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Purpose This study investigates the key factors and evolutionary dynamics of collaborative governance in addressing artificial intelligence-generated content (AIGC) disinformation. Design/methodology/approach A tripartite evolutionary game model (EGM) is developed to analyze interactions among users, social media, and the government. The model integrates Bounded Rationality Theory (BRT), Deterrence Theory (DT), and Public Goods Theory (PGT). Evolutionary stable strategies are derived using the Jacobian matrix and Lyapunov's first method, supported by numerical simulations and sensitivity analyses. Findings Social media and the government's initial willingness are vital for accelerating users' transition to compliant behavior through adaptive learning of boundedly rational agents. Due to proximity effects, users are more responsive to social media penalties than government sanctions, revealing an asymmetric deterrence effect. Stronger government regulation reduces social media's incentive for active governance. This creates a free-rider dilemma that requires carefully designed subsidies and penalties to curb opportunistic behavior. Cost-benefit dynamics determine optimal governance – reducing costs or improving effectiveness fosters active governance and lenient regulation, while high costs lead to instability – highlighting the importance of technological innovation for sustainable governance. Originality/value This study extends EGM and BRT to generative artificial intelligence governance by integrating AI-specific parameters into AIGC disinformation analysis. It refines DT and PGT by revealing asymmetric deterrence effects and formalizing the free-rider dilemma. It proposes a tripartite governance framework that unifies BRT, DT, and PGT and identifies cost-benefit dynamics that challenge the assumption that “more regulation is better.” These findings offer a flexible framework and actionable strategies for collaborative governance of AIGC disinformation.
Zhang et al. (Wed,) studied this question.