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

• Published in: Risk analysis 2023-12, Vol.43 (12), p.2659-2670
• Main Authors: Johnson, Paul M., Baroud, Hiba, Brady, Corey E., Abkowitz, Mark
• Format: Article
• Language: English
• Subjects: Aversion; Bayes Theorem; Bayesian additive regression tree; Bayesian analysis; big five personality; Community; Community planning; community resilience; Decision analysis; Decision Making; Decision theory; Disaster insurance; Disaster management; Disaster Planning; Disasters; Emergency preparedness; Game Theory; Games; Humans; Infrastructure; Insurance; Learning; Mathematical models; Openness; Regression analysis; Resilience; Resilience, Psychological; Risk; Risk assessment; Risk aversion; social dilemma; Social dilemmas; Statistical analysis; Statistical methods
• ISSN: 0272-4332; 1539-6924; 1539-6924
• DOI: 10.1111/risa.14116

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.