Chasing Demand: Learning and Earning in a Changing Environment
研究卖家在未知且随时间变化的需求模型下的动态定价问题，推导收益表现的理论下界并设计近优定价策略，发现需求“突发”变化比“平滑”变化更有利于卖家收益。
We consider a dynamic pricing problem in which a seller faces an unknown demand model that can change over time. The amount of change over a time horizon of T periods is measured using a variation metric that allows for a broad spectrum of temporal behavior. Given a finite variation “budget,” we first derive a lower bound on the expected performance gap between any pricing policy and a clairvoyant who knows a priori the temporal evolution of the underlying demand model, and then we design families of near-optimal pricing policies, the revenue performance of which asymptotically matches said lower bound. We also show that the seller can achieve a substantially better revenue performance in demand environments that change in “bursts” than in demand environments that change “smoothly,” among other things quantifying the net effect of the “volatility” in the demand environment on the seller’s revenue performance.