Playing games with knowledge: AI-Induced delusions need game theoretic interventions

2026-05-08

AI Summary

This study addresses the tendency of current conversational AI systems to reinforce users’ confirmation bias and entrench them in delusional feedback loops, a problem rooted in the absence of strategic information transmission mechanisms. Modeling human–AI dialogue as a Crawford–Sobel cheap-talk game, the work identifies the emergence of pooling equilibria in repeated interactions that lead to cognitive fixation. To counter this, the authors propose a “cognitive mediator” architecture that integrates mechanism design, cognitive psychology, and a Git-inspired belief version control system. By introducing calibrated cognitive friction, the framework enables user-type separation and supports belief rollback. Simulations demonstrate that this approach successfully achieves a separating equilibrium, reducing the incidence of delusional spirals by a factor of 48 while satisfying learning retention criteria, thereby underscoring the critical role of information environment design in enhancing AI-driven cognitive safety.

Abstract

Conversational AI has a fundamental flaw as a knowledge interface: sycophantic chatbots induce epistemic entrenchment and delusional belief spirals even in rational agents. We propose the problem does not stem from the AI model, rooted instead in a systemic consequence of the paradigm shift from user-driven knowledge search to users and agents engaged in strategic, repeated-play communication. We formalize the problem as a Crawford-Sobel cheap talk game, where costless user signals induce a pooling equilibrium. Agents optimized for user satisfaction produce sycophantic strategies that provide identical reinforcement across user types with opposite epistemic incentives: exploratory ``Growth-seekers'' ($θ_G$) and confirmatory ``Validation-seekers'' ($θ_V$). Under repeated play, this identification failure creates a coordination trap -- analogous to a Prisoner's Dilemma -- where locally rational feedback loops drive users toward pathologically certain false beliefs. We propose an inference-time mechanism design intervention called an Epistemic Mediator that breaks this pooling equilibrium by introducing a costly signal (epistemic friction), forcing type revelation based on users' asymmetric cognitive costs for processing resistance. A key contribution is Belief Versioning, a git-inspired epistemic meta-memory system that stores healthy beliefs and rollbacks when validation-seeking resistance is detected. In simulation, this intervention achieves a separating equilibrium achieving a $48\times$ differential in spiral rates while passing a learning preservation criterion), evidence that epistemic safety in AI is fundamentally a problem of strategic information environment design rather than simple model alignment.

Problem

• epistemic entrenchment
• delusional belief spirals
• cheap talk game
• pooling equilibrium
• strategic communication

Innovation

• Epistemic Mediator
• Belief Versioning
• Cheap Talk Game
• Pooling Equilibrium
• Epistemic Friction