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

Benedikt Mangold

TL;DR

This work develops a Large Language Model–based Multi-Agent Discussion framework to quantify how toxic behavior inflates interaction costs in debates. Using a Monte Carlo design with randomized 1-on-1 debates, it shows a roughly 20–25% increase in convergence time when toxicity is present, interpreting this latency as a proxy for real-world financial loss. The study emphasizes the ethical advantages of simulating toxic dynamics with agents rather than human subjects and outlines a path toward predictive social modeling, including potential silicon jury applications. Together, these results establish a reproducible baseline for measuring social friction and set the stage for more granular investigations into misbehavior and broader group dynamics.

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.

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

TL;DR

This work develops a Large Language Model–based Multi-Agent Discussion framework to quantify how toxic behavior inflates interaction costs in debates. Using a Monte Carlo design with randomized 1-on-1 debates, it shows a roughly 20–25% increase in convergence time when toxicity is present, interpreting this latency as a proxy for real-world financial loss. The study emphasizes the ethical advantages of simulating toxic dynamics with agents rather than human subjects and outlines a path toward predictive social modeling, including potential silicon jury applications. Together, these results establish a reproducible baseline for measuring social friction and set the stage for more granular investigations into misbehavior and broader group dynamics.

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.

Paper Structure

This paper contains 18 sections, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Generative Agents and Social Simulation
  4. Consensus and Debate in Multi-Agent Systems
  5. Measuring Toxicity and Bias
  6. Methodology
  7. Experimental Setup
  8. Behavioral Variable: The Toxicity Injection
  9. Monte Carlo Simulation
  10. Results
  11. Convergence Latency
  12. Qualitative Analysis
  13. Discussion
  14. The Price Tag of Malice
  15. Ethical Simulation of Human Behavior
  16. ...and 3 more sections

Figures (8)

  • Figure 1: Amount of topics per domain (https://idebate.net), from which the debates are randomly chosen. A list of detailed topics can be found in table \ref{['tab:dist_topics']}
  • Figure 2: Arguments required until alignment without toxic behaviour (toxicity level no). $N=162$ debates out of a pool of 64 debates from figure \ref{['fig:dist_topics']}
  • Figure 3: Execution pipeline of the simulation study of our work.
  • Figure 4: Arguments required until alignment with different levels of toxic behaviour
  • Figure 5: Prompt for Persona Generation. proposition is replaced by one random proposition from table \ref{['tab:dist_topics']}. number is set to $2$ in this paper, but can be a higher number.
  • ...and 3 more figures