 Optimal Bayesian Demand Learning over Short HorizonsJue Wang Production and Operations Management, 2021, vol. 30, issue 4, 1154-1177 Abstract: We investigate the optimal Bayesian dynamic pricing and demand learning policy over short selling horizons, where the pricing decisions are time‐sensitive. The seller fine‐tunes the price near an incumbent price in order to maximize the total revenue. The existing literature focuses on policies that are asymptotically optimal, that is, near optimal when the selling horizons are sufficiently long, but little is known about the optimal Bayesian policies, especially over short horizons. We formulate the problem as a finite‐horizon stochastic dynamic program and identify a connection between the optimality equations and the generalized Weierstrass transform (GWT). We fully characterize the structure of the Bayesian optimal policy for the linear Gaussian demand model and prove that the optimal policy adjusts the myopic price away from the incumbent price. A notable exception occurs when the two prices coincide and the precision of the posterior belief exceeds a threshold, in which case it is optimal to forgo learning and use a fixed‐price policy. Exploiting the structural results makes it possible to compute the optimal policy efficiently on an ordinary computer. Date: 2021 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:popmgt:v:30:y:2021:i:4:p:1154-1177 Ordering information: This journal article can be ordered from Access Statistics for this article Production and Operations Management is currently edited by Kalyan Singhal More articles in Production and Operations Management from Production and Operations Management Society |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.