Complex Structured Decision Making Model: A hierarchical frame work for complex structured data

Abstract

We introduce a hierarchical framework we call Complex Structured Decision Making model for complexly structured knowledge representation in intelligent decision making. We show that our model extends non-hierarchical (flat) decision making models to hierarchical decision making models that are similar to comprehensible human decision making processes. Further, we make an argument that hierarchial representation of knowledge in a Complex Structured Decision Making Model simplifies the approximation of aggregation functions to easily adapt to the underline relation of the system. Additionally, using a real world complex structured data set, we show that hierarchically organized Fuzzy Integrals, e.g. Choquet Integral, and Sugeno Integral and Fuzzy Signatures outperform these non-hierarchical Fuzzy Integrals.