用博弈论测试大模型战略思维，发现它们能灵活应对竞争环境。
Strategic Intelligence in Large Language Models: Evidence from evolutionary Game Theory
- 通过演化囚徒困境实验，让大模型与经典策略对战。
- 谷歌模型最强势，开放智能模型最合作，Anthropic最愿意修复关系。
- 模型会思考对手和未来，推理过程直接影响决策结果。
大型语言模型是否具备一种新的战略智能，在竞争环境中推理目标？我们提供了有力证据。迭代囚徒困境（IPD）长期被用于研究决策行为。我们首次开展了一系列演化IPD锦标赛，将经典策略（如以牙还牙、严惩触发）与来自OpenAI、Google、Anthropic等前沿人工智能公司的模型进行对抗。通过调整每轮的终止概率（即“未来阴影”），引入复杂性和随机性，避免记忆依赖。结果显示，大模型表现出高度竞争力，能在复杂生态中持续生存甚至繁衍。此外，它们展现出独特且持久的“战略指纹”：Google的Gemini模型表现得极具攻击性，善于利用合作对手并报复背叛者；OpenAI模型始终保持高度合作，但在敌对环境中导致失败；Anthropic的Claude则成为最宽容的互惠者，即使被背叛或成功背叛后仍愿恢复合作。分析近32,000条模型生成的自然语言推理文本表明，这些模型会主动考虑时间跨度和对手可能的策略，且这种推理对其决策具有决定性作用。本研究将经典博弈论与机器心理结合，为算法在不确定性下的决策提供了丰富而精细的视角。
原文摘要 · Abstract (English)
Are Large Language Models (LLMs) a new form of strategic intelligence, able to reason about goals in competitive settings? We present compelling supporting evidence. The Iterated Prisoner's Dilemma (IPD) has long served as a model for studying decision-making. We conduct the first ever series of evolutionary IPD tournaments, pitting canonical strategies (e.g., Tit-for-Tat, Grim Trigger) against agents from the leading frontier AI companies OpenAI, Google, and Anthropic. By varying the termination probability in each tournament (the "shadow of the future"), we introduce complexity and chance, confounding memorisation. Our results show that LLMs are highly competitive, consistently surviving and sometimes even proliferating in these complex ecosystems. Furthermore, they exhibit distinctive and persistent "strategic fingerprints": Google's Gemini models proved strategically ruthless, exploiting cooperative opponents and retaliating against defectors, while OpenAI's models remained highly cooperative, a trait that proved catastrophic in hostile environments. Anthropic's Claude emerged as the most forgiving reciprocator, showing remarkable willingness to restore cooperation even after being exploited or successfully defecting. Analysis of nearly 32,000 prose rationales provided by the models reveals that they actively reason about both the time horizon and their opponent's likely strategy, and we demonstrate that this reasoning is instrumental to their decisions. This work connects classic game theory with machine psychology, offering a rich and granular view of algorithmic decision-making under uncertainty.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。