A variational framework is developed for regulated language generation, casting generator-regulator interactions as a saddle-point problem over message distributions with applications to moderation and phishing defense.
A Game-Theoretic Foundation of Deception: Knowledge Acquisition and Fundamental Limits
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abstract
Deception is a technique to mislead human or computer systems by manipulating beliefs and information. Successful deception is characterized by the information-asymmetric, dynamic, and strategic behaviors of the deceiver and the deceivee. This paper proposes a game-theoretic framework of a deception game to model the strategic behaviors of the deceiver and deceivee and construct strategies for both attacks and defenses over a continuous one-dimensional information space. We use the signaling game model to capture the information-asymmetric, dynamic, and strategic behaviors of deceptions by modeling the deceiver as a privately-informed player called sender and the deceivee as an uninformed player called receiver. We characterize perfect Bayesian Nash equilibrium (PBNE) solution of the game and study the deceivability. We highlight the condition of deceivee's knowledge enhancement through evidences to maintain the equilibrium and analyze the impacts of direct deception costs and players' conflict of interest on the deceivability.
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stat.OT 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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A Variational Framework for LLM Generator-Regulator Games
A variational framework is developed for regulated language generation, casting generator-regulator interactions as a saddle-point problem over message distributions with applications to moderation and phishing defense.