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A Representational Reading of the Pillars of Statisticalism: Towards a Perspectival Analysis in Population Biology

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  • Cristián Novelli,
  • Tiago Rama
Cristián Novelli
Subunidad de Lógica y Metodología, Unidad Académica Instituto de Filosofía, Facultad de Humanidades y Ciencias de la Educación, Universidad de la República, Montevideo, Uruguay.
Tiago Rama
Subunidad de Filosofía e Historia de la Ciencia, Unidad Académica Instituto de Filosofía, Facultad de Humanidades y Ciencias de la Educación, Universidad de la República, Montevideo, Uruguay.
DOI: https://doi.org/10.36446/af.e1282

Published 2026-06-24

Keywords

  • Representación científica,
  • Perspectivismo científico,
  • Visión estadística,
  • Modelos matemáticos,
  • Selección natural,
  • Biología poblacional
    • ...More
    Less
  • Scientific Representation,
  • Scientific Perspectivism,
  • Statisticalism,
  • Mathematical Models,
  • Natural Selection,
  • Population Biology
    • ...More
    Less
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Within evolutionary theory, the causalist and statisticalist stances propose different ways of understanding the parameters and concepts involved in Modern Synthesis models. In particular, the core of the debate centers on whether natural selection and drift should be understood as causal processes or as statistical parameters defined by these models. This essay offers a representational reading of the core commitments of one of these stances: the four ‘pillars’ of Statisticalism (Walsh et al., 2017). To this end, we analyze these commitments through the lens of Van Fraassen’s (2008) account of scientific representation, interpreting them as consequences of the representational nature of population models. In articulating each pillar, we emphasize the relevance of model use for specific scientific aims, as well as the perspectival character of population models. This allows us to counter certain criticisms and interpretations regarding statisticalism; in particular, we argue that it is incorrect to assume that statisticalism establishes population genetics as “just” mathematics.

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