Communication Enhances LLMs' Stability in Strategic Thinking
Nunzio Lore, Babak Heydari

TL;DR
This paper shows that simple, costless communication can significantly improve the stability and predictability of strategic behavior in large language models during multi-agent interactions, especially in volatile models.
Contribution
It demonstrates that cheap-talk style communication reduces variability in LLMs' strategic decisions, enhancing stability across different models and contexts.
Findings
Communication reduces trajectory noise in LLMs' strategic behavior
The stabilizing effect is consistent across multiple prompt variants
Higher baseline volatility models benefit most from communication
Abstract
Large Language Models (LLMs) often exhibit pronounced context-dependent variability that undermines predictable multi-agent behavior in tasks requiring strategic thinking. Focusing on models that range from 7 to 9 billion parameters in size engaged in a ten-round repeated Prisoner's Dilemma, we evaluate whether short, costless pre-play messages emulating the cheap-talk paradigm affect strategic stability. Our analysis uses simulation-level bootstrap resampling and nonparametric inference to compare cooperation trajectories fitted with LOWESS regression across both the messaging and the no-messaging condition. We demonstrate consistent reductions in trajectory noise across a majority of the model-context pairings being studied. The stabilizing effect persists across multiple prompt variants and decoding regimes, though its magnitude depends on model choice and contextual framing, with…
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Taxonomy
TopicsLanguage and cultural evolution · Topic Modeling · Artificial Intelligence in Healthcare and Education
