4 papers
Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation
Kehang Zhu, Nithum Thain, Vivian Tsai +2
As AI usage becomes more prevalent in social contexts, understanding agent-user interaction is critical to designing systems that imp rove both individual and group outcomes. We pr…
Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining
Crystal Qian, Kehang Zhu, John Horton +4
Markets increasingly accommodate large language models (LLMs) as autonomous decision-making agents. As this transition occurs, it becomes critical to evaluate how these agents beha…
Learning from Synthetic Labs: Language Models as Auction Participants
Anand Shah, Kehang Zhu, Yanchen Jiang +4
This paper investigates the behavior of simulated AI agents (large language models, or LLMs) in auctions, introducing a novel synthetic data-generating process to help facilitate t…
LLM-Powered Preference Elicitation in Combinatorial Assignment
Ermis Soumalias, Yanchen Jiang, Kehang Zhu +3
We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in combinatorial assignment. While traditional PE methods rely…