Identifying Narrative Descriptions in Agent-Based Models Representing Past Human-Environment Interactions

There is a growing use of bottom-up simulation models to reconstruct past human-environment interactions. Such detailed representations pose difficult questions not only in their design (the generality-realism trade-off) but also about the inferences that are made from them. The historical sciences are faced with seeking to make robust inferences from limited, potentially biased and/or incomplete samples from uncontrolled systems, and as a result have sometimes employed narrative explanation. By contrast, simulation models can be used experimentally and can generate large amounts of data. Here, using an agent-based model of hunter-gatherer foraging in a previously unexplored ecosystem, we consider how narratives might be identified from the trajectories produced by simulations. We show how machine learning methods can isolate qualitatively similar types of model behaviour based on summaries of model outcomes and time series. We stand to learn from this approach because it enables us to answer two questions: (i) under what conditions (representations and/or parameterisations) do we observe in the model what is recorded in the archaeological and/or palaeoenvironmental record? and (ii) does the model yield unobserved dynamics? If so, are they plausible? Using models to develop narratives is a logical extension of the bottom-up approach inherent in agent-based modelling and has the potential, alongside conventional methods of model evaluation, to aid in learning from the rich dynamics of such simulations.