Inferring cultural transmission processes from frequency data

Cultural change can be quantified by temporal frequency changes of different cultural artefacts. Based on those (observable) frequency patterns researchers often aim to infer the nature of the underlying cultural transmission processes and therefore to identify the (unobservable) causes of cultural change. Especially in archaeological and anthropological applications this inverse problem gains particular importance as occurrence or usage frequencies are commonly the only available information about past cultural traits or traditions and the forces affecting them. Matters are further complicated by the fact that observed changes often describe the dynamics in samples of the population of artefacts whereas transmission processes act on the whole population. In this talk we start analysing the described inference problem. We develop a generative inference framework which firstly establishes a causal relationship between underlying transmission processes and temporal changes in frequency of cultural artefacts and secondly infers which cultural transmission processes are consistent with observed frequency changes. In this way we aim to deduce underlying transmission modes directly from available data without any optimality or equilibrium assumption. Importantly this framework allows us to explore the theoretical limitations of inference procedures based on population-level data and to start answering the question of how much information about the underlying transmission processes can be inferred from frequency patterns. Our approach might help narrow down the range of possible processes that could have produced observed frequency patterns, and thus still be instructive in the face of uncertainty. Rather than identifying a single transmission process that explains the data, we focus on excluding processes that cannot have produced the observed changes in frequencies. We apply the developed framework to a dataset describing the LBK culture.