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Dynamic Pricing with Online Learning and Strategic Consumers: An Application of the Aggregating Algorithm
2009
Operations Research
We study the problem faced by a monopolistic company that is dynamically pricing a perishable product or service and simultaneously learning the demand characteristics of its customers. In the learning procedure, the company observes the sales history over consecutive learning stages and predicts consumer demand by applying an aggregating algorithm (AA) to a pool of online stochastic predictors. Numerical implementation uses finite-sample distribution approximations that are periodically
doi:10.1287/opre.1080.0577
fatcat:pqeahj73gve3rhscc2wifcy7ni