Dynamical causes

Abstract

Mechanistic explanations are often said to explain because they reveal the causal structure of the world. Conversely, dynamical models supposedly lack explanatory power because they do not describe causal structure. The only way for dynamical models to produce causal explanations is via the 3M criterion: the model must be mapped onto a mechanism. This framing of the situation has become the received view around the viability of dynamical explanation. In this paper, I argue against this position and show that dynamical models can themselves reveal causal structure and consequently produce non-mechanistic, dynamical explanations. Taking the example of cell fates from systems biology, I show how dynamical models, and specifically the attractor landscapes they describe, identify the causes of cell differentiation and explain why cells select particular fates. These dynamical features of the system better fit Woodward’s (Biol Philos 25(3):287–318, 2010. https://doi.org/10.1007/s10539-010-9200-z; Synthese, 2018. https://doi.org/10.1007/s11229-018-01998-6) criteria of specificity and proportionality and make them the best candidate causes of cell fates than mechanisms. I also show how these causes are irreducible and inaccessible to mechanistic models, making 3M unworkable and counterproductive in this case. Dynamical models can reveal dynamical causes and thereby provide causal explanations.

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Notes

  1. 1.

    Following the convention proposed by Glennan and Illari (2018b) I distinguish the philosophical stance of Mechanism from the object called a mechanism via a capitalisation added to the former.

  2. 2.

    Though Kaplan and Craver (2011) qualify this demand for completeness: descriptions that reveal a partial causal structure and are in the process of completion can also be considered explanatory.

  3. 3.

    A nonmechanistic explanation refers broadly to any explanation that does not appeal to underlying causal mechanisms for its explanatory power.

  4. 4.

    It should be mentioned here that there are ongoing discussions regarding the feasibility of non-causal dynamical explanations (e.g. Ross 2015; Chirimuuta 2017). I will however focus specifically on the case for causal dynamical explanations and bracket the non-causal option.

  5. 5.

    I acknowledge here the significant debates around higher-level interventions in the mechanist literature, particularly the problem of fat-handedness: intervening on a higher-level variable necessitates simultaneously intervening on its supervenience base, and hence violating the interventionist requirement for isolating a single variable for intervention (see Baumgartner and Gebharter 2017; Krickel 2017). I bracket this substantial discussion by adhering to Woodward’s (2015) clarification to (M). Woodward specifies that non-causal supervenience relations between micro- and macro-levels need not be held steady in the same fashion as causal relations, so that “properties that supervene on but that are not identical with realizing properties can be causally efficacious.” (Woodward 2015, p. 303).

References

  1. Bechtel W, Abrahamsen A (2010) Dynamic mechanistic explanation: computational modeling of circadian rhythms as an exemplar for cognitive science. Stud Hist Philos Sci Part A 41(3):321–333. https://doi.org/10.1016/j.shpsa.2010.07.003

  2. Bechtel W, Abrahamsen AA (2013) Thinking dynamically about biological mechanisms: Networks of Coupled Oscillators. Found Sci 18(4):707–723. https://doi.org/10.1007/s10699-012-9301-z

  3. Chemero A, Silberstein M (2008) After the philosophy of mind: replacing scholasticism with science*. Philos Sci 75(1):1–27. https://doi.org/10.1086/587820

    Article  Google Scholar 

  4. Chirimuuta M (2017) Explanation in computational neuroscience: causal and non-causal. Br J Philos Sci. https://doi.org/10.1093/bjps/axw034

    Article  Google Scholar 

  5. Craver CF (2006) When mechanistic models explain. Synthese 153(3):355–376. https://doi.org/10.1007/s11229-006-9097-x

    Article  Google Scholar 

  6. Craver CF (2007) Explaining the brain: mechanisms and the mosaic unity of neuroscience. Oxford University Press, Oxford

    Book  Google Scholar 

  7. Craver CF, Kaplan DM (2018) Are more details better? On the norms of completeness for mechanistic explanations. Br J Philos Sci 1:7. https://doi.org/10.1093/bjps/axy015

    Article  Google Scholar 

  8. Davila-Velderrain J, Martinez-Garcia JC, Alvarez-Buylla ER (2015) Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development. Front Genet. https://doi.org/10.3389/fgene.2015.00160

    Article  Google Scholar 

  9. Enver T, Pera M, Peterson C, Andrews PW (2009) Stem cell states, fates, and the rules of attraction. Cell Stem Cell 4(5):387–397. https://doi.org/10.1016/j.stem.2009.04.011

    Article  Google Scholar 

  10. Ferrell JE (2012) Bistability, bifurcations, and Waddington’s epigenetic landscape. Curr Biol 22(11):R458–R466. https://doi.org/10.1016/j.cub.2012.03.045

    Article  Google Scholar 

  11. Glennan S, Illari P (2018a) Introduction. In: Glennan S, Illari P (eds) The Routledge handbook of mechanisms and mechanical philosophy. Routledge, Abington

    Google Scholar 

  12. Glennan S, Illari P (2018b) Varieties of mechanisms. In: Glennan S, Illari P (eds) The Routledge handbook of mechanisms and mechanical philosophy. Routledge, Abington

    Google Scholar 

  13. Graf T, Enver T (2009) Forcing cells to change lineages. Nature 462(7273):587–594. https://doi.org/10.1038/nature08533

    Article  Google Scholar 

  14. Haken H, Kelso JAS, Bunz H (1985) A theoretical model of phase transitions in human hand movements. Boil Cybern 51:347–356

