Enhancing Persuasive Dialogue Agents by Synthesizing Cross-Disciplinary Communication Strategies

2026-02-26

AI Summary

This work proposes a novel dialogue agent framework that integrates effective communication strategies from social psychology, behavioral economics, and communication studies to address the limitations of existing persuasive agents, which often rely on predefined tactics and struggle with the dynamic complexity of real-world interactions. By systematically unifying multidisciplinary persuasion mechanisms, the proposed approach significantly enhances persuasive efficacy—particularly for users with low initial willingness—and improves cross-scenario generalization. Leveraging interdisciplinary strategy modeling, dialogue system design, and advanced natural language understanding and generation techniques, the method achieves substantially higher persuasion success rates on both the Persuasion for Good and DailyPersuasion datasets, with especially strong performance on challenging cases.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Current approaches to developing persuasive dialogue agents often rely on a limited set of predefined persuasive strategies that fail to capture the complexity of real-world interactions. We applied a cross-disciplinary approach to develop a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory. We validated our proposed framework through experiments on two distinct datasets: the Persuasion for Good dataset, which represents a specific in-domain scenario, and the DailyPersuasion dataset, which encompasses a wide range of scenarios. The proposed framework achieved strong results for both datasets and demonstrated notable improvement in the persuasion success rate as well as promising generalizability. Notably, the proposed framework also excelled at persuading individuals with initially low intent, which addresses a critical challenge for persuasive dialogue agents.

Problem

• persuasive dialogue agents
• cross-disciplinary communication
• persuasion strategies
• dialogue systems
• behavioral complexity

Innovation

• cross-disciplinary
• persuasive dialogue agents
• behavioral economics
• social psychology
• communication strategies