Unbiased Cultural Transmission in Time-Averaged Archaeological Assemblages

Madsen, Mark E.

doi:10.2139/ssrn.2037622

Cited by 14 publications

(22 citation statements)

References 63 publications

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“…In addition to post-depositional processes, substantial differences in the time of accumulation of different layers may have deleterious effects on the accurate representation of faunal spectra. This process, known as time-averaging, is extremely frequent in geologic and anthropic contexts (Binford, 1981;Kowalewski 1996;Premo, 2014;Madsen, 2018), and has a direct impact on the reliability of the quantification of abundance, richness, evenness, and diversity in time-averaged samples (Leonard and Jones, 1989). Specifically, the longer the duration of layer formation, the more inflated richness and diversity will be.…”

Section: Methodsmentioning

confidence: 99%

“…This makes tracking change over time more problematic and increases the risk of misidentifying inflated counts for actual human choices (i.e. Type I error when testing hypotheses; Premo, 2014;Madsen, 2018). The presence of differential accumulation rates, palimpsests, and taphonomic processes therefore complicates any attempt at quantifying the effective temporal scale of individual layers solely based on stratigraphy.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Macromammal and bird assemblages across the late Middle to Upper Palaeolithic transition in Italy: an extended zooarchaeological review

Romandini

Crezzini

Bortolini

et al. 2020

Quaternary International

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Macromammal and bird assemblages across the late Middle to Upper Palaeolithic transition in Italy: an extended zooarchaeological review

Romandini

Crezzini

Bortolini

et al. 2020

Quaternary International

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show abstract

“…involve evolutionary mechanisms, the simulated cultural record (for example, density, spatial distribution or variability in cultural assemblages) is not described by summary statistics but instead sampled to mimic the time-averaging and other postdepositional biases that affect archaeological data (Madsen 2012;Premo 2014). The question of how to represent the archaeological consequences of the modelled processes in a way that is directly comparable with the archaeological record is an issue particularly relevant to archaeology.…”

Section: Analysing and Interpreting The Resultsmentioning

confidence: 99%

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

Romanowska¹

2015

Human Biology

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With the current surge of simulation studies in archaeology there is a growing concern for the lack of engagement and feedback between modellers and domain specialists. To facilitate this dialogue I present a compact guide to the simulation modelling process applied to a common research topic and the focus of this special issue of Human Biology-human dispersals. The process of developing a simulation is divided into nine steps grouped in three phases. ThePre-print version. Visit digitalcommons.wayne.edu/humbiol after publication for the final version.conceptual phase consists of identifying research questions (step 1) and finding the most suitable method (step 2), designing the general framework and the resolution of the simulation (step 3) and then by filling in that framework with the modelled entities and the rules of interactions (step 4). This is followed by the technical phase of coding and testing (step 5), parameterising the simulation (step 6) and running it (step 7). In the final phase the results of the simulation are analysed and re-contextualised (step 8) and the findings of the model are disseminated in publications and code repositories (step 9). Each step will be defined and characterised and then illustrated with examples of published human dispersals simulation studies. While not aiming to be a comprehensive textbookstyle guide to simulation, this overview of the process of modelling human dispersals should arm any non-modeller with enough understanding to evaluate the quality, strengths and weaknesses of any particular archaeological simulation and provide a starting point for further exploration of this common scientific tool.All archaeologists start modelling the moment they step out of the excavation trench. We interpret the individual finds (e.g., pots, skeletons, buildings etc.) within certain frameworks (e.g., pottery typologies, bone taxonomies, architectural types etc.) and analyse sets of finds to detect population level patterns (e.g., cultural similarities, age profiles of bone assemblages, urbanPre-print version. Visit digitalcommons.wayne.edu/humbiol after publication for the final version. development, etc.) with strict analytical rigour. However, the interpretations of these patterns in terms of human behaviour and causality are predominantly built using natural language, i.e. they are constructed in the form of conceptual models that hypothesise which causal mechanisms might have led from actions of individual actors (the owners of pots, users of skeletons and inhabitants of buildings) to the detected population-level patterns. These causal relationships are often described as, for example, "culture A influenced culture B," "the dispersal reached area C," or "population D outcompeted population E." Although inferences like these are made on the basis of rigorously collected and analysed data, and are commonly built upon extensive research and a good understanding of multiple strands of evidence, they nevertheless represent a thought experiment and are therefore limited ...

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“…Just such distributions seem to exist in nature [24,73]. In cultural evolution, neutral theories have been used to explain the power law distributions shown by artefacts of many kinds [28,29,32,35,[74][75][76][77][78][79][80][81]. In network science they have been invoked to explain putative power law degree distributions [39,41,82,83] (Box 2).…”

Section: Variant Abundance Distributionsmentioning

confidence: 99%

Neutral syndrome

et al. 2020

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Neutral models, formerly confined to ecology and evolutionary biology, are spreading. In recent years they've been applied to explaining the diversity of baby names, scientific citations, cryptocurrencies, pot decorations, literary lexica, tumour variants and much more besides. Here, we survey important neutral models and highlight their similarities. We investigate the most widely used tests of neutrality, show that they are weak, and suggest more powerful methods. We conclude by discussing the role of neutral models in the explanation of diversity. We suggest that the ability of neutral models to fit low-information distributions should not be taken as evidence for the absence of selection. Nevertheless, many studies, in increasingly diverse fields, make just such claims. We call this tendency Neutral Syndrome.

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Unbiased Cultural Transmission in Time-Averaged Archaeological Assemblages

Cited by 14 publications

References 63 publications

Macromammal and bird assemblages across the late Middle to Upper Palaeolithic transition in Italy: an extended zooarchaeological review

Macromammal and bird assemblages across the late Middle to Upper Palaeolithic transition in Italy: an extended zooarchaeological review

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

Neutral syndrome

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