  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.
