Abstract
We consider a general class of network revenue management problems, where mean demand at each point in time is determined by a vector of prices, and the objective is to dynamically adjust these prices so as to maximize expected revenues over a finite sales horizon. A salient feature of our problem is that the decision maker can only observe realized demand over time, but does not know the underlying demand function which maps prices into instantaneous demand rate. We introduce a family of "blind" pricing policies which are designed to balance tradeoffs between exploration (demand learning) and exploitation (pricing to optimize revenues). We derive bounds on the revenue loss incurred by said policies in comparison to the optimal dynamic pricing policy that knows the demand function a priori and prove that asymptotically, as the volume of sales increases, this gap shrinks to zero.