Abstract
It is often claimed—especially by scientific realists—that science provides understanding of the world only if its theories are (at least approximately) true descriptions of reality, in its observable as well as unobservable aspects. This paper critically examines this ‘realist thesis’ concerning understanding. A crucial problem for the realist thesis is that (as study of the history and practice of science reveals) understanding is frequently obtained via theories and models that appear to be highly unrealistic or even completely fictional. So we face the dilemma of either giving up the realist thesis that understanding requires truth, or allowing for the possibility that in many if not all practical cases we do not have scientific understanding. I will argue that the first horn is preferable: the link between understanding and truth can be severed. This becomes a live option if we abandon the traditional view that scientific understanding is a special type of knowledge. While this view implies that understanding must be factive, I avoid this implication by identifying understanding with a skill rather than with knowledge. I will develop the idea that understanding phenomena consists in the ability to use a theory to generate predictions of the target system’s behavior. This implies that the crucial condition for understanding is not truth but intelligibility of the theory, where intelligibility is defined as the value that scientists attribute to the theoretical virtues that facilitate the construction of models of the phenomena. I will show, first, that my account accords with the way practicing scientists conceive of understanding, and second, that it allows for the use of idealized or fictional models and theories in achieving understanding.
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Notes
Many alternative conceptions of realism have been proposed since The Scientific Image appeared, most of which are weaker than the version characterized by van Fraassen (e.g. structural realism, entity realism, constructive realism). van Fraassen’s definition is a suitable starting-point for the discussion, however.
Cf. Pearson (Pearson 1957 [1911], p. 302): “the object of science [...] is not to explain but to describe by conceptual shorthand our perceptual experience.”
van Fraassen has even developed a full-fledged theory of explanation; see Chap. 5 of The Scientific Image (1980).
The account of models as epistemic artefacts is also defended by (Knuuttila and Merz 2009), who support it by cases studies of computer models used in particle physics (event generators) and linguistics (constraint grammar parsers). For a different view on how economists achieve understanding through modeling, see Lehtinen and Kuorikoski (2007).
A similar analysis is provided by Lenhard (2006), who discusses how computer simulations in nanoscience provide understanding. Against the objection of their ‘epistemic opacity’ he argues that they can still result “the ability to control phenomena, to gain power to perform interventions, and thus to gain pragmatic understanding.” (Lenhard 2006, p. 613).
Lipton (2009, pp. 49–52) presents interesting arguments for the thesis that potential (how-possibly) explanations can provide understanding. His view is compatible with my analysis.
See Leonelli (2009) for a general account of understanding in biology, illustrated with an analysis of how model organisms and their virtual (digitalized) counterparts furnish understanding.
See Grimm (2006) for a discussion of the contrast between understanding-as-knowledge and understanding-as-skill conceptions. As Grimm (2006, pp. 532–533) observes, understanding might turn out to be a combination of knowledge and skill. See also Khalifa (2012) and Wilkenfeld (2013) for discussions of understanding as an ability.
In the well-known party game ‘Truth or Dare?’ a player is given the choice between answering a question truthfully and performing a task successfully. The other players provide the question or task, which may be embarrassing or difficult, only after the first player has made a choice.
This view is elaborated in more detail in De Regt (2009).
I owe this example to Paul Teller (workshop presentation, Bochum, 25 April 2013).
Teller (2009, pp. 243–244). Note that both representations are incorrect from the viewpoint of fundamental physics, if we take the ontology of quantum field theory as basic. Thanks to Paul Teller for pointing this out.
Hempel (1965, p. 248) mentions the case of phlogiston as an example of a potential explanation that fails the empirical condition of adequacy.
See Hankins (1985, pp. 94–100) for a summary of phlogiston theory and the transition to oxygen theory. See Chang (2010) and Allchin (1997) for sophisticated accounts of the historical case of phlogiston, showing that the transition was more gradual than traditionally assumed, and for criticisms of philosophers’ use of the case.
This may seem similar to what Salmon (1984) has dubbed the ‘epistemic conception of explanation’, which would imply that adherents of the alternative ‘ontic conception’ (among whom Salmon counted himself) would disagree. However, Salmon’s distinction is misleading: explanations, also Salmon’s causal-mechanical ones, are always epistemic and not ontic, in the sense that they are items of knowledge. I submit that any explanation is a structured epistemic item (and not merely a list of disconnected facts) and can therefore be regarded as an argument in the broad sense. See also Schurz and Lambert (1994) for a defense of the idea that explanations are arguments in a broad sense.
Explanations are arguments in a broad sense; they are not necessarily linguistic. Explanations can take on a variety of forms: pictorial representations, for example, can be part of explanations; these may count as arguments as well.
This paragraph summarizes the theory of understanding and intelligibility I have presented elsewhere in more detail; see De Regt (2009).
See De Regt (2001) for an extensive account of the discovery of electron spin and the intelligibility of visualizable models in quantum theory.
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Acknowledgments
An earlier version of this paper was presented at the workshop Understanding through Modeling: Epistemology, Semantics, and Metaphysics of ‘Inadequate’ Representation, Ruhr Universität Bochum, 25–25 April 2013. Thanks to Raphael van Riel and Markus Eronen for organizing this workshop and for editing this special issue. I thank the other participants in the workshop for stimulating talks and discussions. Special thanks to Kathrin Hönig, Paul Teller, the members of the VU research group Philosophy of Science and Technology, and two anonymous referees for commenting on earlier versions of this paper.
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de Regt, H.W. Scientific understanding: truth or dare?. Synthese 192, 3781–3797 (2015). https://doi.org/10.1007/s11229-014-0538-7
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DOI: https://doi.org/10.1007/s11229-014-0538-7