Dartmouth Events

Modern customers' decisions are intricately shaped not only by their own preferences but also by the associated product information and the offered price. We consider a novel dynamic pricing and learning setting where in addition to setting prices of products in sequential rounds, the seller also ex-ante commits to `advertising schemes’. Using the popular Bayesian persuasion framework to model the effect of these signals on the buyers’ valuation and purchase responses, we formulate the problem of finding an optimal design of the advertising scheme along with a pricing scheme that maximizes the seller’s expected revenue. Without knowing the buyers’ demand function, our goal is to design an online algorithm that can adaptively learn the optimal pricing and advertising strategy. Our main result is a computationally efficient online algorithm that achieves a near optimal regret bound.