Apples and Oranges? Comparing Human and AI Bids in Experimental Auctions for Food Products

My research looks into how well AI systems can perform in auctions

Can ChatGPT and other AI models replicate human bidding behavior in experimental auctions? And if so, what does that mean for future economic research?

This was the question that we begin with in the article I co-wrote for The Council on Food, Agricultural & Resource Economics (C-FARE) with Carola Grebitus and Jay Corrigan.

We write:

Artificial intelligence (AI) is an increasingly popular tool for economics research. This raises questions about whether it can complement—or perhaps even replace—human participants in economics experiments. We explored this possibility by asking large language models (LLMs) like ChatGPT, Claude, and Gemini to stand in for people in experimental auctions used to measure consumer willingness to pay.

Experimental auctions are a well-established technique for estimating the value people derive from different food attributes—like organic labels, fair trade certification, or local sourcing. But experimental auction studies are costly and time-consuming, often requiring hundreds of participants and costing tens--or sometimes even hundreds--of thousands of dollars. If AI could accurately mimic human bidding behavior, it could make this type of research faster and cheaper.

We asked three LLMs—ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash—to bid in two experiments we had previously conducted with human participants.

For more - check out the article!