University College London, AHRC CECD, Institute of Archaeology
A given cultural trait (for instance subsistence strategy) might exhibit a number of different functionally equivalent variants (i.e. hunting, farming, fishing) that differ in the degree of benefit that they confer depending on the environmental settings. Individuals face the task of choosing between cultural variants according to their utility that results in a population-specific assemblage of cultural variants. We characterize the ‘process of choosing’ as either (i) cultural transmission in form of direct-bias transmission and frequency-dependent transmission and (ii) individual learning.
Now what is the best adoption strategy (which can be a pure strategy or a combination of different modes of cultural transmission and individual learning) for populations living in temporally and spatially changing environments (where ‘environments’ encompass social, ecological and physical variables) and how do population-level characteristics such as cultural diversity depend on the chosen strategy and consequently on the degree of environmental heterogeneity?
To answer those questions we develop a temporally and spatially explicit model that describes the changes in frequencies of different cultural variants under different modes of cultural transmission and individual learning in changing environments. This framework enables us to derive the best adoption strategy of a population in regards to the experienced degree of environmental variability and to establish the relationship between environmental variability and cultural diversity (in particular the size of the assemblage of cultural variants).
Further, the developed approach can be applied to time series data of usage or occurrence of different cultural variants. Often researchers are interested in inferring something about the underlying social process that produced those usage or occurrence frequencies. As our approach links frequency change pattern of cultural variants with the adoption decision of the population it potentially informs such inference. We envision that our model, in combination with ABC methods, could shed some light into this problem. We do not claim the existence of a unique relationship between observed frequency patterns and underlying processes; to the contrary, we suspect that different processes can produce the same frequency pattern. However, we maintain that our approach can help narrow down the range of possible processes that could have produces those patterns, and thus still be instructive in the face of uncertainty. Rather than identifying a single social process that explains the data, we focus on excluding processes that cannot have produced the observed changes in frequencies. Deploying this reasoning, based on the widths of the posterior distributions of different model parameter researchers could draw conclusions about the adoption mechanisms manifest in the population.