Who contributes to disaster preparedness? Predicting decision making in social dilemmas pertaining to community resilience

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. 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. 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. 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. However, higher trait Openness scores reflect an individual's tendency to follow a risk-neutral strategy, and fewer available resources predict lower perceived utilities derived from the infrastructure developments. In addition, several input variables have nonlinear effects on decisions, suggesting that it may be warranted to use more sophisticated statistical learning methods to reexamine results from previous studies that assume linear relationships between individuals' dispositions and responses in applications of game theory or decision theory.

Automated summary

This study used statistical learning techniques to analyze participants' investment decisions in public infrastructure projects aimed at improving community resilience to disasters. The results showed that participants tend to overcontribute, indicating a general risk aversion. However, individuals with higher trait Openness scores were more inclined to adopt a risk-neutral strategy, while fewer available resources resulted in lower perceived utilities from infrastructure developments. Additionally, some input variables had nonlinear effects on decisions, suggesting the need for more sophisticated statistical learning methods to reexamine previous research that assumes linear relationships between individuals' dispositions and responses in applications of game theory or decision theory.