How do personas affect LLM agent auction success?
HARBOR: Exploring Persona Dynamics in Multi-Agent Competition
This paper explores how LLMs behave in competitive multi-agent environments, specifically auctions, by giving them distinct "personas" (preferences and backstories). The system, HARBOR, simulates house bidding and investigates how personas influence bidding strategies and outcomes (profit, item acquisition aligned with persona). Key points for LLM-based multi-agent systems include: persona-driven agents deviate from purely profit-driven behavior, agents can profile each other's personas based on bidding behavior, Theory of Mind combined with strategic bidding improves agent performance, and second-order Theory of Mind, while helpful, needs expert strategies to be truly effective. More agents competing makes profiling and persona-based strategy less reliable.