Title: Learning from Synthetic Labs: Language Models as Auction Participants
Primary area: learning theory
Keywords: OSP, Auction, proxy, mechanism design, Language Model

Abstract: This paper investigates the behavior of simulated AI agents (large language mod-
els, or LLMs) in auctions, introducing a novel synthetic data-generating process
to help facilitate the study and design of auctions. We find that LLMs reproduce
well-known findings from experimental literature in auctions across a variety of
classic auction formats. In particular, we find that LLM bidders produce results
consistent with risk-averse human bidders; that they perform closer to theoret-
ical predictions in obviously strategy-proof auctions; and, that in a real-world
eBay-style setting, LLMs strategically produce end-of-auction “sniping” behav-
ior. On prompting, we find that LLMs are robust to naive changes in prompts
(e.g., language, currency) but can improve dramatically towards theoretical pre-
dictions with the right mental model (i.e., the language of Nash deviations). We
run 1,000+ auctions for less than $400 with GPT-4o models (three orders of mag-
nitude cheaper than modern auction experiments) and develop a framework flexi-
ble enough to run auction experiments with any LLM model and a wide range of
auction design specifications, facilitating further experimental study by decreasing
costs and serving as a proof-of-concept for the use of LLM proxies.