An evolutionary game analysis of dual-model governance for false advertising in live e-commerce: multi-agent and complex network perspectives

Abstract The pervasive false advertising in live-commerce ecosystems has significantly eroded consumer trust and posed systemic risks to market integrity. This study proposes a dual-pronged governance framework by categorizing deceptive practices into unintentional and intentional false advertising. For unintentional false advertising, a tripartite evolutionary game model among live-commerce hosts , merchants, and platform is developed to analyze strategic interactions under information asymmetry. For intentional false advertising, a bipartite evolutionary game model with hybrid updating rules is constructed on complex networks to examine intra-host competition dynamics, incorporating critical parameters such as consumer types and regulatory coverage rate. Findings reveal that increasing hosts’ probability of cooperating with dishonest merchants encourages information concealment by merchants, while platform’s enhanced monitoring effectively mitigates unintentional false advertising. In intentional false advertising scenarios, significant strategy divergence exists across host hierarchies, with moderate network scale and higher proportions of discerning consumers promoting truthful content. Government regulators should adopt moderate regulatory coverage rates coupled with stringent penalties, while platform needs to implement efficient monitoring systems combined with proportionate fines to stabilize truthful advertising equilibria.