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Complexity and Resilience

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Explaining Disease: Philosophical Reflections on Medical Research and Clinical Practice

Part of the book series: European Studies in Philosophy of Science ((ESPS))

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Abstract

As has already emerged in the previous chapters, diseases resulting from interactions of large numbers of heterogeneous probabilistic factors present us with a range of epistemological issues. This is even more the case when non-organic factors, such as psychological, cultural and socio-economic matters play an important role. The biomedical model has usually been associated with identification of the mechanisms underlying the disease. But what about fields in which the biomedical model is not necessarily the dominant model, or in which its application turns out to be problematic? Does the appeal to causal explanatory models actually help and how?

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Notes

  1. 1.

    On the biomedical model and eventual integrations and alternatives to it, see also Marcum (2020).

  2. 2.

    On this see, e.g., Cooper (2020).

  3. 3.

    For discussion, see Faucher and Forest (2022).

  4. 4.

    See e.g., Hyman (2010).

  5. 5.

    See Murphy (2009).

  6. 6.

    See Repnikov and Murphy (2017, p. 274).

  7. 7.

    See Kendler (2008).

  8. 8.

    RDoC, 2008; 2015; nimh.nih.gov/research-priorities/rdoc/index.shtml; https://www.nimh.nih.gov/research-priorities/rdoc/constructs/rdoc-matrix.shtml)

  9. 9.

    On the causal interplay of different sorts of variables, see e.g., Miller and Keller (2000); Schwartz et al. (2016); Borsboom et al. (2019); Miller and Bartholomew (2020).

  10. 10.

    Potochnik and McGill (2012) also provide a survey of possible ways to conceive hierarchical levels of organization – where the significance of levels can be attributed (i) metaphysical; (ii) causal; (iii) evidential and explanatory significance.

  11. 11.

    In his paper, Eronen actually argues against Craver and Bechtel’s view on levels in neuroscience, which he believes fails to provide an adequate criterion for two elements being at the same level, or to account for downward causation.

  12. 12.

    As for the former, factors must be included among which patterns of dependency hold that are graspable and comprehensible; as for the latter, in Woodward’s view, relevant explanatory variables are those that can in principle be exploitable for purposes of manipulation. Woodward’s view – which we will address in Chap. 8 – does not belong to a neo-mechanistic perspective but it is not incompatible with it. As we will see, he takes the notion of “invariance under intervention” as the core of his theory; mechanistic details are admitted as providing finer-grained descriptions of patterns of causal relations. For Woodward on levels, see also Woodward (2020b).

  13. 13.

    Analogous concerns are expressed by other authors. For instance: “To date there is not yet a single example of a thoroughly worked out causal story about mechanisms by which biological events account for nontrivial psychological events” (Miller & Bartholomew, 2020, p. 254); “trying to establish a bio-behavioral mechanism […] is a worthy enterprise for a scientific psychopathologist, but it has not been a particularly successful one so far” (Turkheimer, 2020, p. 534).

  14. 14.

    See e.g.. Sonuga-Barke (2003) and (2005). For a deeper analysis of this case study, see Campaner (2016).

  15. 15.

    For instance, it could help differentiate between autism and Asperger syndrome.

  16. 16.

    For instance, a purely mechanistic account does not seem capable of telling a panic attack due to the presence of a crowd – which we would regard as pathological – from a panic attack caused by falling from a high peak while climbing – which we would regard as not-pathological (See Hartner & Theurer, 2018; Verhoeff, 2013).

  17. 17.

    Reflections on organization, resilience and cognitive reserve in Sects. 5.2 and 5.3 partly rely on Campaner (2020).

  18. 18.

    See e.g. Levi et al. (2013); Schneider et al. (2014).

  19. 19.

    Studies include autopsies showing brain changes which are consistent with advanced Alzheimer’s disease. On these issues see e.g. Stern et al. (2012). On cognitive reserve see also, e.g., Forcada et al. (2015); Grande et al. (2019); Habeck et al. (2017).

  20. 20.

    For attempts to measure cognitive reserve, see e.g. Reed et al. (2010); Zahodne et al. (2013).

  21. 21.

    It has been shown that when part of the fronto-parietal system is damaged an increase in activity occurs throughout the system which might reflect either the use of different strategies or the recruitment of more cortex to compensate. See e.g. Woolgar et al. (2013).

  22. 22.

    Amongst first relevant works on psychiatric resilience, see Rutter (1985). On psychiatric resilience being something different from the mere absence of disease, see Almedom and Glandon (2007).

  23. 23.

    E.g. Collishaw et al. (2016) consider high-risk adolescent (e.g. given severe parental depression) and show that child, family, social, and lifestyle factors can jointly positively contribute to adolescent mental health resilience.

  24. 24.

    See e.g. Russo et al. (2012).

  25. 25.

    See e.g. Soldan et al. (2013); Rapp et al. (2013).

  26. 26.

    See Sheering et al. (2018).

  27. 27.

    Rutter, for instance, has suggested that depression itself is essentially an adaptation to adverse conditions, and, in this sense, a sign of resilience, rather that of lack of resilience. See Rutter (1993). See also Thorén (2014).

  28. 28.

    On the possible relations between descriptive, normative and predictive notions of resilience, see Olsson et al. (2015).

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  1. Department of Philosophy and Communication Studies, University of Bologna, Bologna, Italy

    Raffaella Campaner

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Campaner, R. (2022). Complexity and Resilience. In: Explaining Disease: Philosophical Reflections on Medical Research and Clinical Practice. European Studies in Philosophy of Science. Springer, Cham. https://doi.org/10.1007/978-3-031-05883-7_5

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