  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.
