SpringerLink
Log in

How realist is informational structural realism?

  • Original Research
  • Published:
Synthese Aims and scope Submit manuscript

Abstract

Informational structural realism (ISR) offers a new way to understand the nature of the “structure” that structural realists claim our best scientific theories get right about the world. According to Luciano Floridi, who has given the most detailed formulation of ISR so far, this structure is composed of information representing binary differences. In this paper I assess whether ISR offers a good way to resolve the tension between the no miracle argument (often taken to support scientific realism) and the pessimistic meta-induction (often taken to support antirealism). With regards to this important motivation for structural realism, I shall argue that ISR faces insurmountable difficulties. However, I agree that interpreting “structure” in terms of information can be profitable for the realist. Instead, I offer a new version of ISR that borrows from algorithmic information theory. As a result, a more realist version of ISR is provided.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of data and material

N/A.

Notes

  1. For critical discussion of physical structure, abstract structure, and Ramsey sentences, see Frigg & Votsis (2011). For discussion of partial structures via the semantic approach to theories see Chakravartty (2001) and Landry (2007).

  2. See Otavio Bueno (2010) for an informational version of structural realism that retains van Fraassen’s agnostic stance towards unobservables.

  3. For a detailed explanation of the difference between syntactic and semantic approaches to the nature of scientific theories see Rasmus Grønfeldt Winther (2020).

  4. I am grateful to an anonymous reviewer from Synthese for emphasizing this point.

  5. I wish to thank an anonymous reviewer at Synthese for pointing this out.

  6. For a fuller discussion of this issue as well as his own position, see Krebs (2019).

  7. Majid Davoody Beni has done some work to create a version of ISR he calls “epistemic informational structural realism” (EISR). He agrees that Floridi’s own ISR leans too far in the direction of antirealism (2016). He develops accounts around the idea of biosemiotics (2017) and Shannon’s concept of entropy (2018). These provide interesting alternative versions to the one I give. However, both these new accounts rely on ideas that renders EISR less realist than standard forms of structural realism. For example, biosemiotics defines information in terms of “intentions” (2017, 191–195) and entropy appears to depend on numerous pragmatic constraints (2018, 639–640). For that reason, I think it is important to explore how algorithmic information theory provides a more realist measure.

  8. See Li and Vitányi (2019, 108) for the formal proof.

  9. McAllister (2003) has argued that scientific laws do not provide effective algorithmic compressions of empirical data. For a response see Twardy et al., (2005) as well as Wheeler (2019).

  10. Whilst all structural realists agree that the structure of the world can be known, and that this cuts across the observable/unobservable divide, there are differences held at the level of observables. Some structural realists allow knowledge of non-structural properties of observables whilst others do not. Here I do not take a stance on this debate and ASR could be adopted by advocates of either position.

  11. See Li and Vitányi (2019, 177) for the formal proof.

References

  • Boyd, R. N. (1983). On the Current Status of the Issue of Scientific Realism. Erkenntnis, 19(1/3).

  • Bueno, O. (1997). Empirical Adequacy: A Partial Structures Approach. Studies in History and Philosophy of Science, 28, 585–610.

    Article  Google Scholar 

  • Beni, M. D. (2016). Epistemic Informational Structural Realism. Minds & Machines, 26, 323–339.

    Article  Google Scholar 

  • Beni, M. D. (2017). The Downward Path to Epistemic Informational Structural Realism. Acta Analytica, 33, 181–197.

    Article  Google Scholar 

  • Beni, M. D. (2018). Syntactical Informational Structural Realism. Minds & Machines, 28, 623–643.

    Article  Google Scholar 

  • Braddon-Mitchell, D. (2001). Lossy Laws. Nous, 35(2):260–277.

    Article  Google Scholar 

  • Bueno, O. (2000). Empiricism, Mathematical Change and Scientific Change. Studies in History and Philosophy of Science, 31, 269–296.

    Article  Google Scholar 

  • Carnap, R. (1928). The Logical Structure of the World. University of California Press.

