title: Revealing patterns of cultural transmission from frequency data: equilibrium and non-equilibrium assumptions.
abstract: A long tradition of cultural evolutionary studies has developed a rich repertoire of mathematical models of social learning. Early studies have laid the foundation of more recent endeavours to infer patterns of cultural transmission from observed frequencies of a variety of cultural data, from decorative motifs on potsherds to baby names and musical preferences. While this wide range of applications provides an opportunity for the development of generalisable analytical workflows, archaeological data present new questions and challenges that require further methodological and theoretical discussion. Here we examine the decorative motifs of Neolithic pottery from an archaeological assemblage in Western Germany, and argue that the widely used (and relatively undiscussed) assumption that observed frequencies are the result of a system in equilibrium conditions is unwarranted, and can lead to incorrect conclusions. We analyse our data with a simulation-based inferential framework that can overcome some of the intrinsic limitations in archaeological data, as well as handle both equilibrium conditions and instances where the mode of cultural transmission is time-variant. Results suggest that none of the models examined can produce the observed pattern under equilibrium conditions, and suggest. instead temporal shifts in the patterns of cultural transmission.
authors.affiliation: University of Cambridge, Department of Archaeology and Anthropology, CB2 3ER, Cambridge,United Kingdom.
authors.forename: Enrico R
authors.initials: ER
authors.lastname: Crema
authors.affiliation: Max Planck Institute for Evolutionary Anthropology,Department of Human Behavior, Ecology and Culture, 04103,Leipzig,Germany.
authors.forename: Anne
authors.initials: A
authors.lastname: Kandler
authors.affiliation: UCL Institute of Archaeology WC1H 0PY,London, United Kingdom.
authors.forename: Stephen
authors.initials: S
authors.lastname: Shennan