How do LLMs impact auction ad bidding strategies?
InfoBid: A Simulation Framework for Studying Information Disclosure in Auctions with Large Language Model-based Agents
This paper introduces InfoBid, a simulation framework using Large Language Models (LLMs) as agents to study how revealing different amounts of information affects online advertising auctions. It explores how LLM-based agents make decisions when they don't know everyone's value for the advertised item. Key findings relevant to LLM-based multi-agent systems include: LLM agents generally bid rationally when given clear information but deviate when given partial information about their standing relative to other bidders; agents appear not to explicitly consider their competitors' strategies, aligning with theoretical predictions for this type of auction; providing more information to high-value bidders generally leads to both higher revenue and better overall outcomes. This suggests that carefully controlling information flow in such systems is crucial for good results.