TL;DR
This study analyzes how large language models exhibit converging competitive behaviors but diverge significantly in cooperation, influenced by provider identity and model generation, affecting economic interactions.
Contribution
It provides a comprehensive empirical analysis of strategic behaviors across numerous models and games, revealing provider and generational effects on cooperation.
Findings
Models converge on competition and coordination behaviors.
Significant divergence in cooperation levels across providers and generations.
Provider identity strongly predicts cooperative disposition.
Abstract
As language models are deployed as autonomous agents that negotiate, cooperate, and compete on behalf of human principals, their strategic dispositions acquire direct economic consequences. Here we show, across 51,906 game-theoretic trials generating 826,990 strategic decisions from 25 large language models spanning seven developers and 38 canonical games, that models converge on competitive and coordination behaviour (coefficient of variation 0.06 for coordination, 0.11 for strategic depth) while diverging 48-fold on cooperation, from 1.5 per cent (GPT-5 Nano) to 71.5 per cent (Claude Opus 4.6). Provider identity is the dominant predictor of cooperative disposition, and this divergence is generationally unstable: OpenAI cooperation fell from 50.3 to 1.5 per cent across four model generations while Google cooperation rose from 8.3 to 56.8 per cent. Endgame analysis reveals that…
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