Learning and Earning

Price experimentation is an important tool for firms to find the optimal selling price of their products. It should be conducted properly, since experimenting with selling prices can be costly. A firm therefore needs to find a pricing policy that optimally balances between learning the optimal price and gaining revenue. We investigate the so-called 'certainty equivalent pricing' policy, where estimating consumer behaviour and optimising profit are completely decoupled, and discuss situations where this rule may or may not lead to the profit rate that is achievable. It turns out that it is sometimes necessary to develop algorithms that ensure that the right amount of price experimentation is undertaken so as to learn and exploit consumer behaviour as efficiently as possible.