Tuesday, February 21 at 4:15pm Frank H.T. Rhodes Hall, 253 Pricing decisions often involve a trade-off between learning about customer behavior to increase long-term revenues, and earning short-term revenues. A common practice is to first estimate the parameters of a demand curve, and then choose the optimal price, assuming the parameter estimates are accurate. In this talk, we will see that this conventional and myopic approach is far from being optimal because it runs the risk of incomplete learning--a negative statistical outcome in which the decision maker stops learning prematurely. To complement that observation, we derive a unified set of conditions under which the myopic approach can be modified to achieve near-optimal performance. These conditions spell out critical rates of price experimentation to guard against incomplete learning, and can guide the use of price experimentation in practice.