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arXiv · 2502.20012

Learning Classifiers That Induce Markets

Yonatan Sommer·Ivri Hikri·Lotan Amit·Nir Rosenfeld
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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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BibTeXRIS

Yonatan Sommer, Ivri Hikri, Lotan Amit, Nir Rosenfeld. 2025-02-27. Learning Classifiers That Induce Markets. https://arxiv.org/abs/2502.20012

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