Posted Pricing sans Discrimination

In the quest for market mechanisms that are easy to implement, yet close to optimal, few seem as viable as posted pricing. Despite the growing body of impressive results, the performance of most posted price mechanisms however, rely crucially on "price discrimination" when multiple copies of a good are available. For the more general case with non-linear production costs on each good, hardly anything is known for general multi-good markets. With this in mind, we study the problem of social welfare maximization in a Bayesian setting where the seller can produce any number of copies of a good but faces convex production costs for the same. Our central contribution is a structured framework for decision making and static item pricing in the face of uncertainty and production costs, i.e., the seller decides how much to produce and posts a single price per good that is common to all buyers, the buyers arrive sequentially and purchase utility maximizing bundles of goods. The framework yields constant factor approximations to the optimum welfare when buyer valuations are fractionally subadditive, extends to more general valuations and also settings where the seller is completely oblivious to buyer valuations. Our work presents the first known results for non-discriminatory pricing in environments with non-linear costs where we only have access to stochastic information regarding buyer preferences. At a high level, our results imply that it is often possible to obtain good guarantees without discriminating against buyers, i.e., charging them differently for the same good.

Mining Generalized Association Rules in an Evolving Environment

The mining of Generalized Association Rules (GARs) from a large transactional database in the presence of item taxonomy has been recognized as an important model for data mining. Most previous studies on mining generalized association rules, however, were conducted on the assumption of a static environment, i.e., static data source and static item taxonomy, disregarding the fact that the taxonomy might be updated as new transactions are added into the database over time, and as such, the analysts may have to continuously change the support and confidence constraints, or to adjust the taxonomies from different viewpoints to discover more informative rules. In this chapter, we consider the problem of mining generalized association rules in such a dynamic environment. We survey different strategies incorporating state-of-the-art techniques for dealing with this problem and investigate how to efficiently update the discovered association rules when there are transaction updates to the database along with item taxonomy evolution and refinement of support constraint.