Optimizing Emergency Response by Digital Spontaneous Volunteers: Insight From Agent‐Based Modeling Analysis

The efficiency of emergency response is crucial, yet traditional top‐down systems are often overwhelmed. Digital spontaneous volunteers (DSVs) offer a vital bottom‐up force, but their effectiveness is frequently constrained by a dual dilemma of external integration and internal coordination. This study explores how to optimize DSV crowdsourcing by investigating the role of sustained trust from formal organizations and the logic of adaptive crowdsourcing based on complex adaptive systems theory. Using an agent‐based model calibrated with data from the “life‐saving document” case during China's 2021 Henan rainstorm, we conducted counterfactual experiments. The results demonstrate that sustained trust from formal organizations is fundamental; its erosion leads to a collapse in rescue efficiency, even with highly accessible information channels. Furthermore, the study reveals a counterintuitive finding: Adaptive crowdsourcing significantly improves efficiency not by maximizing volunteer numbers, but by restraining their generation based on real‐time demand gaps. This research highlights that the effectiveness of DSV crowdsourcing hinges on dynamic trust‐building and controlled, adaptive coordination, offering a conceptual shift from viewing self‐organization as an uncontrollable force to a system that can be optimized through design.