  15. Huang S (2012) The molecular and mathematical basis of Waddington’s epigenetic landscape: a framework for post-Darwinian biology? BioEssays 34(2):149–157. https://doi.org/10.1002/bies.201100031

    Article  Google Scholar 

  16. Huang S, Eichler G, Bar-Yam Y, Ingber DE (2005) Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys Rev Lett 94(12):128701. https://doi.org/10.1103/PhysRevLett.94.128701

    Article  Google Scholar 

  17. Huang S, Guo Y-P, May G, Enver T (2007) Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev Biol 305(2):695–713. https://doi.org/10.1016/j.ydbio.2007.02.036

    Article  Google Scholar 

  18. Jaeger J, Monk N (2014) Bioattractors: dynamical systems theory and the evolution of regulatory processes. J Physiol 592(11):2267–2281. https://doi.org/10.1113/jphysiol.2014.272385

    Article  Google Scholar 

  19. Kaplan DM (2011) Explanation and description in computational neuroscience. Synthese 183(3):339–373. https://doi.org/10.1007/s11229-011-9970-0

    Article  Google Scholar 

  20. Kaplan DM (2015) Moving parts: the natural alliance between dynamical and mechanistic modeling approaches. Biol Philos 30(6):757–786. https://doi.org/10.1007/s10539-015-9499-6

    Article  Google Scholar 

  21. Kaplan DM, Craver CF (2011) The explanatory force of dynamical and mathematical models in neuroscience: a mechanistic perspective*. Philos Sci 78(4):601–627. https://doi.org/10.1086/661755

    Article  Google Scholar 

  22. Krickel B (2017) Making sense of interlevel causation in mechanisms from a metaphysical perspective. J Gen Philos Sci 48(3):453–468. https://doi.org/10.1007/s10838-017-9373-0

    Article  Google Scholar 

  23. Meyer R (2018) The non-mechanistic option: defending dynamical explanations. Br J Philos Sci. https://doi.org/10.1093/bjps/axy034

    Article  Google Scholar 

  24. Moris N, Pina C, Arias AM (2016) Transition states and cell fate decisions in epigenetic landscapes. Nat Rev Genet 17(11):693–703. https://doi.org/10.1038/nrg.2016.98

    Article  Google Scholar 

  25. Ross LN (2015) Dynamical models and explanation in neuroscience. Philos Sci 82(1):32–54. https://doi.org/10.1086/679038

    Article  Google Scholar 

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

    Google Scholar 

  27. Scholz J, Kelso J (1989) A quantitative approach to understanding the formation and change of coordinated movement patterns. J Mot Behav 21(2):122–144

    Article  Google Scholar 

  28. Stepp N, Chemero A, Turvey MT (2011) Philosophy for the rest of cognitive science. Top Cogn Sci 3(2):425–437. https://doi.org/10.1111/j.1756-8765.2011.01143.x

    Article  Google Scholar 

  29. van Eck D (2018) Rethinking the explanatory power of dynamical models in cognitive science. Philos Psychol 31(8):1131–1161. https://doi.org/10.1080/09515089.2018.1480755

    Article  Google Scholar 

  30. Waddington CH (1957) The strategy of the genes. George Allen and Unwin, London

    Google Scholar 

  31. Waters C (2007) Causes that make a difference. J Philos 104(11):551–579. https://doi.org/10.5840/jphil2007104111

    Article  Google Scholar 

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

    Google Scholar 

  33. Woodward J (2008) Mental causation and neural mechanisms. In: Hohwy J, Kallestrup J (eds) Being reduced: new essays on reduction, explanation, and causation. Oxford University Press, Oxford, pp 218–262

    Google Scholar 

  34. Woodward J (2010) Causation in biology: stability, specificity, and the choice of levels of explanation. Biol Philos 25(3):287–318. https://doi.org/10.1007/s10539-010-9200-z

    Article  Google Scholar 

  35. Woodward J (2013) Mechanistic explanation: Its scope and limits. Aristotelian Soc Suppl 87(1):39–65. https://doi.org/10.1111/j.1467-8349.2013.00219.x

  36. Woodward J (2015) Interventionism and causal exclusion. Res 91(2):303–347. https://doi.org/10.1111/phpr.12095

    Article  Google Scholar 

  37. Woodward J (2017) Explanation in neurobiology: an interventionist perspective. In: Kaplan DM (ed) Explanation and integration in mind and brain science. Oxford University Press, Oxford. https://doi.org/10.1093/oso/9780199685509.001.0001

    Google Scholar 

  38. Woodward J (2018) Explanatory autonomy: the role of proportionality, stability, and conditional irrelevance. Synthese. https://doi.org/10.1007/s11229-018-01998-6

    Article  Google Scholar 

  39. Yablo S (1992) Mental causation. Philos Rev 101(2):245–280. https://doi.org/10.2307/2185535

    Article  Google Scholar 

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Acknowledgements

I am particularly grateful to Nick Brancazio for minor comments and major support. Many thanks also to Michael Kirchhoff for helpful comments on this manuscript.

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Correspondence to Russell Meyer.

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Meyer, R. Dynamical causes. Biol Philos 35, 48 (2020). https://doi.org/10.1007/s10539-020-09755-1

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Keywords

  • Mechanistic explanation
  • Mechanism
  • Dynamical models
  • Dynamical explanation
  • Cell fates
  • Systems biology