AI Contagion in Social Networks

Abstract:We study how artificial intelligence (AI) interacts with social communication networks to shape the stability of collective knowledge. Agents exchange information through a network while AI systems generate content and retrain on the aggregate informational environment they influence. This interaction creates a recursive feedback loop in which informational distortions diffuse through society and subsequently feed back into future AI outputs. Despite the high dimensionality of the environment, we show that the long-run dynamics admit a two-dimensional representation whose spectral radius completely characterizes the stability of AI-mediated information systems. We derive a sharp regulatory frontier identifying the minimum filtering required for stability and show how homophily and core-periphery network structures shape systemic informational risk.

Theoretical Economics (econ.TH); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Social and Information Networks (cs.SI)

arXiv:2606.15206 [econ.TH]

(or arXiv:2606.15206v2 [econ.TH] for this version)

https://doi.org/10.48550/arXiv.2606.15206 Focus to learn more arXiv-issued DOI via DataCite

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