Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that assigning personality traits to LLMs can influence their behavior, how personality steering affects cooperation under controlled conditions remains unclear. In this study, we examine the effects of personality steering on cooperative behavior in LLM agents using repeated Prisoner's Dilemma games. Based on the Big Five framework, we first measure basic personality scores of three models, GPT-3.5-turbo, GPT-4o, and GPT-5, using the Big Five Inventory. We then compare behavior under baseline and personality-informed conditions, and further analyze the effects of independently manipulating each personality dimension to extreme values. Our results show that agreeableness is the dominant factor promoting cooperation across all models, while other personality traits have limited impact. Explicit personality information increases cooperation but can also raise vulnerability to exploitation, particularly in earlier-generation models. In contrast, later-generation models exhibit more selective cooperation. These findings indicate that personality steering acts as a behavioral bias rather than a deterministic control mechanism.

翻译：大型语言模型（LLMs）正日益作为自主智能体应用于战略性和社会性交互中。尽管近期研究表明，为LLMs赋予人格特质能够影响其行为，但在受控条件下人格引导如何影响合作行为仍不明确。本研究通过重复囚徒困境博弈，考察了人格引导对LLM智能体合作行为的影响。基于大五人格框架，我们首先使用大五人格量表测量了GPT-3.5-turbo、GPT-4o和GPT-5三个模型的基础人格得分。随后，我们比较了基线条件与人格信息引导条件下的行为表现，并进一步分析了独立操纵各人格维度至极端值所产生的影响。结果表明，宜人性是促进所有模型合作行为的主导因素，而其他人格特质的影响有限。显性人格信息虽能提升合作水平，但也会增加被利用的脆弱性，这在早期代次模型中尤为明显。相比之下，后期代次模型表现出更具选择性的合作行为。这些发现表明，人格引导更倾向于作为一种行为偏差机制，而非确定性的控制机制。