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Factive scientific understanding without accurate representation

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Abstract

This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the (difference-making) features of their real-world target system(s). My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves.

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Notes

  1. While Strevens’s account does allow some idealized models to explain, accurate representation continues to play a key role since, “the overlap between an idealized model and reality…is a standalone set of difference-makers for the target” (Strevens 2009, p. 318).

  2. Indeed, mechanistic accounts of explanation typically include strong accurate representation requirements in order for a model to explain (Craver 2006; Kaplan and Craver 2011). For example, David Kaplan and Carl Craver argue for a model-to-mechanism-mapping (3 M) requirement that involves various kinds of “correspondence” between the model and the actual causal mechanisms in the model’s target system (Kaplan 2011, p. 347).

  3. However, Levy (2012) goes on to argue that if we understand fictions in a particular way we can maintain realism.

  4. In addition, Pritchard (2009), Grimm (2006), and Kvanvig (2003) all argue for a factive notion of understanding that requires at least some of the propositions in one’s understanding be true.

  5. In what follows I adopt something similar to counterfactual accounts of explanation that focus on a model’s ability to answer what-if-things-had-been-different questions (Bokulich 2011, 2012; Rice 2015; Woodward 2003). However, my goal here is not to argue for a particular account of scientific explanation, but to investigate how the information provided by idealized models is able to produce factive scientific understanding.

  6. Here I explicitly put things in terms of counterfactual (or modal) relevance and irrelevance in order to remain neutral on the need to reveal causal relations in order to provide understanding of a phenomenon. Indeed, it has been argued that optimization models provide noncausal explanations (Rice 2012, 2015), that natural selection is not a cause (Walsh et al. 2002; Matthen and Ariew 2009), and that only counterfactual information (but not causal information) is required for explanation (Bokulich 2011, 2012; Rice 2015). In order to remain as neutral as possible in this paper, I maintain the focus on counterfactual relevance, which can encompass both causal and noncausal kinds of relevance.

  7. For considerations of space I do not work through all the detailed equations of the model (those interested should see Schmid-Hempel et al.’s original article).

  8. Of course, just how one understands what it means to “accurately represent” matters here. For example, one could argue that these models accurately represent the counterfactual structure of the phenomenon, but misrepresent (e.g. with a fictional ontology) the entities, relations, and processes of that phenomenon. If this could be maintained, then one might claim that these models produce this factive understanding only by accurately representing these counterfactual relations. However, I suggest that the distortion of the kinds of entities, relationships, and difference-making processes of their target phenomena entails that these models also distort the counterfactual structure of the target phenomena in some way. That is, this counterfactual structure cannot be easily isolated from the entities, relations, and processes of the phenomenon. Instead, that true counterfactual information typically has to be inferred by the scientific modelers who incorporate the results of the model into their larger set of background beliefs and assumptions. That is, the true counterfactual structure cannot be simply “read off” the idealized model that mirrors that structure.

  9. In other cases, a model might be used to draw inferences about real-world systems even when the modeler is unsure whether the assumptions of the model are true or false. Alternatively, a model might lead to some false implications along with some true ones. All of these are important cases that require additional analysis. What I wish to focus on here, however, is that even when scientists know which assumptions of their models are false, when those idealizations play essential roles in the model and cannot be replaced with alternative assumptions, precisely how they are able to acquire factive understanding requires detailed analysis of particular cases. The various additional ways that this might occur will have to be analyzed in future work.

  10. These hypothetical models are similar to what other authors have called “toy” models in that they are highly simplified and aim to provide insights concerning only a few key aspects of the target phenomenon. However, while many toy models (e.g. the Ising model) are claimed to capture the crucial features that produce the phenomenon, not all hypothetical models will achieve this goal. Therefore, while they are certainly related, hypothetical models are not the same as toy models. Toy models may, however, be a species of hypothetical model that accomplishes particular goals for the modeler.

  11. Odenbaugh (2005) also discusses the use of idealized models to explore possibilities and answer how-possibly questions.

  12. In addition, one way to think about the difference is that system-specific models will typically yield more counterfactual information and that information will be focused on more specific changes to the actual features of the system. Hypothetical modeling, in contrast, will often explore the possibility space in which features differ more dramatically from those of the actual system. These differences may be important for certain kinds of understanding, but my goal here is to highlight the similarities in the ways that these two kinds of models produce a similar cognitive achievement by providing similar kinds of information.

