What good are abstract and what-if models? Lessons from the Gaïa hypothesis

Abstract

This article on the epistemology of computational models stems from an analysis of the Gaïa hypothesis (GH). It begins with James Kirchner’s criticisms of the central computational model of GH: Daisyworld. Among other things, the model has been criticized for being too abstract, describing fictional entities (fictive daisies on an imaginary planet) and trying to answer counterfactual (what-if) questions (how would a planet look like if life had no influence on it?). For these reasons the model has been considered not testable and therefore not legitimate in science, and in any case not very interesting since it explores non actual issues. This criticism implicitly assumes that science should only be involved in the making of models that are “actual” (by opposition to what-if) and “specific” (by opposition to abstract). I challenge both of these criticisms in this article. First by showing that although the testability—understood as the comparison of model output with empirical data—is an important procedure for explanatory models, there are plenty of models that are not testable. The fact that these are not testable (in this restricted sense) has nothing to do with their being “abstract” or “what-if” but with their being predictive models. Secondly, I argue that “abstract” and “what-if” models aim at (respectable) epistemic purposes distinct from those pursued by “actual and specific models”. Abstract models are used to propose how-possibly explanation or to pursue theorizing. What-if models are used to attribute causal or explanatory power to a variable of interest. The fact that they aim at different epistemic goals entails that it may not be accurate to consider the choice between different kinds of model as a “strategy“.

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Fig. 1

Notes

  1. 1.

    For reviews, see Wood et al. (2008), McDonald-Gibson et al. (2008), Lenton and Williams (2009), Dutreuil (2013).

  2. 2.

    Sarkar and Plutynski’s judgement of GH as being a “philosophically intriguing idea at the fringe of science” (2010, p. xxiii, my emphasis) belongs to the more sympathetic positions within the community. According to Ruse (2013, chap. 8), part of the reasons for the rejection of GH by evolutionary biologists, besides epistemological issues, are to be found in the own insecurity and tension that existed within evolutionary biology at the time GH was proposed.

  3. 3.

    On GH and niche construction, see Free and Barton (2007), Pocheville (2010).

  4. 4.

    For a detailed review, precise quotations, references and pages, see Dutreuil (2013).

  5. 5.

    Kirchner also worries about the simplicity of Daisyworld which may not reflect the complexity of the world; for a discussion of this point, see Dyke and Weaver (2013), Dutreuil (2013).

  6. 6.

    For details about “fidelity criteria” and “intended scope”, see Weisberg (2013), Sect. 3.3.

  7. 7.

    One may worry that it may not be warranted to talk about one model and to talk about the same model (run in two different scenarios) since not only the values of some parameters may be modified but also the structure of the model. First, the modification of the structure that takes place here can actually be thought of as a borderline case of the change of the value of some parameters (bringing one parameter to zero). Second, calling model-A and model-B the two different scenarios does not change my argument, namely the idea that the comparison between the two (models or scenarios) is a particular modelling practice, which, as it will be argued below, is epistemologically sound and has a praticular epistemic purpose.

  8. 8.

    Notice that the same algorithm may alternatively be used to explain or to predict phenomena depending on the epistemic context in which it is used. Besides, I would be happy to grant that there are grey cases where it is not clear in which epistemic situation (prediction or explanation) we are.

  9. 9.

    Notice that one may rely on the source procedure even if one knows that part of the simulation artificially misrepresents the target but one has other reasons to believe in the validity of this model building technique (Winsberg 2006; Grüne-Yanoff and Weirich 2010).

  10. 10.

    Huneman, this issue, offers a detailed and interesting discussion of the way the evaluation of the likelihood can be carried out.

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Acknowledgments

I am indebted to all those present at the EASPLS meeting (Geneva, September 2012) and at the Duke Consortium for the history and philosophy of biology (June, 2013) for giving me their comments on these ideas, especially to Marie Kaiser and Tyler Curtain. I would like also to thank warmly Frédéric Bouchard, Jean Gayon, Philippe Huneman, Arnon Levy, Arnaud Pocheville and Judith Villez as well as an anonymous reviewer who helped me significantly improve earlier versions of this paper. I must finally thank Staffan Müller-Wille for his careful reading which has greatly contributed to the clarification and amelioration of the submitted version.

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Dutreuil, S. What good are abstract and what-if models? Lessons from the Gaïa hypothesis. HPLS 36, 16–41 (2014). https://doi.org/10.1007/s40656-014-0003-4

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Keywords

  • Gaïa hypothesis
  • Computational models
  • Daisyworld
  • Artificial life
  • Explanation
  • Model validation