Learning to Price: Interpretable Attribute-Level Models for Dynamic Markets

• URL: http://arxiv.org/abs/2602.00188v1
• Date: Fri, 30 Jan 2026 07:52:07 GMT
• Title: Learning to Price: Interpretable Attribute-Level Models for Dynamic Markets
• Authors: Srividhya Sethuraman, Chandrashekar Lakshminarayanan,
• Abstract summary: We introduce an interpretable emphAdditive Feature Decomposition-based Low-Dimensional Demand (textbfAFDLD) model.<n>We show that ADEPT learns near-optimal prices under dynamic market conditions.<n>Results demonstrate that interpretability and efficiency in autonomous pricing agents can be achieved jointly.
• Score: 1.0195618602298682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamic pricing in high-dimensional markets poses fundamental challenges of scalability, uncertainty, and interpretability. Existing low-rank bandit formulations learn efficiently but rely on latent features that obscure how individual product attributes influence price. We address this by introducing an interpretable \emph{Additive Feature Decomposition-based Low-Dimensional Demand (\textbf{AFDLD}) model}, where product prices are expressed as the sum of attribute-level contributions and substitution effects are explicitly modeled. Building on this structure, we propose \textbf{ADEPT} (Additive DEcomposition for Pricing with cross-elasticity and Time-adaptive learning)-a projection-free, gradient-free online learning algorithm that operates directly in attribute space and achieves a sublinear regret of $\tilde{\mathcal{O}}(\sqrt{d}T^{3/4})$. Through controlled synthetic studies and real-world datasets, we show that ADEPT (i) learns near-optimal prices under dynamic market conditions, (ii) adapts rapidly to shocks and drifts, and (iii) yields transparent, attribute-level price explanations. The results demonstrate that interpretability and efficiency in autonomous pricing agents can be achieved jointly through structured, attribute-driven representations.

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