Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQ

Abstract

Models allow us to simplify reality and give advantages to both decomposition and abstraction. Models can have various forms from textual, tabular, mathematical, and graphical to a combination of these formats. Formal models can be processed, or even executed, by machines. An engineering model must satisfy such characteristics as abstraction, understandability, accuracy, predictiveness, and inexpensiveness. Models explicitly represent knowledge of the modeled domain in a form suitable for reasoning about them and learning. Knowledge may be descriptive, structural, procedural, meta-, or heuristic. Focus on one type of knowledge during the analysis may ignore the other one. Moreover, analysis and reasoning also rely on data representation forms which may lose accuracy due to simplification and different assumptions. Therefore, completeness, correctness, and adequacy of knowledge as well as particularities of the representing structure may affect the results of knowledge processing and decision making. Therefore, the capability of models (and other structures) to represent knowledge completely, adequately, and accurately is still a matter of various research activities. This issue of CSIMQ is devoted to this matter.

Keywords

• knowledge graph
• design science research
• agent-based model
• metamodel
• land use modeling
• enterprise modeling
• fractal enterprise modeling
• innovation
• capability
• low-code development