Dynamic Optimization and Learning: How Should a Manager set Prices when the Demand Function is Unknown ?

This paper considers the problem of changing prices over time to maximize expectedrevenues in the presence of unknown demand distribution parameters. It providesand compares several methods that use the sequence of past prices and observeddemands to set price in the current period. A Taylor series expansion of the futurereward function explicitly illustrates the tradeoff between short term revenuemaximization and future information gain and suggests a promising pricing policyreferred to as a one-step look-ahead rule. An in-depth Monte Carlo study comparesseveral different pricing strategies and shows that the one-step look-ahead rulesdominate other heuristic policies and produce good short term performance. Thereasons for the observed bias of parameter estimates are also investigated.