Learning and pricing in an internet environment with binomial demands

Abstract

This paper considers the problem of setting prices dynamically to maximise expected revenues in a finite horizon model in which the demand parameters are unknown. At each decision epoch, the manager chooses a price and observes a binary response (buy or not) for each consumer visiting the website during that period. This paper focuses on comparing several easy to implement good pricing policies. A Taylor series expansion of the future reward function explicity illustrates the trade-off between short-term revenue maximisation and future information gains and suggests a pricing policy referred to as a one-step look ahead rule. A Monte Carlo study compares several different pricing strategies and shows that the one-step look ahead rule dominates other policies and produces good short term performance.