    Google Scholar 

  • Chaitin, G. (1987). Algorithmic Information Theory. Cambridge University Press.

    Book  Google Scholar 

  • Chaitin, G. (2005). Meta Maths - The Quest for Omega. Atlanta Books.

    Google Scholar 

  • Chakravartty, A. (2001). The Semantic or Model-Theoretic View of Theories and Scientific Realism. Synthese, 127, 325–345.

    Article  Google Scholar 

  • da Costa, N. C. A., & French, S. (2003). Science and Partial Truth: A Unitary Approach to Models and Scientific Reasoning. Oxford University Press.

    Book  Google Scholar 

  • Davies, P. (1995). Algorithmic Compressibility, Fundamental and Phenomenological Laws. In F. Weinert (Ed.), Laws of Nature: Essays on the Philosophical, Scientific and Historical Dimensions (pp. 248–267). Walter de Gruyter & Co.

    Google Scholar 

  • Dennett, D. (1991). Real Patterns. Journal of Philosophy, 88(1), 27–51.

    Article  Google Scholar 

  • Floridi, L. (2004). Outline of a Theory of Strongly Semantic Information. Minds and Machines, 14, 197–221.

    Article  Google Scholar 

  • Floridi, L. (2008a). A Defence of Informational Structural Realism. Synthese, 161, 219–253.

    Google Scholar 

  • Floridi, L. (2008b). The Method of Levels of Abstraction. Minds and Machines, 18, 303–329.

    Article  Google Scholar 

  • Floridi, L. (2009). Against Digital Ontology. Synthese, 168, 151–178.

    Article  Google Scholar 

  • Floridi, L. (2011a). Semantic Information and the Correctness Theory of Truth. Erkenntnis, 74, 147–175.

    Google Scholar 

  • Floridi, L. (2011b). The Philosophy of Information. Oxford University Press.

    Book  Google Scholar 

  • Frigg, R., & Votsis, I. (2011). Everything You Always Wanted to Know About Structural Realism but Were Afraid to Ask. European Journal for Philosophy of Science, 1(2), 227–276.

    Article  Google Scholar 

  • Gell-Mann, M. (1987). Simplicity and Complexity in the Description of Nature. Talk Delivered to The Caltech Associates, Pasadena 01/10/987.

  • Grünwald, D. P., & Vitányi, P. (2008). Algorithmic Information Theory. In P. Adriaans, & van J. Benthem (Eds.), Handbook of Philosophy of Science Vol. 8: Philosophy of Information (pp. 289–328). Elsevier.

    Google Scholar 

  • Kolmogorov, A. (1965). Three Approaches to the Definition of the Quantity of Information. Problems of Information Transmission, 1(1), 1–7.

    Google Scholar 

  • Krebs, J. (2011). Philosophy of Information and Pragmatistic Understanding of Information. Etica & Politica / Ethics & Politics, XIII, 2, 235–245.

  • Ladyman, J., & Ross, D. (2007). Everything Must Go: Metaphysics Naturalized. Oxford University Press.

    Book  Google Scholar 

  • Ladyman, J., & Ross, D. (2013). The World in the Data. In J. Ladyman, D. Ross, & H. Kincaid (Eds.), Scientific Metaphysics (pp. 108–150). Oxford University Press.

    Chapter  Google Scholar 

  • Landry, E. (2007). Shared Structure Need not be Shared Set-Structure. Synthese, 158, 1–17.

    Article  Google Scholar 

  • Lewis, D. (2009). Ramseyan Humility. In D. Braddon-Mitchell, & R. Nola (Eds.), The Canberra Programme (pp. 203–222). Oxford University Press.

    Google Scholar 

  • Li, P., & Vitányi, P. (2019). An Introduction to Kolmogorov Complexity and its Applications. Springer.

    Book  Google Scholar 

  • Maxwell, G. (1970a). Structural realism and the meaning of theoretical terms. In S. Winokur, & M. Radner (Eds.), Analyses of Theories and Methods of Physics and Psychology (Minnesota Studies in the Philosophy of Science: Volume 4) (pp. 181–192). University of Minnesota Press.