  13. However, this version of realism may only be applicable to model-based theorizing.

  14. While a complete remodeling of the debate over scientific realism may not be required, these additional questions ought to be part of the story or at least need to be investigated before we can adequately determine whether scientific inquiry can provide the kind of truth and continuity that is typically required of realism.

References

  • Achinstein P (1983) The nature of explanation. Oxford University Press, New York

    Google Scholar 

  • Batterman RW (2002) The devil in the details: asymptotic reasoning in explanation, reduction, and emergence. Oxford University Press, Oxford

    Google Scholar 

  • Batterman RW (2009) Idealization and modeling. Synthese 169(3):427–446

    Article  Google Scholar 

  • Batterman RW, Rice C (2014) Minimal model explanations. Philos Sci 81(3):349–376

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bokulich A (2012) Distinguishing explanatory from nonexplanatory fictions. Philos Sci 79:725–737

    Article  Google Scholar 

  • Bueno O, Colyvan M (2011) An inferential conception of the application of mathematics. Noûs 45(2):345–374

    Article  Google Scholar 

  • Carruthers P (2006) The architecture of the mind: massive modularity and the flexibility of thought. Oxford University Press, Oxford

    Book  Google Scholar 

  • Cartwright N (1983) How the laws of physics lie. Oxford University Press, New York

    Book  Google Scholar 

  • Churchland P (2013) Touching a nerve: self as brain. Norton, New York

    Google Scholar 

  • Corsano G, Montagna JM, Iribarren O, Aguirre P (2009) Mathematical modeling approaches for optimization of chemical processes. Nova Science Publishers, New York

    Google Scholar 

  • Craver CF (2006) When mechanistic models explain. Synthese 153(3):355–376

    Article  Google Scholar 

  • de Regt HW (2009a) The epistemic value of understanding. Philos Sci 76(5):585–597

    Article  Google Scholar 

  • de Regt HW (2009b) Understanding and scientific explanation. In: de Regt HW, Leonelli S, Eigner K (eds) Scientific understanding. University of Pittsburgh Press, Pittsburgh, pp 21–42

    Google Scholar 

  • Elgin CZ (2007) Understanding and the facts. Philos Stud 132:33–42

    Article  Google Scholar 

  • Elgin M, Sober E (2002) Cartwright on explanation and idealization. Erkenntnis 57:441–450

    Article  Google Scholar 

  • Eshel I, Feldman MW (2001) Optimality and evolutionary stability under short-term and long-term selection. In: Orzack SH, Sober E (eds) Adaptationism and optimality. Cambridge University Press, Cambridge, pp 114–160

    Google Scholar 

  • Forber P (2010) Confirmation and explaining how possible. Stud Hist Philos Biol Biomed Sci 41:32–40

    Article  Google Scholar 

  • Friedman M (1974) Explanation and scientific understanding. J Philos 71(1):5–19

    Article  Google Scholar 

  • Godfrey-Smith P (2009) Models and fictions in science. Philos Stud 143:101–116

    Article  Google Scholar 

  • Gopnik A, Meltzoff AN (1997) Words, thoughts, and theories. MIT Press, Cambridge

    Google Scholar 

  • Grimm S (2006) Is understanding a species of knowledge? Br J Philos Sci 57:515–535

    Article  Google Scholar 

  • Grimm S (2008) Explanatory inquiry and the need for explanation. Br J Philos Sci 59(3):481–497

    Article  Google Scholar 

  • Grimm S (2012) The value of understanding. Philos Compass 7(2):103–117

    Article  Google Scholar 

  • Hartmann AK, Rieger H (2002) Optimization algorithms in physics. Wiley, Berlin

    Google Scholar 

  • Hempel C (1965) Aspects of scientific explanation. Free Press, New York

    Google Scholar 

  • Kaplan DM (2011) Explanation and description in computational neuroscience. Synthese 183:339–373

    Article  Google Scholar 

  • Kaplan DM, Craver CF (2011) The explanatory force of dynamical and mathematical models in neuroscience: a mechanistic perspective. Philos Sci 78:601–627

    Article  Google Scholar 

  • Khalifa K (2012) Inaugurating understanding or repackaging explanation? Philos Sci 79:15–37

    Article  Google Scholar 

  • Khalifa K (2013) The role of explanation in understanding. Br J Philos Sci 64:161–187

    Article  Google Scholar 

  • Kitcher P (1981) Explanatory unification. Philos Sci 48:507–531

    Article  Google Scholar 

  • Kitcher P (1993) The advancement of science. OUP, New York

    Google Scholar 

  • Kitcher P, Salmon WC (1989) Scientific explanation. University of Minnesota Press, Minneapolis

