Introducing A Multi-Agent System Approach to Psychological Safety in Diverse Teams: An Organizational Behavior Perspective

In modern organizational behavior and design research, implying machine learning techniques have become the major computational approach, machine learning can excel tasks with labeled data. However, they cannot explain why these patterns emerge, are not equipped to test unknown interventions, and are incapable of exploring causal mechanisms underlying organizational phenomena. In this study, we introduce a multi-agent system approach to simulate 2160 teams and we identify psychological safety is the main factor of diversity’s impact. The team’s performance reached a peak when diversity ranges from 0.68–0.72, it delivered a massive 34% gain—under the condition that psychological safety surpasses 0.53. Reversing its effect from negative (r = −0.34) to positive (r = 0.52). The performance improved 38% despite the conflict having decreased 64%. Especially the early-stage behavioral shaping is 3.8 times more effective than delayed interventions. Behavioral patterns: Empathic mirroring amplifies psychological safety (+0.23), while exclusion mitigates it (−0.31). Multi-agent system discloses the fundamental structure—psychological safety lessens diversity’s organization costs via ref ined communication channels. The model’s validation (r = 0.71) confirms the robust predictive power, and optimized teams operate 47% better—these insights have not been discovered by machine learning frameworks. Keywords: Multi-Agent Systems, Organizational Behavior and Design, Psychological Safety, Team Diversity, Agent-Based Modeling