LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders
Researchers tested Large Language Models (LLMs) as proxies for human bidders in various auction formats. By giving LLMs chain-of-thought reasoning, they found these agents mimic established human bidder behaviors, including risk aversion and the winner's curse. The study shows that guiding LLMs with game theory concepts like Nash deviations improves their strategic accuracy.