Authors: Kenan Jiang, Li Xiong, Fei Liu
Abstract: We investigate factors contributing to LLM agents’ success in competitive
multi-agent environments, using auctions as a testbed where agents bid to
maximize profit. The agents are equipped with bidding domain knowledge,
distinct personas that reflect item preferences, and a memory of auction
history. Our work extends the classic auction scenario by creating a realistic
environment where multiple agents bid on houses, weighing aspects such as size,
location, and budget to secure the most desirable homes at the lowest prices.
Particularly, we investigate three key questions: (a) How does a persona
influence an agent’s behavior in a competitive setting? (b) Can an agent
effectively profile its competitors’ behavior during auctions? (c) How can
persona profiling be leveraged to create an advantage using strategies such as
theory of mind? Through a series of experiments, we analyze the behaviors of
LLM agents and shed light on new findings. Our testbed, called HARBOR, offers a
valuable platform for deepening our understanding of multi-agent workflows in
competitive environments.
Source: http://arxiv.org/abs/2502.12149v1
