  We evaluate whether LLMs learn to make human-like preference judgements in
strategic scenarios as compared with known empirical results. We show that
Solar and Mistral exhibit stable value-based preference consistent with human
in the prisoner's dilemma, including stake-size effect, and traveler's dilemma,
including penalty-size effect. We establish a relationship between model size,
value based preference, and superficiality. Finally, we find that models that
tend to be less brittle were trained with sliding window attention.
Additionally, we contribute a novel method for constructing preference
relations from arbitrary LLMs and support for a hypothesis regarding human
behavior in the traveler's dilemma.
