LLM Agents Show Strong, Stereotyped, and Miscalibrated Personality-Based Partner Selection
A preprint reports that large language model (LLM) agents, when assigned Big Five personality archetypes, select partners for collaboration based on task-related stereotypes, even when all candidates have equal capabilities. Agents consistently favored 'open' personalities for creative tasks and 'conscientious' ones for strategic or problem-solving tasks, while rarely choosing extraverted, agreeable, or balanced archetypes—despite human research showing team agreeableness predicts performance. Unlike humans, LLM agents also avoided self-similar partners, preferring those with more distinct personality traits.
Why it matters: This suggests that LLM agent partner selection can introduce systematic, human-mismatched biases, raising concerns for fairness and effectiveness in multi-agent AI systems and marketplaces.
Full story at: arXiv Multiagent Systems ↗