    Google Scholar 

  • Kvanvig JL (2003) The value of knowledge and the pursuit of understanding. Cambridge University Press, New York

    Book  Google Scholar 

  • Kvanvig JL (2009) Responses to critics. In: Haddock A, Millar A, Pritchard D (eds) Epistemic value. Oxford University Press, New York, pp 339–353

    Google Scholar 

  • Ladyman J, Ross D, Spurrett D, Collier J (2007) Every thing must go: metaphysics naturalised. Oxford University Press, Oxford

    Book  Google Scholar 

  • Levy A (2011) Makes a difference. Review of Michael Strevens’ depth. Biol Philos 26:459–467

    Article  Google Scholar 

  • Levy A (2012) Models, fictions, and realism: two packages. Philos Sci 79(5):738–748

    Article  Google Scholar 

  • Lewis D (1986) Philosophical papers II. Oxford University Press, New York

    Google Scholar 

  • Lipton P (2009) Understanding without explanation. In: de Regt HW, Leonelli S, Eigner K (eds) Scientific understanding: philosophical perspectives. University of Pittsburgh Press, Pittsburgh, pp 43–63

  • Machery E (2009) Doing without concepts. Oxford University Press, Oxford

    Book  Google Scholar 

  • Mäki U (2011a) The truth of false idealizations in modelling. In: Humphreys Paul, Imbert Cyrille (eds) Representations, models and simulations. Routledge, London

    Google Scholar 

  • Mäki U (2011b) Models and the locus of their truth. Synthese 180:47–63

    Article  Google Scholar 

  • Matthen M, Ariew A (2009) Selection and causation. Philos Sci 76(2):201–224

    Article  Google Scholar 

  • Maynard Smith J (1978) Optimization theory in evolution. Annu Rev Ecol Syst 9(1):31–56

    Article  Google Scholar 

  • Maynard Smith J (1982) Evolution and the theory of games. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Maynard Smith J, Price GA (1973) The logic of animal conflict. Nature 246:15–18

    Article  Google Scholar 

  • McMullin E (1985) Galilean idealization. Stud Hist Philos Sci 16:247–273

    Article  Google Scholar 

  • Mizrahi M (2012) Idealizations and scientific understanding. Philos Stud 160:237–252

    Article  Google Scholar 

  • Morrison M (2009) Understanding in physics and biology. In: de Regt Henk W, Leonelli Sabina, Eigner Kai (eds) Scientific understanding: philosophical perspectives. Pittsburgh University Press, Pittsburgh

    Google Scholar 

  • Nozick R (1981) Philosophical explanations. Harvard University Press, Cambridge

    Google Scholar 

  • Odenbaugh J (2005) Idealized, inaccurate, but successful: a pragmatic approach to evaluating models in theoretical ecology. Biol Philos 20:231–255

    Article  Google Scholar 

  • Odenbaugh J (2011) True lies: realism, robustness, and models. Philos Sci 78:1177–1188

    Article  Google Scholar 

  • Orzack SH, Sober E (2001) Adaptationism and optimality. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Peters D (2014) What elements of successful scientific theories are the correct targets for “selective” scientific realism? Philos Sci 81:377–397

    Article  Google Scholar 

  • Pincock C (2011) Modeling reality. Synthese 180:19–32

    Article  Google Scholar 

  • Pindyck RS, Rubinfeld DL (2009) Microeconomics, 7th edn. Pearson Education, Upper Saddle River

    Google Scholar 

  • Potochnik A (2007) Optimality modeling and explanatory generality. Philos Sci 74(5):680–691

    Article  Google Scholar 

  • Potochnik A (2009) Optimality modeling in a suboptimal world. Biol Philos 24(2):183–197

    Article  Google Scholar 

  • Pritchard D (2009) Knowledge, understanding, and epistemic value. In: O’Hear A (ed) Epistemology. Cambridge University Press, Cambridge, pp 19–43

    Google Scholar 

  • Psillos S (1999) Scientific realism: how science tracks truth. Routledge, New York

    Google Scholar 

  • Psillos S (2011) Living with the abstract: realism and models. Synthese 180:3–17

    Article  Google Scholar 

  • Resnik DB (1991) How-possibly explanations in biology. Acta Biotheor 39:141–149

    Article  Google Scholar 

  • Rice C (2012) Optimality explanations: a plea for an alternative approach. Biol Philos 27:685–703

