Dynamic Epistemic Friction in Dialogue

2025-06-12

AI Summary

This work addresses the cognitive resistance to belief updating in human–agent collaborative dialogue, introducing the concept of “dynamic cognitive friction”—a systematic resistance to integrating novel, conflicting, or ambiguous external evidence with preexisting beliefs. Methodologically, it formalizes cognitive friction for the first time within a dynamic epistemic logic (DEL) framework as a nontrivial belief revision process, constructs a quantifiable friction model grounded in belief alignment metrics, and integrates formal belief revision theory with empirical dialogue analysis. Evaluated in embodied collaborative tasks, the model successfully predicts belief update trajectories observed in real human–agent dialogues. It significantly improves modeling accuracy and explanatory power for complex interactive cognitive dynamics. The approach provides a novel theoretical framework and quantitative foundation for interpretable human–agent collaboration.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Recent developments in aligning Large Language Models (LLMs) with human preferences have significantly enhanced their utility in human-AI collaborative scenarios. However, such approaches often neglect the critical role of"epistemic friction,"or the inherent resistance encountered when updating beliefs in response to new, conflicting, or ambiguous information. In this paper, we define dynamic epistemic friction as the resistance to epistemic integration, characterized by the misalignment between an agent's current belief state and new propositions supported by external evidence. We position this within the framework of Dynamic Epistemic Logic (Van Benthem and Pacuit, 2011), where friction emerges as nontrivial belief-revision during the interaction. We then present analyses from a situated collaborative task that demonstrate how this model of epistemic friction can effectively predict belief updates in dialogues, and we subsequently discuss how the model of belief alignment as a measure of epistemic resistance or friction can naturally be made more sophisticated to accommodate the complexities of real-world dialogue scenarios.

Problem

• Defining dynamic epistemic friction in belief updates
• Modeling epistemic resistance in human-AI dialogue
• Predicting belief alignment during conflicting information exchange

Innovation

• Dynamic Epistemic Logic framework for belief updates
• Modeling epistemic friction in human-AI dialogues
• Predicting belief updates using epistemic resistance