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  Statistical signals of copying are robust to time- and space-averaging

Youngblood, M., Miton, H., & Morin, O. (2023). Statistical signals of copying are robust to time- and space-averaging. Evolutionary Human Sciences, 5: e10. doi:10.1017/ehs.2023.5.

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 Creators:
Youngblood, Mason1, Author           
Miton, Helena1, Author           
Morin, Olivier1, Author                 
Affiliations:
1The MINT independent research group, Max Planck Institute of Geoanthropology, Max Planck Society, ou_3504342              

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Free keywords: copying, complexity, time-averaging, space-averaging, generative inference
 Abstract: Cattle brands (ownership marks left on animals) are subject to forces influencing other graphic codes: the copying of constituent parts, pressure for distinctiveness, and pressure for complexity. The historical record of cattle brands in some US states is complete due to legal registration, providing a unique opportunity to assess how sampling processes leading to time- and space-averaging influence our ability to make inferences from limited datasets in fields like archaeology. In this preregistered study, we used a dataset of ~81,000 Kansas cattle brands (1990-2016) to explore two questions: (1) the relative influence of copying, pressure for distinctiveness, and pressure for complexity on the creation and diffusion of brand components, and (2) the effects of time- and space- averaging on statistical signals. By conducting generative inference with an agent-based model, we found that the patterns in our data are consistent with copying and pressure for intermediate complexity. In addition, by comparing mixed and structured datasets, we found that these statistical signals of copying are robust to, and possibly boosted by, time- and space-averaging.

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Language(s): eng - English
 Dates: 2023-04-13
 Publication Status: Published online
 Pages: 15
 Publishing info: -
 Table of Contents: 1 Introduction
1.1 First aim: distinctiveness, copying, and complexity
1.2 Second aim: time- and space-averaging
2 Methods
2.1 Data
2.2 Generative inference (first aim)
2.3 Shuffling model (second aim)
2.4 Temporal distance (supplemental analysis)
3 Results
3.1 Generative inference (first aim)
3.2 Shuffling model (second aim)
3.3 Temporal distance (supplementary aim)
4 Discussion
 Rev. Type: Peer
 Identifiers: DOI: 10.1017/ehs.2023.5
Other: gea0046
 Degree: -

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Title: Evolutionary Human Sciences
Source Genre: Journal
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Pages: - Volume / Issue: 5 Sequence Number: e10 Start / End Page: - Identifier: ISSN: 2513-843X
CoNE: https://pure.mpg.de/cone/journals/resource/2513-843X