  Agent-based modeling of artificial societies offers a platform to test
human-interpretable, causal explanations of human behavior that generate
society-scale phenomena. However, parameter calibration is insufficient to
conduct an adequate data-driven exploration of the importance of causal factors
that constitute agent rules, resulting in models with limited causal accuracy
and robustness. We introduce evolutionary model discovery, a framework that
combines genetic programming and random forest regression to evaluate the
importance of a set of causal factors hypothesized to affect the individual's
decision-making process. We investigated the farm plot seeking behavior of the
ancestral Pueblo of the Long House Valley simulated in the Artificial Anasazi
model our proposed framework. We evaluated the importance of causal factors not
considered in the original model that we hypothesized to have affected the
decision-making process. Contrary to the original model, where closeness was
the sole factor driving farm plot selection, selection of higher quality land
and desire for social presence are shown to be more important. In fact, model
performance is improved when agents select farm plots further away from their
failed farm plot. Farm selection strategies designed using these insights into
the socio-agricultural behavior of the ancestral Pueblo significantly improved
the model's accuracy and robustness.
