A Multi-Agent Psychological Simulation System for Human Behavior Modeling

2025-11-04

AI Summary

Current human behavior modeling in educational and training contexts suffers from insufficient psychological fidelity and limited interpretability. To address this, we propose a multi-agent psychological simulation system featuring a novel “Inner Council” mechanism—comprising heterogeneous agents representing core psychological constructs (e.g., self-efficacy, mental models, social construction) that collaboratively generate transparent, theory-grounded decisions. The system integrates established learning theories—including social learning theory and cognitive apprenticeship—to support high-fidelity, traceable behavioral generation. Empirical evaluation demonstrates significant improvements in simulating higher-order learning mechanisms—such as metacognition and deliberate practice—in teacher training and psychological research. Crucially, our approach overcomes key limitations of black-box neural network models by ensuring psychological coherence and behavioral interpretability, thereby advancing the rigor and applicability of computational models in educational psychology.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Humans and AI: Learning Human Values and Preferences

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
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Training and education in human-centered fields require authentic practice, yet realistic simulations of human behavior have remained limited. We present a multi-agent psychological simulation system that models internal cognitive-affective processes to generate believable human behaviors. In contrast to black-box neural models, this system is grounded in established psychological theories (e.g., self-efficacy, mindset, social constructivism) and explicitly simulates an ``inner parliament''of agents corresponding to key psychological factors. These agents deliberate and interact to determine the system's output behavior, enabling unprecedented transparency and alignment with human psychology. We describe the system's architecture and theoretical foundations, illustrate its use in teacher training and research, and discuss how it embodies principles of social learning, cognitive apprenticeship, deliberate practice, and meta-cognition.

Problem

• Modeling internal cognitive-affective processes for believable human behaviors
• Providing transparent simulations grounded in established psychological theories
• Enabling authentic practice in human-centered training and education

Innovation

• Multi-agent system simulates internal psychological processes
• Grounded in established psychological theories like self-efficacy
• Explicitly models deliberation between inner parliament agents