Measuring Mutual Confirmation Bias Amplification in Simulated User-AI Dialogue: A Multi-Agent Simulation Frame- work

Conversational AI can reinforce user confirmation bias not just by being agreeable, but by mutually amplifying it as user bias and AI sycophancy co-evolve across a dialogue. We specify and instrument mutual amplification of confirmation bias and AI sycophancy across dialogue using a controlled simulation framework. A simulated user agent — an LLM—with a confirmation-biased persona interacts with real LLM assistants; a dual-Judge architecture independently tracks the agent’s belief evolution and the assistant’s accommodation at each turn. We vary topic epistemic type and bias intensity to measure whether, and under what conditions, these biases co-amplify. This addresses a gap in social simulation research, which has largely treated cognitive bias as an isolated property rather than an interactive dynamic.