  Modeling subrational agents, such as humans or economic households, is
inherently challenging due to the difficulty in calibrating reinforcement
learning models or collecting data that involves human subjects. Existing work
highlights the ability of Large Language Models (LLMs) to address complex
reasoning tasks and mimic human communication, while simulation using LLMs as
agents shows emergent social behaviors, potentially improving our comprehension
of human conduct. In this paper, we propose to investigate the use of LLMs to
generate synthetic human demonstrations, which are then used to learn
subrational agent policies though Imitation Learning. We make an assumption
that LLMs can be used as implicit computational models of humans, and propose a
framework to use synthetic demonstrations derived from LLMs to model
subrational behaviors that are characteristic of humans (e.g., myopic behavior
or preference for risk aversion). We experimentally evaluate the ability of our
framework to model sub-rationality through four simple scenarios, including the
well-researched ultimatum game and marshmallow experiment. To gain confidence
in our framework, we are able to replicate well-established findings from prior
human studies associated with the above scenarios. We conclude by discussing
the potential benefits, challenges and limitations of our framework.
