Chasing Demand: Learning and Earning in a Changing Environment
N. Bora Keskin, The University of Chicago, Booth School of Business
We consider a dynamic pricing problem in which a seller faces an unknown demand model that can change over time. We measure the amount of change over a time horizon of T periods using a quadratic variation metric, and allow a finite “budget" for such changes. 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 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 it would in a demand environment that changes “smoothly." Finally, we extend our analysis to the case of rapidly changing demand settings, and obtain a range of results that quantify the net effect of the volatility in the demand environment on the seller's revenue performance.
SSRN link: http://ssrn.com/abstract=2389750