Firms that price perishable resources -- airline seats, hotel rooms, seasonal inventory -- now routinely use demand predictions, but these predictions vary widely in quality. Under hard capacity constraints, acting on an inaccurate prediction can irreversibly deplete inventory needed for future periods. We study how prediction uncertainty propagates into dynamic pricing decisions with linear demand, stochastic noise, and finite capacity. A certified demand forecast with known error bound~$ε^0$ specifies where the system should operate: it shifts regret from $O(\sqrt{T})$ to $O(\log T)$ when $ε^0 \lesssim T^{-1/4}$, and we prove this threshold is tight. A misspecified surrogate model -- biased but correlated with true demand -- cannot set prices directly but reduces learning variance by a factor of $(1-ρ^2)$ through control variates. The two mechanisms compose: the forecast determines the regret regime; the surrogate tightens estimation within it. All algorithms rest on a boundary attraction mechanism that stabilizes pricing near degenerate capacity boundaries without requiring non-degeneracy assumptions. Experiments confirm the phase transition threshold, the variance reduction from surrogates, and robustness across problem instances.

翻译：对易逝品（如机票、酒店客房、季节性库存）进行定价的企业，现已普遍使用需求预测，但这些预测的质量差异很大。在硬容量约束下，基于不准确的预测采取行动可能会不可逆转地耗尽未来时段所需的库存。我们研究了在线性需求、随机噪声和有限容量条件下，预测不确定性如何传导至动态定价决策。一个经过认证且具有已知误差界~$ε^0$的需求预测能够指明系统应处的运行区间：当$ε^0 \lesssim T^{-1/4}$时，它可将遗憾值从$O(\sqrt{T})$降至$O(\log T)$，我们证明这一阈值是紧致的。一个设定错误的替代模型——虽有偏差但与真实需求相关——虽不能直接设定价格，但可通过控制变量法将学习方差降低$(1-ρ^2)$倍。这两种机制是组合使用的：预测决定了遗憾值的量级；替代模型则在该量级内缩小估值区间。所有算法均基于一种边界吸引机制，该机制无需非退化性假设即可稳定容量边界附近的定价行为。实验验证了相变阈值、替代模型对方差的降低效果以及跨问题实例的鲁棒性。