Participants tend to overcontribute relative to these Pareto-efficient strategies, indicating general risk aversion that is analogous to individuals purchasing disaster insurance even though it exceeds expected actuarial costs. Given participants' dispositions and in-game circumstances, Bayesian additive regression tree (BART) models are able to accurately predict deviations from players' decisions that would reasonably lead to Pareto-efficient outcomes for their communities. Using statistical learning techniques trained on the results of a web-based common pool resource game, we analyze participants' decisions to invest in hypothetical public infrastructure projects that bolster their community's resilience to disasters. Planning for community resilience through public infrastructure projects often engenders problems associated with social dilemmas, but little work has been done to understand how individuals respond when presented with opportunities to invest in such developments.
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