Community-informed Decisions for Equitable, Cost-effective, and Inclusive Disaster Resilience Planning (Co-DECIDR): A modeling approach

Evaluating coping strategies for community resilience planning in the face of natural hazards and disasters presents significant challenges, including limited system clarity, diverging community priorities, resource inequalities, and inherent uncertainties. Traditional methods — often reliant on economic metrics through benefit-cost analysis — primarily assess monetary aspects, overlooking critical non-monetary factors. The increasing shift towards transdisciplinary methods underscores the importance of integrating local community values into resilience planning. This research introduces the Co-DECIDR (Community-informed Decisions for Equitable, Cost-effective, and Inclusive Disaster Resilience) modeling framework, designed to evaluate resilience planning alternatives by embracing community-identified values, leveraging local insights, navigating uncertainties, and balancing monetary with non-monetary considerations. By merging Fuzzy Cognitive Mapping (FCM) for qualitative depth with Benefit-Cost Analysis (BCA) for quantitative rigor, Co-DECIDR enables a comprehensive understanding of complex social-ecological systems. The framework utilizes accessible online tools — Mental Modeler for FCM and Economic Decision Guide Software (EDGe$) for BCA — to enhance user engagement and model efficacy. Demonstrated through proof-of-concept examples in Flint, Michigan, focusing on resilience to extreme weather events and pandemics like COVID-19, this study showcases Co-DECIDR’s practicality and adaptability in real-world contexts. These examples highlight the framework's capacity to support informed, community-centric, and equitable resilience planning. Furthermore, Co-DECIDR advances good modeling practice by utilizing participatory modeling in resilience planning to bridge the gap between modelers and end-users. It systematically captures stakeholder requirements, investigates subjectivity, addresses collaborative modeling challenges, encourages the engagement of diverse groups, and adopts a comprehensive approach to assessing model performance, embracing both qualitative and quantitative criteria.