Abstract

To describe the stochastic environment in descriptive or prescriptive models, it is implicitly assumed that enough data will be available to guarantee the validity of a decision or the consistency of a statistical estimate. Unfortunately, in a real life environment the data available is rarely enough to reach the asymptotic range, either because it is not available or there is not enough time to collect an adequate data base before decisions or estimates must be produced. One serious shortcoming is our ability to systematically blend data and non-data information, in other words, our inability to deal with the fusion of hard and soft information. The lecture deals with these challenges.