# Adaptive Inference for Resource-Constrained Dynamic Pricing

arXiv: https://arxiv.org/abs/2606.03736 · HTML: https://mlsanity.com/paper/2606.03736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Builds on (most cited first, 1 of 1)

- [Regret Minimization and Statistical Inference in Online Decision Making with High-dimensional Covariates](https://mlsanity.com/paper/2411.06329/) (2024)

## Abstract (arXiv)

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 binding-capacity family, an exact-input target-reserved controller assigning mass t-γ obtains an information clock of order T1-γ in probability, radius OpT-(1-γ)/2, and, under an exposed-face reward identity, a signed fluid-benchmark gap bounded above by O(log T+T1-γ). Under the exogenous affine-face condition, predeclared target support, and polynomial error spending with exponent greater than one, learned barycentric re-solving has a linear information clock in probability and an O(log T) signed-gap upper bound; centered local pricing has the same orders under slack capacity and global target optimality. An exact-input, target-compatible smooth alternative without reservation gives a linear clock in probability, an Op(T-1/2) deterministic-envelope radius with unconditional coverage and reporting probability tending to one, and an O(log2 T) signed-gap upper bound. Boundary results show when physical support is lost and why a 1/t target branch yields only Op(1) information if it is the sole target-local source. The policy reports an interval when its prespecified support and information conditions hold and otherwise abstains.