No abstract

In this paper we study multi issue alternating-offers bargaining in a perfect information finite horizon setting, we determine the pertinent subgame perfect equilibrium, and we provide an algorithm to compute it. The equilibrium is determined by making a novel use of backward induction together with convex programming techniques in multi issue settings. We show that the agents reach an agreement immediately and that such an agreement is Pareto efficient. Furthermore, we prove that, when the multi issue utility functions are linear, the problem of computing the equilibrium is tractable and the related complexity is polynomial with the number of issues and linear with the deadline of bargaining.

In this article, we introduce pesudoconstraints, a novel data mining pattern aimed at identifying rare events in databases. At first, we formally define pesudoconstraints using a probabilistic model and provide a statistical test to identify pesudoconstraints in a database. Then, we focus on a specific class of pesudoconstraints, named cycle pesudoconstraints, which often occur in databases. We define cycle pesudoconstraints in the context of the ER model and present an automatic method for detecting cycle pesudoconstraints from a relational database. Finally, we present an experiment to show cycle pesudoconstraints “at work” on real data