2025/12/09 by Mangold, Benedikt
Business, Management and Accounting · Social Sciences · #Artificial Intelligence (cs.AI) #Business Law and Ethics #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Innovation, Sustainability, Human-Machine Systems #Multiagent Systems (cs.MA) #Social Power and Status Dynamics
paper · doi:10.48550/arxiv.2512.08345
openalex publication_date 2025/12/09 · openalex created_date 2025/12/11 · openalex updated_date 2026/07/28
Workplace toxicity is widely recognized as detrimental to organizational culture, yet quantifying its direct impact on operational efficiency remains methodologically challenging due to the ethical and practical difficulties of reproducing conflict in human subjects. This study leverages Large Language Model (LLM) based Multi-Agent Systems to simulate 1-on-1 adversarial debates, creating a controlled "sociological sandbox". We employ a Monte Carlo method to simulate hundrets of discussions, measuring the convergence time (defined as the number of arguments required to reach a conclusion) between a baseline control group and treatment groups involving agents with "toxic" system prompts. Our results demonstrate a statistically significant increase of approximately 25% in the duration of conversations involving toxic participants. We propose that this "latency of toxicity" serves as a proxy for financial damage in corporate and academic settings. Furthermore, we demonstrate that agent-based modeling provides a reproducible, ethical alternative to human-subject research for measuring the mechanics of social friction.