The Mechanism of Manipulation: Dynamically-Stabilized Certainty Traps and Strategic Frame Control

This paper develops a general mechanism by which a strategic actor can actively engineer and sustain dynamically-stabilized certainty traps (DSCTs) against an adaptive, prediction-error learner. When a sender can choose payoff frames ex post after observing receiver actions, they can dose intermittent rewards to regulate temporal-difference residuals at an exploitative indifference point, thereby maintaining a self-confirming equilibrium that resists falsification. The paper formalizes DSCT as a closed-loop control process, generalizes the mechanism across the class of prediction-error learners, and links it to equilibrium concepts in game theory. It demonstrates how DSCT dynamics arise in institutional and algorithmic environments—such as digital platforms and reinforcement learning from human feedback—where powerful principals can shift operative payoff mappings after the fact. The analysis includes theoretical results, computational evidence, detection signatures, and implications for AI alignment, platform governance, and institutional design.