    Google Scholar 

  • Maxwell, G. (1970b). Theories, perception and structural realism. In R. Colodny (Ed.), The Nature and Function of Scientific Theories (pp. 3–34). University of Pittsburgh Press. University of Pittsburgh Series in the Philosophy of Science: Volume4.

    Google Scholar 

  • Maxwell, G. (1972). Scientific methodology and the causal theory of perception. In H. Feigl, H. Sellars, & K. Lehrer (Eds.), New Readings in Philosophical Analysis (pp. 148–177). Appleton-Century Crofts.

    Google Scholar 

  • McAllister, J. (2003). Algorithmic Randomness in Empirical Data. Studies in the History and Philosophy of Science, 34(3), 633–646.

    Article  Google Scholar 

  • Ross, D. (2000). Rainforest Realism: A Dennettian Theory of Existence. In D. Ross, A. Brook, & D. Thompson (Eds.), Dennett’s Philosophy: A Comprehensive Assessment (pp. 77–94). MIT Press.

    Chapter  Google Scholar 

  • Russell, B. (1927). The Analysis of Matter. Routledge Kegan Paul.

    Google Scholar 

  • Shannon, C., & Weaver, W. (1949). The Mathematical Theory of Information. University of Illinois Press.

    Google Scholar 

  • Solomonoff, R. (1964). A Formal Theory of Inductive Inference: Part I. Information and Control, 7(1), 1–22.

    Article  Google Scholar 

  • Tomkow, T. (2013). The Computational Theory of the Laws of Nature. http://tomkow.typepad.com/tomkowcom/2013/09/the-computational-theory-of-natural-laws.html. Access: 08/06/2019.

  • Tomkow, T. (2014). Computation, Laws and Supervenience. https://tomkow.typepad.com/tomkowcom/2014/02/computation-laws-and-supervenience.html. Access: 08/06/2019.

  • Twardy, C., Gardner, S., & Dowe, D. (2005). Empirical Data Sets are Algorithmically Compressible: Reply to McAllister? Studies in the History and Philosophy of Science, 36(2), 391–402.

    Article  Google Scholar 

  • Wheeler, B. (2019). Compressibility and the Algorithmic Theory of Laws. Principia: An International Journal of Epistemology, 23(3), 461–485.

    Article  Google Scholar 

  • Wheeler, J. A. (1990). Information, physics, quantum: The search for links. In W. H. Zurek (Ed.), Complexity, Entropy, and the Physics of Information. Addison-Wesley.

    Google Scholar 

  • Winther, R. S. (2020). The Structure of Scientific Theories. In Edward N. Zalta (ed.) The Stanford Encyclopedia of Philosophy.URL = < https://plato.stanford.edu/archives/spr2021/entries/structure-scientific-theories/

  • Worrall, J. (1989). Structural Realism: The Best of Both Worlds?.Dialectica, 43, 99–124.

    Article  Google Scholar 

Download references

Acknowledgements

An earlier version of this paper was presented at the conference “Alternative Approaches to Scientific Realism” 12–14 April 2021 at the Munich Center for Mathematical Philosophy. I am very grateful for the constructive comments and suggestions of the attendees. I am also grateful for the insightful recommendations from three anonymous reviewers at Synthese that helped improve the paper in multiple respects.

Funding

The study did not receive any funding that needs acknowledgement.

Author information

Authors and Affiliations

  1. College of Arts and Sciences, VinUniversity, Hanoi, Vietnam

    Billy Wheeler

Authors
  1. Billy Wheeler
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The Corresponding Author is the sole contributor.

Corresponding author

Correspondence to Billy Wheeler.

Ethics declarations

Ethics Approval and Consent to Participate

N/A.

Consent to publication

The author gives consent to publication

Competing interests

There are no financial or non-financial conflicts of interest to declare

Additional information

Publisher’s Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wheeler, B. How realist is informational structural realism?. Synthese 200, 480 (2022). https://doi.org/10.1007/s11229-022-03911-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11229-022-03911-8

Keywords

Search

Navigation