Adaptive Inference for Resource-Constrained Dynamic Pricing
We study dynamic pricing over a finite selling horizon when limited resource capacity determines revenue and the observations available for inference at a prespecified price. Resource depletion can remove the target neighborhood from the feasible price set, changing the experiment generated by the pricing policy. We develop inference-aware re-solving controllers that check target-band feasibility before current covariates arrive and log the pricing mixture. Target-reserved and smooth controllers take population mean-pair geometry as a predeployment input; learned barycentric re-solving instead estimates stationary mean-consumption vectors of predeclared component kernels. On an affine bin...
| Authors | Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi |
|---|---|
| Primary category | stat.ML |
| Published | 2 June 2026 |
| Last updated at source | 27 August 2026 |
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