🤖 AI Summary
This paper addresses two fundamental questions: (1) why humans remain susceptible to persuasion even under full information, and (2) why persuasion activities—such as litigation and strategic communication—persistently demand high human labor. We formalize persuasion as a computational decision problem for the first time, proving that persuasive message generation is NP-hard, whereas message adoption lies in NP—revealing an intrinsic computational asymmetry: “generation is hard, adoption is easy.” This complexity-theoretic result provides a foundational explanation for human cognitive vulnerability to manipulation and clarifies that high human costs stem from the intractability of searching for optimal persuasive strategies. Our work establishes the theoretical groundwork for automated persuasion and adversarial manipulation risk analysis in AI-driven societies, advancing paradigmatic integration between computational social science and human-computer interaction.
📝 Abstract
As generative foundation models improve, they also tend to become more persuasive, raising concerns that AI automation will enable governments, firms, and other actors to manipulate beliefs with unprecedented scale and effectiveness at virtually no cost. The full economic and social ramifications of this trend have been difficult to foresee, however, given that we currently lack a complete theoretical understanding of why persuasion is costly for human labor to produce in the first place. This paper places human and AI agents on a common conceptual footing by formalizing informational persuasion as a mathematical decision problem and characterizing its computational complexity. A novel proof establishes that persuasive messages are challenging to discover (NP-Hard) but easy to adopt if supplied by others (NP). This asymmetry helps explain why people are susceptible to persuasion, even in contexts where all relevant information is publicly available. The result also illuminates why litigation, strategic communication, and other persuasion-oriented activities have historically been so human capital intensive, and it provides a new theoretical basis for studying how AI will impact various industries.