New algorithm optimizes dynamic service fee pricing on third-party platforms

Researchers have developed a new algorithm for dynamic service fee pricing on third-party platforms, addressing the challenge of learning demand under confounding conditions. The algorithm achieves optimal regret and reveals how supply-side noise impacts learnability, leading to a phase transition in regret. The study also demonstrates that non-i.i.d. actions can serve as instrumental variables for demand learning and establishes efficiency guarantees for deep neural networks in this context. Simulations and offline data from Talabat and Lyft illustrate the potential revenue implications of this approach. AI

IMPACT This research could lead to more efficient pricing strategies for online platforms, potentially impacting revenue for businesses and costs for consumers.

RANK_REASON Academic paper on a novel algorithm for demand learning and pricing. [lever_c_demoted from research: ic=1 ai=0.7]

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• Hugging Face
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