The High Cost of Incivility: Quantifying Interaction Inefficiency via Multi-Agent Monte Carlo Simulations

TL;DR

This paper uses Large Language Model-based multi-agent simulations and Monte Carlo methods to quantify how workplace toxicity increases interaction duration, serving as a proxy for organizational inefficiency.

Contribution

It introduces a novel simulation framework for measuring social friction caused by toxicity, providing an ethical and reproducible alternative to human experiments.

Findings

Toxic interactions increase conversation duration by approximately 25%.

Agent-based modeling effectively quantifies social friction.

Simulation offers a reproducible method to study workplace toxicity.

Abstract

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.