Scientific understanding and felicitous legitimate falsehoods

Abstract

Science is replete with falsehoods that epistemically facilitate understanding by virtue of being the very falsehoods they are. In view of this puzzling fact, some have relaxed the truth requirement on understanding. I offer a factive view of understanding (i.e., the extraction view) that fully accommodates the puzzling fact in four steps: (i) I argue that the question how these falsehoods are related to the phenomenon to be understood and the question how they figure into the content of understanding it are independent. (ii) I argue that the falsehoods do not figure into the understanding’s content by being elements of its periphery or core. (iii) Drawing lessons from case studies, I argue that the falsehoods merely enable understanding. When working with such falsehoods, only the truths we extract from them are elements of the content of our understanding. (iv) I argue that the extraction view is compatible with the thesis that falsehoods can have an epistemic value by virtue of being the very falsehoods they are.

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Notes

  1. 1.

    Understanding language acquisition is an example of so-called objectual understanding. Scientists also want to understand why things are the case. My arguments apply to both forms of understanding. I remain neutral regarding whether understanding why is a form of objectual understanding or vice versa (see, e.g., Grimm 2011; Khalifa 2013; Baumberger and Brun 2017).

  2. 2.

    I do not address any other arguments against factivism, such as arguments from history against factivism, according to which scientists gained understanding based on theories which turned out to be false (see, e.g., de Regt 2015; de Regt and Gijsbers 2017). For a discussion, see, e.g., Khalifa (2017), ch. 6.2.

  3. 3.

    I do not use the term ‘idealization’ as an umbrella term for these falsehoods because it might be too narrow. Some scientific falsehoods are claimed to be fictions rather than idealizations (see, e.g., Godfrey-Smith 2009; Frigg 2010; Bokulich 2012; Sugden 2013). And idealizations are often distinguished from abstractions (see, e.g., Jones 2005) or approximations (see, e.g., Norton 2012). For more on idealizations see, e.g., Elliott-Graves and Weisberg (2014), Potochnik (2017).

  4. 4.

    Another interesting case are falsehoods involved in hypothetical reasoning, such as scientific thought experiments or how-possibly models. I do not consider their characteristics in this paper; but see, e.g., Dray (1957), Grüne-Yanoff (2013), Rohwer and Rice (2013), Sugden (2013), Bokulich (2014), van Riel (2015), Verreault-Julien (2019).

  5. 5.

    It goes without saying that the notion ‘approximately’ is context-sensitive. One has to specify a proximity range for the value of interest. A value is significantly different if it is not equivalent to the value of interest or within its specified proximity.

  6. 6.

    Models are typically not identified as falsehoods for at least two reasons. On the one hand, many models are not something that could be true or false. On the other hand, models typically also involve accurate stipulations about their target phenomena.

  7. 7.

    I thank an anonymous reviewer for emphasizing this point.

  8. 8.

    Frigg and Nguyen’s representation-as account differs in at least one important respect. According to them, the properties exemplified by a model are typically not the ones that are imputed to the phenomena of interest (Frigg and Nguyen 2018, p. 217). They propose a detailed account of how the exemplified properties are related to the imputed ones via so-called specification keys (cf. Frigg and Nguyen 2018, sect. 5–7).

  9. 9.

    For more on these and other accounts see, e.g., Suárez (2010), Downes (2011), Frigg and Hartmann (2012).

  10. 10.

    Some variants of the inferential account conflate these questions when it comes to explanation (see, e.g., Kennedy 2012; Jebeile and Kennedy 2015; Fang 2019). They would thus, arguably, also conflate them when it comes to the content of understanding.

  11. 11.

    This law is a combination of Boyle’s law, Charles’s law, Gay-Lussac’s law, and Avogadro’s law. There are more refined forms of the law, such as the van der Waals equation, but the differences do not matter for my purposes.

  12. 12.

    There is more than one veridical counterpart for every distorting falsehood. For instance, the collision frequency of gas molecules can be described to varying degrees of accuracy.

  13. 13.

    Some argue that the thermodynamic limit is dispensable (e.g., Butterfield 2011; Norton 2012). For some useful discussion see, e.g., Shech (2013, 2017). I take for granted that it is necessary.

  14. 14.

    Rice considers his view to be a form of factivism (Rice 2016). However, I use the term ‘factivism’ exclusively for views that demand that all propositions be true.

