Prices as model weights: learning from dispersed data

Oct 24, 2025 matching market market design

Market prices are similar to model weights in the sense that they are "fit" through price discovery mechanisms to optimize for equilibrium. The difference between supply and demand is analogous to the gradient in backpropagation.

An important property of prices is that they can be "fit" to distributed and sometimes hidden data. As Friedrich Hayek argued, prices convey dispersed knowledge that no single person or central planner could ever fully possess. There are problems that deal with information which cannot be accessed directly. A well-designed price discovery mechanism can solve such problems more efficiently than an AI/ML model with limited information.

This analogy came to my mind while I was considering the labor market congestion that occurs when too many candidates apply for a role. Most of those candidates are not a good fit for the job, but they still try their luck since it requires little effort from them. As a solution, employers automate screening with AI. Candidates also use AI to scrape the job description and apply automatically (like "tinda finger"). This creates a vicious cycle in which nobody wins.

A solution can be a market design that involves paying for an application by candidates, with dynamic pricing. Paying to apply may sound provocative and require thoughtful consideration and careful testing. Payments can be made with platform-issued virtual points, available in a limited supply. But here, I focus on why price signals may address this problem better than AI-based screening.

The labor market is a matching market with information asymmetry, where both sides have unique preferences resolved only through costly interaction. Any AI/ML algorithm is only as good as the data, and the data is limited and often even untrue. Requiring payment to apply would elicit a candidate’s own estimate of their fit for the job and their true level of interest. These estimates would be reflected in prices, providing a visible signal that guides other candidates considering the role. This approach could reduce congestion, lower screening costs, and help form better matches.