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Signaling in an Unknown World

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Abstract

This paper proposes a sender-receiver model to explain two large-scale patterns observed in natural languages: Zipf’s inverse power law relating the frequency of word use and word rank, and the negative correlation between the frequency of word use and rate of lexical change. Computer simulations show that the model recreates Zipf’s inverse power law and the negative correlation between signal frequency and rate of change, provided that agents balance the rates with which they invent new signals and forget old ones. Results are robust across a wide range of parameter values and structural assumptions, such as different forgetting rules and forgetting rates. Analysis of the model further suggests that Zipf’s law relating word frequency and rank arises because of language-external factors and that frequent signals change less because frequent signals are less subject to drift than rare ones. The paper concludes with some brief considerations on model-based and data-driven approaches in philosophy.

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Notes

  1. Pence and Ramsey (2018) offer an overview of data repositories, analytical tools, and related challenges in this emerging field.

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Acknowledgements

I would like to thank Brian Skyrms, Louis Narens, Cailin O’Connor, Simon Huttegger, Jeffrey Barrett, Travis LaCroix, and other members of the Social Dynamics Seminar at UC Irvine for their comments on an earlier version of this paper. I am equally grateful for the organizers and the audience of the conference “Generalized Theory of Evolution” in Düsseldorf. I would also like to thank Hannah Read for her numerous comments on previous versions of this paper, as well as Gareth Roberts and Justin Bruner for their helpful feedback on earlier drafts.

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    Rafael Ventura

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Ventura, R. Signaling in an Unknown World. Erkenn 88, 885–905 (2023). https://doi.org/10.1007/s10670-021-00385-x

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