mlsanityData to 7 Oct 2026

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

arXiv 2512.08345 ↗First posted 9 Dec 2025cs.AI cs.CL cs.CYcited by 1

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 7 of its 9 references that are in the map. First posted on arXiv 9 Dec 2025.

Builds on

7 of 9 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument Generation28 Jun
  2. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 5

  1. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  2. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  3. 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
  4. 2022Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studiescited by 506
  5. 2023Playing repeated games with Large Language Modelscited by 213

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  1. 2023MetaAgents: Large Language Model Based Agents for Decision-Making on Teaming
  2. 2026Harm in AI-Driven Societies: An Audit of Toxicity Adoption on Chirper.ai
  3. 2025SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
  4. 2024Simulating Human Strategic Behavior: Comparing Single and Multi-agent LLMs
  5. 2026Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits
  6. 2024Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-Agent Debates
  7. 2025Towards Operational Validation of LLM-Agent Social Simulations: A Replicated Study of a Reddit-like Technology Forum
  8. 2026An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents