May 10, 2024Open Access

Dynamic Contextual Pricing with Doubly Non-Parametric Random Utility Models

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Authors

ECElynn ChenNew York University XCXi ChenNew York University LGLan GaoDeakin University

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Implication

Theoretical analysis demonstrates minimax optimal learning rates for demand estimation in digital commerce, highlighting robust data-driven strategies for dynamic pricing environments.

Key Points

Minimax optimal statistical learning rates are established for consumer demand estimation, utilizing doubly nonparametric random utility models without restrictive assumptions.
A theoretical regret bound quantifies market performance early, capturing the critical interaction between model dimensionality and noise distribution smoothness.
Theoretical analysis establishes new uniform convergence rates for distributional nearest neighbors, adapting offline regression estimators to dynamic online pricing.
Supports data-driven dynamic contextual pricing across digital commerce, providing theoretical foundations that simultaneously recover the mean utility function.

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Chen et al. (2024) studied this question.

synapsesocial.com/papers/68e6ac5ab6db64358762ed0d https://doi.org/10.48550/arxiv.2405.06866

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Implication

Theoretical analysis demonstrates minimax optimal learning rates for demand estimation in digital commerce, highlighting robust data-driven strategies for dynamic pricing environments.

Dynamic Contextual Pricing with Doubly Non-Parametric Random Utility Models

Discussion

Member takes

Implication

Key Points

Cite This Study

Dynamic Contextual Pricing with Doubly Non-Parametric Random Utility Models

Discussion

Member takes

Implication

Key Points

Cite This Study

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