Can you analyse that? Fitting simulations to idealised outcomes for the origins of farming

Elizabeth M Gallagher, Mark G Thomas, Stephen J Shennan


Monte-Carlo simulation is a powerful tool for exploring model behaviour and sharpening intuitions about prehistoric processes. Model-based inference requires empirical data, both to assess relative support for alternative models and to estimate model parameter values. However, inference on prehistoric processes is sometimes challenged by a lack of quantitative and ascertainment bias-free data, and often only general outcomes of past processes are known. In addition, when Monte-Carlo simulation is used to explore model behaviour, this is often done via a fix-all-but-one approach, whereby parameters are set at some default values and then varied one at a time. Such an approach will fail to capture many of the subtleties of parameter interactions.

In this study we examine the origins of agriculture by applying Monte-Carlo simulation to a model first proposed by Bowles and Choi [Bowles S, Choi J-K (2013) Proc Natl Acad Sci USA 110(22):8830–8835]. We vary 11 parameters simultaneously within defined ranges over 12 million simulations to ensure better exploration of parameter space. We also introduce a new method – fitting to idealized outcomes (FIO) – which permits identification of potential parameter interactions. Our FIO approach is analogous to approximate Bayesian computation and allows us to infer the optimum conditions under which farming would evolve, given our model. Our results reveal previously unidentified model behaviours. By setting our ‘ideal outcome’ as farming being fully established by 9,000 yBP, we show that the key factors for its emergence include farming-friendly property rights (supporting Bowles and Choi’s original work), group structuring and size, and conservatism . Furthermore we find that for farming to emerge it is not essential for farming productivity to be greater than that of foraging.