Learning Classifiers That Induce Markets

• URL: http://arxiv.org/abs/2502.20012v2
• Date: Sun, 06 Jul 2025 09:20:32 GMT
• Title: Learning Classifiers That Induce Markets
• Authors: Yonatan Sommer, Ivri Hikri, Lotan Amit, Nir Rosenfeld,
• Abstract summary: We present an analysis of the learning task, devise an algorithm for computing market prices, and conduct experiments to explore our novel setting and approach.<n>Our idea is simple: when users seek positive predictions, this creates demand for important features; and if features are available for purchase, then a market will form, and competition will give rise to prices.
• Score: 10.73085261703945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When learning is used to inform decisions about humans, such as for loans, hiring, or admissions, this can incentivize users to strategically modify their features, at a cost, to obtain positive predictions. The common assumption is that the function governing costs is exogenous, fixed, and predetermined. We challenge this assumption, and assert that costs can emerge as a result of deploying a classifier. Our idea is simple: when users seek positive predictions, this creates demand for important features; and if features are available for purchase, then a market will form, and competition will give rise to prices. We extend the strategic classification framework to support this notion, and study learning in a setting where a classifier can induce a market for features. We present an analysis of the learning task, devise an algorithm for computing market prices, propose a differentiable learning framework, and conduct experiments to explore our novel setting and approach.

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