标题： 博弈论实验中的GPT 显示英文标题

标题： GPT in Game Theory Experiments

摘要： 本文探讨了生成式预训练变压器（GPT）在战略游戏实验中的应用，特别是最后通牒博弈和囚徒困境。 我设计了提示和架构以使GPT能够理解游戏规则，并生成其选择及其决策背后的推理。 主要发现表明，GPT表现出与人类反应相似的行为，例如在最后通牒博弈中提出积极的提议并拒绝不公平的提议，以及在囚徒困境中进行条件合作。 研究探索了用公平关切或自私特质提示GPT如何影响其决策。 值得注意的是，在最后通牒博弈中，“公平”的GPT倾向于提出更高的报价并更频繁地拒绝报价，而与“自私”的GPT相比， 在囚徒困境中，只有当两个GPT玩家都是“公平”的时候，才会保持较高的合作率。 GPT在游戏过程中产生的推理陈述揭示了在游戏中观察到的一些有趣模式背后的逻辑。 总体而言，这项研究展示了GPT作为社会科学领域研究的有价值工具的潜力，特别是在实验研究和社会模拟方面。 显示英文摘要

摘要： This paper explores the use of Generative Pre-trained Transformers (GPT) in strategic game experiments, specifically the ultimatum game and the prisoner's dilemma. I designed prompts and architectures to enable GPT to understand the game rules and to generate both its choices and the reasoning behind decisions. The key findings show that GPT exhibits behaviours similar to human responses, such as making positive offers and rejecting unfair ones in the ultimatum game, along with conditional cooperation in the prisoner's dilemma. The study explores how prompting GPT with traits of fairness concern or selfishness influences its decisions. Notably, the "fair" GPT in the ultimatum game tends to make higher offers and reject offers more frequently compared to the "selfish" GPT. In the prisoner's dilemma, high cooperation rates are maintained only when both GPT players are "fair". The reasoning statements GPT produces during gameplay reveal the underlying logic of certain intriguing patterns observed in the games. Overall, this research shows the potential of GPT as a valuable tool in social science research, especially in experimental studies and social simulations.