, 19(10), 1263–1278.

    Article  Google Scholar 

  • Knuuttila, T., & Loettgers, A. (2013). Synthetic modeling and mechanistic account: Material recombination and beyond. Philosophy of Science, 80(5), 874–885.

    Article  Google Scholar 

  • Knuuttila, T. T. (2005). Models as epistemic artefacts: Toward a non-representationalist account of scientific representation. (Philosophical Studies from the University of Helsinki; No. 8). Edita Prima.

  • Knuuttila, T. (2009). Some consequences of the pragmatist approach to representation. In M. Suárez, M. Dorato, & M. Rédei (Eds.), EPSA epistemology and methodology of science. Springer. https://doi.org/10.1007/978-90-481-3263-8_12

  • Knuuttila, T. (2011). Modeling and representing: An artefactual approach to model-based representation. Studies in History and Philosophy of Science Part A, 42(2), 262–271.

    Article  Google Scholar 

  • Knuuttila, T. (2017). Imagination extended and embedded: Artifactual versus fictional accounts of models. Synthese, 198, 5077–5097.

    Article  Google Scholar 

  • Knuuttila, T., & Koskinen, R. (2021). Synthetic fictions: Turning imagined biological systems into concrete ones. Synthese, 198(9), 1–18.

    Article  Google Scholar 

  • Knuuttila, T., &  Loettgers, A. (2013). Synthetic biology as an engineering science? analogical reasoning, synthetic modeling, and integration. In H. Andersen, D. Dieks, W. Gonzalez, T. Uebel, & G. Wheeler (Eds.), New challenges to philosophy of science. The philosophy of science in a European perspective (vol. 4). Springer. https://doi.org/10.1007/978-94-007-5845-2_14

  • Knuuttila, T.. &  Loettgers, A. (2016). Contrasting cases: The Lotka-Volterra model times three.  In the philosophy of historical case studies (vol. 319, pp. 151–178). Springer

  • Knuuttila, T., & Loettgers, A. (2017). Modeling as indirect representation? The Lotka-Volterra model revisited. The British Journal for the Philosophy of Science, 68(4), 1007–1036.

    Article  Google Scholar 

  • Lancet, D., Segrè, D., & Kahana, A. (2019). Twenty years of lipid world: A fertile partnership with David Deamer. Life, 9(4), 77.

    Article  Google Scholar 

  • Lancet, D., Zidovetzki, R., & Markovitch, O. (2018). Systems protobiology: Origin of life in lipid catalytic networks. Journal of the Royal Society Interface, 15(144), 20180159.

    Article  Google Scholar 

  • Lanier, K., & Williams, L. (2017). The origin of life: Models and data. Journal of Molecular Evolution, 84(2–3), 85–92.

    Article  Google Scholar 

  • Massimi, M. (2018). Perspectival modeling. Philosophy of Science, 85(3), 335–359.

    Article  Google Scholar 

  • Massimi, M. (2019). Two kinds of exploratory models. Philosophy of Science, 86(5), 869–881.

    Article  Google Scholar 

  • Morrison, M. (1999). Models as autonomous agents. Ideas in Context, 52, 38–65.

    Google Scholar 

  • Morrison, M., & Morgan, M. (1999). Models as mediators : Perspectives on natural and social sciences. Cambridge University Press.

    Google Scholar 

  • Neveu, M., Kim, H.-J., & Benner, S. A. (2013). The strong RNA world hypothesis: Fifty years old. Astrobiology, 13(4), 391–403.

    Article  Google Scholar 

  • O'Hara, R. (1988). Homage to Clio, or, toward an historical philosophy for evolutionary biology. Systematic Zoology, 37(2), 142–155.

    Article  Google Scholar 

  • Poznic, M. (2016). Representation and similarity: Suárez on necessary and sufficient conditions of scientific representation. Journal for General Philosophy of Science., 47, 331–347.