  15. 15.

    For other explanations why scientists use multiple models for examining the same phenomenon see, e.g., Morrison (1999), Bailer-Jones (2003), Weisberg (2007), Elliott-Graves and Weisberg (2014).

  16. 16.

    This should not be surprising. It is precisely because idealized models involve distorting falsehoods that they do not provide us with accurate results. For instance, the effect of collisions between gas molecules has some effect on the behavior of the gas.

  17. 17.

    This argumentative step is also at the heart of Rice’s quasi-factivist view (Rice 2016). He argues that accurately representing the real-world target system in question is not required for understanding.

  18. 18.

    As Elgin emphasized in conversation, one might separately discuss whether the falsehoods figure into the justification of the understanding’s contents. I do not consider this possibility here. But in light of my arguments against non-factivism, I do not expect that this is the case.

  19. 19.

    Note that this result is not specific to understanding. It also matters for debates about model explanation, etc.

  20. 20.

    Recall that the justification question need not collapse into the relation question.

  21. 21.

    Contra Alexandrova, the information we extract from models is typically not the starting point of working with the model but the end. It is because of the model’s empirical success that we have a good reason to believe that the extracted information holds true for real-world phenomena.

  22. 22.

    The extraction view is nonetheless substantially different from the non-difference-maker view of scientific falsehoods in at least two respects. (a) According to the extraction view, there is no need for the falsehoods to be rendered harmless. They do not need to point to non-difference makers. It only matters that they are legitimate and that they can be used to extract relevant information. (b) The non-difference-maker view is crucially limited. Strevens assumes that the non-idealized parts of a model describe the difference-makers for the explanatory target and the idealized parts point to non-difference makers (Strevens 2008, p. 318):

    The content of an idealized model, then, can be divided into two parts. The first part contains the difference-makers for the explanatory target. [...] The second part is all idealization; its overt claims are false but its role is to point to parts of the actual world that do not make a difference to the explanatory target

    This account cannot cope with cases where the idealizations are indispensable for extracting the explanatory information. In such cases, the non-idealized parts do not contain all the difference-makers for the phenomenon to be explained. Instead, one extracts information about the relevant difference-makers from the falsehoods. The account can also not cope with cases of falsehoods that distort difference-makers. For examples of both cases see, e.g., Rice (2018). The extraction view can easily cope with these cases.

  23. 23.

    This metaphor is meant to be compatible with ‘re-using the ladder to reach the top,’ i.e., with re-using the falsehoods to rebuild one’s understanding or to help others understanding the phenomena. I thank an anonymous reviewer for emphasizing this point.

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Acknowledgements

Discussions with colleagues and advisors contributed to shaping the view that I defend in this article. I’m grateful to (in alphabetical order) Christoph Baumberger, Jochen Briesen and his students, Henk de Regt, Finnur Dellsén, Anna-Maria Asunta Eder, Catherine Elgin, Benjamin Feintzeig, Roman Frigg, Philipp Haueis, Christoph Jäger, Marie I. Kaiser, Kareem Khalifa, Federica Malfatti, Christian Nimtz, Thomas Spitzley, Michael Strevens, Emily Sullivan, Raphael van Riel, Kate Vredenburgh, and the participants of Thomas Spitzley’s and Christian Nimtz’s research groups. I also thank the audiences in Aarhus, Atlanta, Barcelona, Bochum, Bordeaux, Exeter, Ghent, Greensboro, Innsbruck, Pärnu, and Seattle, as well as three anonymous reviewers for their constructive criticisms and suggestions.

Funding

I gratefully acknowledge that part of my research for this article was funded by the Volkswagen Foundation for the project ’A Study in Explanatory Power’, by the German Academic Exchange Service (DAAD) for a research stay at the New York University (2015–2016), by the OeAD for an Ernst Mach Scholarship, and by an Emmy Noether Grant from the German Research Council (DFG), Reference No. BR 5210/1-1.

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Correspondence to Insa Lawler.

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Lawler, I. Scientific understanding and felicitous legitimate falsehoods. Synthese (2019). https://doi.org/10.1007/s11229-019-02495-0

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Keywords

  • Scientific understanding
  • Factivism
  • Quasi-factivism
  • Non-factivism
  • Felicitous falsehoods
  • Idealizations
  • Idealized models