  Powered by large language models (LLMs), AI agents have become capable of
many human tasks. Using the most canonical definitions of the Big Five
personality, we measure the ability of LLMs to negotiate within a
game-theoretical framework, as well as methodological challenges to measuring
notions of fairness and risk. Simulations (n=1,500) for both single-issue and
multi-issue negotiation reveal increase in domain complexity with asymmetric
issue valuations improve agreement rates but decrease surplus from aggressive
negotiation. Through gradient-boosted regression and Shapley explainers, we
find high openness, conscientiousness, and neuroticism are associated with fair
tendencies; low agreeableness and low openness are associated with rational
tendencies. Low conscientiousness is associated with high toxicity. These
results indicate that LLMs may have built-in guardrails that default to fair
behavior, but can be "jail broken" to exploit agreeable opponents. We also
offer pragmatic insight in how negotiation bots can be designed, and a
framework of assessing negotiation behavior based on game theory and
computational social science.
