An R Package for a Generative-Inference Based Cultural Evolutionary Analysis

Author(s): Enrico Crema; Anne Kandler; Clémentine Straub

Year: 2019


This is an abstract from the "Practical Approaches to Identifying Evolutionary Processes in the Archaeological Record" session, at the 84th annual meeting of the Society for American Archaeology.

Since the seminal works by Neiman (1995) and Shennan & Wilkinson (2001), evolutionary archaeologists and anthropologists have been trying to infer social learning strategies by analysing the temporal frequency of different cultural variants in a population. These early applications directly employed methods developed in population genetics to test whether observed frequency distributions deviate from those expected under neutral evolution, regarded as functionally equivalent to unbiased learning. Instances of significant deviations were then regarded as evidence of alternative modes of social learning, most commonly conformist or anti-conformist biases. Recent research, however, highlighted the problem of equifinality in such cultural evolutionary studies, i.e. situations where various learning processes can result in very similar population-level characteristics. This R package implements a generative inference framework aimed at analysing which of the social learning strategies considered are consistent with the available data and which are not.

Cite this Record

An R Package for a Generative-Inference Based Cultural Evolutionary Analysis. Enrico Crema, Anne Kandler, Clémentine Straub. Presented at The 84th Annual Meeting of the Society for American Archaeology, Albuquerque, NM. 2019 ( tDAR id: 451985)

Record Identifiers

Abstract Id(s): 23936