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

The Machine Psychology of Cooperation: Can GPT models operationalise  prompts for altruism, cooperation, competitiveness and selfishness in  economic games?