How generative AI reshapes principal-agent relationships: mitigating adverse selection and moral hazard

In the era of digitalization, the rapid proliferation of information and advancements in artificial intelligence have spurred an unprecedented increase in data generation and dissemination. However, these developments have also led to significant challenges in information pollution, adversely affecting market dynamics and societal welfare. This paper develops an integrated economic model that employs Pigouvian taxes, tradable permits, and network diffusion theories to capture both the direct and indirect externalities of information pollution. By incorporating intertemporal decision-making, long-term equilibrium analysis, and the effects of technological innovation, the proposed framework provides robust tools for optimizing pollution control and maximizing social welfare. The insights derived herein offer valuable guidance for policymakers seeking to mitigate the negative impacts of information pollution in digital economies.