In contextual dynamic pricing, a seller sequentially prices goods based on contextual information.

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'. That is, in the beginning of each round the seller can decide what kind of signal they will provide to the buyer about the product's quality upon realization. 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 any apriori knowledge of the buyers' demand function, our goal is to design an online algorithm that can use past purchase responses to adaptively learn the optimal pricing and advertising strategy. We study the regret of the algorithm when compared to the optimal clairvoyant price and advertising scheme.

Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation.

A very useful method for devising online posted prices is viavanishing-regret online learning algorithms inanadversarial environment [12,11,23,9,10,28].

Fair allocation is a central problem in economics since decades. It arises naturally in real-world applications such as advertising, negotiation, rent sharing, inheritance, etc [14, 15, 16, 21, 29, 35]. In discrete fair division, the basic scenario is that we want to distribute a set M of m indivisible items among n agents, such that the allocation is deemed fair by the agents.