We investigated the capability of the GPT-3.5 large language model (LLM) to operationalize natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior in two social dilemmas: the repeated Prisoners Dilemma and the one-shot Dictator Game. Using a within-subject experimental design, we used a prompt to describe the task environment using a similar protocol to that used in experimental psychology studies with human subjects. We tested our research question by manipulating the part of our prompt which was used to create a simulated persona with different cooperative and competitive stances. We then assessed the resulting simulacras' level of cooperation in each social dilemma, taking into account the effect of different partner conditions for the repeated game. Our results provide evidence that LLMs can, to some extent, translate natural language descriptions of different cooperative stances into corresponding descriptions of appropriate task behaviour, particularly in the one-shot game. There is some evidence of behaviour resembling conditional reciprocity for the cooperative simulacra in the repeated game, and for the later version of the model there is evidence of altruistic behaviour. Our study has potential implications for using LLM chatbots in task environments that involve cooperation, e.g. using chatbots as mediators and facilitators in public-goods negotiations.

翻译：本研究探讨了GPT-3.5大语言模型在两种社会困境（重复囚徒困境与单次独裁者博弈）中操作化自然语言描述的协作、竞争、利他与自利行为的能力。采用被试内实验设计，我们使用与人类被试实验心理学研究相似的协议，通过提示描述任务环境。通过操纵提示中用于创建具有不同合作与竞争立场模拟人格的部分，我们检验了研究问题，并评估了各社会困境中相应模拟体的合作水平，同时考虑了重复博弈中不同对手条件的影响。研究结果表明，大语言模型能在一定程度上将不同合作立场的自然语言描述转化为相应任务行为的恰当描述，在单次博弈中尤为明显。在重复博弈中，合作型模拟体表现出类似条件性互惠的行为迹象；在模型后期版本中还观测到利他行为迹象。本研究对在涉及协作的任务环境中使用大语言模型聊天机器人具有潜在启示，例如在公共物品谈判中将其作为调解人与协调者。