Data-Driven Revision of Decision Models

Abstract

Computer models are representations of problem environment that facilitate analysis with high computing power and representation capabilities. They can be either inferred from the data using data mining techniques or designed manually by experts according to their knowledge and experience. When models represent environments that change over time, they must be properly updated or periodically rebuilt to remain useful. The latter is required when changes in the modelled environment are substantial. When changes are slight, models can be merely adapted by revision. Model revision is a process that gathers knowledge about changes in the modelled environment and updates the model accordingly. When performed manually, this process is demanding, expensive and time consuming. However, it can be automated to some extent if current data about the modelled phenomena is available. Databased revision is a procedure of changing the model so as to better comply with new empirical data, but which at the same time keeps as much of the original contents as possible. In the following we describe the model revision principles in general and then focus on a solution for a specific type of models, the qualitative multi-attribute decision models as used in DEX methodology.