LLM Agents Predicting the Valuation of Goods

This study evaluates the capability of Large Language Models (LLMs) to predict individuals' valuation of digital and analog goods, specifically their willingness to accept (WTA) compensation for foregoing services like Facebook. Using survey data from Coyle and Nguyen (2023), which includes demographic information and WTA valuations for various goods, we investigate whether LLMs can simulate individual responses when provided with personal characteristics and prior valuations.

To further assess the applicability of LLMs in predicting valuations, we conducted an extension by surveying participants on Prolific using a new subset of digital and analog goods. We then replicate this survey with LLMs employing the same methodology. The comparison between human and LLM responses provides additional insights into the strengths and limitations of LLMs in capturing individual valuations.