Mapping an expanding territory: computer simulations in evolutionary biology

Abstract

The pervasive use of computer simulations in the sciences brings novel epistemological issues discussed in the philosophy of science literature since about a decade. Evolutionary biology strongly relies on such simulations, and in relation to it there exists a research program (Artificial Life) that mainly studies simulations themselves. This paper addresses the specificity of computer simulations in evolutionary biology, in the context (described in Sect. 1) of a set of questions about their scope as explanations, the nature of validation processes and the relation between simulations and true experiments or mathematical models. After making distinctions, especially between a weak use where simulations test hypotheses about the world, and a strong use where they allow one to explore sets of evolutionary dynamics not necessarily extant in our world, I argue in Sect. 2 that (weak) simulations are likely to represent in virtue of the fact that they instantiate specific features of causal processes that may be isomorphic to features of some causal processes in the world, though the latter are always intertwined with a myriad of different processes and hence unlikely to be directly manipulated and studied. I therefore argue that these simulations are merely able to provide candidate explanations for real patterns. Section 3 ends up by placing strong and weak simulations in Levins’ triangle, that conceives of simulations as devices trying to fulfil one or two among three incompatible epistemic values (precision, realism, genericity).

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Notes

  1. 1.

    See http://www.jurysimulationresearch.org to find examples of such simulations.

  2. 2.

    The simulated flocking behaviour can be seen at http://www.red3d.com/cwr/boids/applet/.

  3. 3.

    Sets of ecological communities of species, related by processes of migration, colonization, gene flow, etc.

  4. 4.

    Often, unpredicted results of experiments, or failure to reproduce experiments in different labs, come from these parasitic unknown factors: most recently, the European experiments that found neutrinos going faster than the speed of light were affected by such uncontrolled factors, i.e. optical fibers with unusual diameter (Reich 2012).

  5. 5.

    Moreover, computer simulations run by themselves, so we cannot see and understand what is going on—in the sense of how Descartes, in principle, required all cognitive operations to be self-certain of the validity of each of its steps.

  6. 6.

    It is still used when producing cartoons with fish [Reynolds’ simulations were first developed in the context of video programming, as is indicated by the fact that his paper was published in a journal of graphic design (Computer Graphics) rather than in a computer science or biology journal].

  7. 7.

    Even though the equations do not specify the cause of the symmetry-breaking, and that it can as well be initial stochastic fluctuations.

  8. 8.

    These three properties are, as formulated by Lewontin (1970), the conditions for natural selection.

  9. 9.

    I say here “processes”, but some of these processes are not “causal processes” stricto sensu, they are, as illustrated, rather causal relations in the sense of counterfactual dependence. What is modeled, through what’s called here “pure possible processes” as representational core of the simulation, is thereby not only causal processes stricto sensu but causal relations, in whatever sense of causation can hold among metaphysicians.

  10. 10.

    As McShea and Brandon (2011) argue, this diffusion process constitutes the first process in evolution—entailed by the mere fact of variation, that is logically prior to natural selection.

  11. 11.

    A neutral model, be it in ecology, paleontology or genetic evolution, models no specific causal process, for example it sets to 0 all parameters on which causal processes depend; however for the epistemological investigation presented in this paper we can call “pure possible process” what is going on in such models, in a way analogous to models of alternate causal hypotheses—“process” here is used in the sense that we talk of “stochastic processes”, “chance processes”, etc. We leave aside the metaphysical question of whether (some) chance processes are causal processes.

  12. 12.

    See, for example, Bell et al. (2006, p. 1382): “It was surprising to find that spatial neutral models give rise to frequency distributions of precision that are very similar to those estimated from biological surveys, as a consequence of the spatial patterns produced by local dispersal alone”.

  13. 13.

    If all relative fitnesses in the agents of a model are equal to 1, of course there is no natural selection.

  14. 14.

    These strong simulations have the same epistemic function as what Weber (2014) calls “experimental models”—namely, processes and systems that are designed in order to experiment and test hypotheses about one very general kind of system.

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Acknowledgments

The author warmly acknowledges Isabelle Drouet and Sébastien Dutreuil for invaluable critiques and suggestions, as well as two anonymous reviewers whose careful reading and constructive criticism considerably improved the paper. Thanks to Adam Hocker for a thorough language checking, and to Staffan Müller-Wille for his precious final suggestions.

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Huneman, P. Mapping an expanding territory: computer simulations in evolutionary biology. HPLS 36, 60–89 (2014). https://doi.org/10.1007/s40656-014-0005-2

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Keywords

  • A-Life
  • Computer simulations
  • Explanations
  • Models
  • Evolutionary biology
  • Hypothesis testing