    Article  Google Scholar 

  • Reydon, T. (2012). How-possibly explanations as genuine explanations and helpful heuristics: A comment on Forber. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43(1), 302–310.

    Article  Google Scholar 

  • Salis, F., & Frigg, R. (2010). Capturing the scientific imagination. The scientific imagination, 17–50.

  • Segré, D., Ben-Eli, D., Deamer, D., & Lancet, D. (2001). The lipid world. Origins of life and evolution of the biosphere : the journal of the International Society for the Study of the Origin of Life, 31, 119–145.

    Article  Google Scholar 

  • Segré, D., Ben-Eli, D., & Lancet, D. (2000). Compositional genomes: Prebiotic information transfer in mutually catalytic noncovalent assemblies. Proceedings of the National Academy of Sciences, 97(8), 4112 LP–4114117.

    Article  Google Scholar 

  • Segré, D., Lancet, D., Kedem, O., & Pilpel, Y. (1998). Graded autocatalysis replication domain (GARD): Kinetic analysis of self-replication in mutually catalytic sets. Origins of Life and Evolution of the Biosphere, 28(4–6), 501–514.

    Article  Google Scholar 

  • Segré, D., Pilpel, Y., & Lancet, D. (1998). Mutual catalysis in sets of prebiotic organic molecules: Evolution through computer simulated chemical kinetics. Physica A: Statistical Mechanics and its Applications, 249(1–4), 558–564.

    Article  Google Scholar 

  • Suárez, M. (2003). Scientific representation: Against similarity and isomorphism. International Studies in the Philosophy of Science, 17(3), 225–244.

    Article  Google Scholar 

  • Suárez, M. (2004). An inferential conception of scientific representation. Philosophy of Science, 71(5), 767–779.

    Article  Google Scholar 

  • Suárez, M. (2010). Scientific Representation. Philosophy Compass, 1(5), 91–101.

    Article  Google Scholar 

  • Thomasson, A. (1999). Fiction and metaphysics. Cambridge University Press.

    Google Scholar 

  • Toon, A. (2012). Models as make-believe: Imagination, fiction and scientific representation. Springer.

    Book  Google Scholar 

  • Toon, A. (2016). Imagination in scientific modeling. In A. Kind (Ed.), The Routledge handbook of philosophy of imagination. Routledge.

    Google Scholar 

  • van Fraassen, B. (1980). The scientific image. Oxford University Press.

    Book  Google Scholar 

  • van Fraassen, B. (2008). Scientific Representation. Oxford University Press.

    Book  Google Scholar 

  • Verreault-Julien, P. (2019). How could models possibly provide how-possibly explanations? Studies in History and Philosophy of Science Part A, 73, 22–33.

    Article  Google Scholar 

  • Walton, K. (1990). Mimesis as make-believe: On the foundations of the representational arts. Harvard University Press.

    Google Scholar 

Download references

Funding

This work was conducted with the generous support of a Junior Fellowship at the Institute for Cross-Disciplinary Engagement at Dartmouth College, 6127Wilder Hall, Hanover, New Hampshire, 03755 USA.

Author information

Authors and Affiliations

  1. Visiting Instructor, College of the Atlantic, Bar Harbor, Maine, USA

    Franklin R Jacoby

Authors
  1. Franklin R Jacoby
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

This work was entirely authored by Dr. Jacoby, who takes full responsibility for its content.

Corresponding author

Correspondence to Franklin R Jacoby.

Ethics declarations

Conflict of interest

Not applicable.

Additional information

Publisher’s note

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

Hear and attend and listen; for this befell and behappened and became and was.

—Rudyard Kipling, Just So Stories

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jacoby, F.R. Exploratory modeling and indeterminacy in the search for life. Euro Jnl Phil Sci 12, 37 (2022). https://doi.org/10.1007/s13194-022-00469-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13194-022-00469-7

Keywords

Search

Navigation