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arXiv AI By Zhuolin Wu, Chengrui Zhu, Wenhua Nie, Kenny Ye Liang, Junming Lin, Haiyang Li, Zhilin Li, Wenjia Geng, Zeyu Wu, Yinan Wu, Jinghua Hao, Renqing He

A General Framework for Budgeted Threshold Incentives on Request

Read the original on arXiv AI →

The paper introduces a request-driven framework for designing budgeted threshold incentives on on-demand delivery platforms. It decomposes the process into four stages—conditional prediction, population reduction, trajectory integration, and budget allocation—using seven interchangeable modules that share conditional trajectory laws. The framework includes a response-correction step that reweights abundant no-offer data to match short pilot moments, and the authors prove that the end-to-end value loss is bounded by the sum of stage errors, with empirical results showing significant speedups and reduced regret compared to traditional trials.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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