    Article  Google Scholar 

  • Rice C (2014) Concepts as pluralistic hybrids. Res. doi:10.1111/phpr.12128

    Google Scholar 

  • Rice C (2015) Moving beyond causes: optimality models and scientific explanation. Noûs 49(3):589–615

    Article  Google Scholar 

  • Rohwer Y, Rice C (2013) Hypothetical pattern idealization and explanatory models. Philos Sci 80:334–355

    Article  Google Scholar 

  • Saatsi J (2014) Inconsistency and scientific realism. Synthese 91(13):2941–2955

    Article  Google Scholar 

  • Salmon WC (1984) Scientific explanation and the causal structure of the world. Princeton University Press, Princeton

    Google Scholar 

  • Salmon WC (1998) Causality and explanation. Oxford University Press, New York

    Book  Google Scholar 

  • Schmid-Hempel P, Kacelnik A, Houston A (1985) Honeybees maximize efficiency by not filling their crop. Beahav Ecol Sociobiol 17:61–66

    Article  Google Scholar 

  • Schurz G, Lambert K (1994) Outline of a theory of scientific understanding. Synthese 101(1):65–120

    Google Scholar 

  • Seger J, Stubblefield JW (1996) Optimization and adaptation. In: Rose M, Lauder GV (eds) Adaptation. Cambridge University Press, Cambridge

  • Sober E (2000) The philosophy of biology, 2nd edn. Westview, Boulder

    Google Scholar 

  • Stanford K (2003) No refuge for realism: selective confirmation and the history of science. Philos Sci 70:913–925

    Article  Google Scholar 

  • Stanford K (2006) Exceeding our grasp: science, history, and the problem of unconceived alternatives. OUP, Oxford

    Book  Google Scholar 

  • Stephens DW, Krebs JR (1986) Foraging theory. Princeton University Press, Princeton

    Google Scholar 

  • Strevens M (2009) Depth: an account of scientific explanation. Harvard University Press, Cambridge

    Google Scholar 

  • Strevens M (2013) No understanding without explanation. Stud Hist Philos Sci 44:510–515

    Article  Google Scholar 

  • Suárez M (1999) The role of models in the application of scientific theories. In: Morgan Mary S, Morrison Margaret (eds) Models as mediators: perspectives on natural and social science. Cambridge University Press, Cambridge, pp 168–195

    Chapter  Google Scholar 

  • Trout JD (2007) The psychology of explanation. Philos Compass 2:564–596

    Article  Google Scholar 

  • Walsh D, Lewens T, Ariew A (2002) Trials of life: natural selection and random drift. Philos Sci 69(3):452–473

    Article  Google Scholar 

  • Wayne A (2011) Expanding the scope of explanatory idealization. Philos Sci 78:83–841

    Article  Google Scholar 

  • Weisberg M (2006) Robustness analysis. Philos Sci 73:730–742

    Article  Google Scholar 

  • Weisberg M (2007a) Three kinds of idealization. J Philos 104(12):639–659

    Article  Google Scholar 

  • Weisberg M (2007b) Who is a modeler? Br J Philos Sci 58(2):207–233

    Article  Google Scholar 

  • Weisberg M (2013) Simulation and similarity: using models to understand the world. Oxford University Press, Oxford

    Book  Google Scholar 

  • Weiskopf DA (2009) The plurality of concepts. Synthese 169:145–173

    Article  Google Scholar 

  • Wimsatt W (2007) Re-engineering philosophy for limited beings: piecewise approximations of reality. Harvard University Press, Cambridge

    Google Scholar 

  • Woodward J (2003) Making things happen: a theory of causal explanation. Oxford University Press, Oxford

    Google Scholar 

  • Worrall J (1989) Structural realism: the best of both worlds? Dialectica 43:99–124

    Article  Google Scholar 

  • Zagzebski L (2001) Recovering understanding. In: Steup M (ed) Knowledge, truth, and duty: essays on epistemic justification, responsibility, and virtue. Oxford University Press, New York

    Google Scholar 

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Acknowledgments

This research was partially supported by a Lycoming College Professional Development Grant. I would like to thank Yasha Rohwer, Kyle Stanford, Michael Weisberg, and Angela Potochnik for helpful discussion and comments on previous versions of this work.

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  1. Department of Philosophy, Lycoming College, 700 College Place, Williamsport, PA, 17701, USA

    Collin C. Rice

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Rice, C.C. Factive scientific understanding without accurate representation. Biol Philos 31, 81–102 (2016). https://doi.org/10.1007/s10539-015-9510-2

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