Public responses to disaster warnings from government versus AI: Evidence from behavioral experiments

Based on two online experiments with residents in China (N = 599), this study investigates the psychological processes of information trust and anticipated regret that shape how the public responds to different warning sources and consistency. The findings advance understanding of how individuals respond to disaster warnings in multi-source information environments.

The authors conclude that government-issued warnings generate higher trust than AI warnings, and consistent messages from both sources further enhance trust and protective intentions. In conflicting warning scenarios, anticipated regret becomes prominent, contributing to individuals’ tendency to follow the higher-level warning. The results support a dual-pathway conceptual framework of decision making in multi-source warning contexts, where cognitive trust grounded in institutional authority and technological support coexists with emotional motivation driven by anticipated regret. This study fills an empirical gap in multi-source warning research and offers theoretical and practical insights for building disaster warning systems that integrate institutional credibility with emerging AI technologies.