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Reliability models in cultural phylogenetics

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Abstract

Cultural phylogenetics has made remarkable progress by relying on methods originally developed in biology. But biological and cultural evolution do not always proceed according to the same principles. So what, if anything, could justify the use of phylogenetic methods to reconstruct the evolutionary history of culture? In this paper, we describe models used to assess the reliability of inference methods and show how these models play an underappreciated role in addressing that question. The notion of reliability is of course central to these models. As we explain, a common way of understanding reliability is in terms of low error rates. A careful look at case studies in cultural phylogenetics suggests that reliability models partly corroborate this understanding of reliability but also raises points of tension. We conclude by hinting at a few ways forward.

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Notes

  1. A related category of synthetic data models are generative models. Generative models simulate data by specifying a probabilistic causal mechanism of interest. By comparing the distribution of simulated data with data observed in the real world, it is then possible to ascertain how likely it is that the observed data was produced by the hypothesized causal mechanism—for a discussion of generative models in cultural evolution, see Kandler and Powell (2018).

  2. The term “reliability” may also be used in a more general sense to denote the extent to which a study is replicable, reproducible, and reasoned. Here, we restrict our attention to the use of the term when it comes to the performance of inference methods.

  3. The choice of what constitutes an acceptable value for error rates has sparked considerable interest and active debate—for a recent example, see Benjamin et al. (2018) and Lakens et al. (2018).

  4. A similar notion is also at the core of reliabilist theories of epistemic justification. Following Goldman (1976, 1979), proponents of reliabilism typically take a belief-forming process to be reliable just in case it tends to deliver true beliefs–for more recent discussions, see Alston (1995), Adler (2005), and Comesaña (2009, 2010); see also Goldman (1999) for the notion of reliable belief-forming processes in social epistemology.

  5. Phylogenetic inference methods in biology had a turbulent history in the 20th century, with proponents of different methodologies engaging in oft-vicious debates. Hull (1988) offers a still authoritative account of the debate’s social epistemology; Sober (1991) is an in-depth conceptual treatment.

  6. Note that the trait-bearing entities in Nunn et al. (2010) are simulated societies rather than simulated individuals. This is important because it underscores the point that phylogenies can be built with either individuals or societies as trait bearers. In the case of accents and eye colors, the trait-bearing entities were individuals; in the case of languages, the trait-bearing entities are societies—i.e., communities of language users.

  7. Bokulich (2020) also discusses models that simulate data. But her focus is on simulations whose purpose is to correct noisy or missing data. They therefore differ from reliability models in that their function is to calibrate methods of data production and data correction, and not to assess the reliability of inference methods.

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Acknowledgements

I would like to thank Hannah Read, Michael Weisberg, Robert Brandon, Alex Rosenberg, Kevin Hoover, Carlotta Pavese, Adrian Currie, and Gareth Roberts for extremely helpful feedback on earlier versions of this paper.

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  1. University of Pennsylvania, Philadelphia, PA, USA

    Rafael Ventura

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  1. Rafael Ventura
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Ventura, R. Reliability models in cultural phylogenetics. Biol Philos 38, 19 (2023). https://doi.org/10.1007/s10539-023-09900-6

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