Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQ
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...
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| Format: | Article |
| Language: | English |
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Riga Technical University Press
2023-10-01
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| Series: | Complex Systems Informatics and Modeling Quarterly |
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| Online Access: | https://csimq-journals.rtu.lv/article/view/7960 |
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| author | Erika Nazaruka Tarmo Robal |
| author_facet | Erika Nazaruka Tarmo Robal |
| author_sort | Erika Nazaruka |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6407f3d5a304418a883aec0ed36d9349 |
| institution | Kabale University |
| issn | 2255-9922 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Riga Technical University Press |
| record_format | Article |
| series | Complex Systems Informatics and Modeling Quarterly |
| spelling | doaj-art-6407f3d5a304418a883aec0ed36d93492025-02-03T12:03:20ZengRiga Technical University PressComplex Systems Informatics and Modeling Quarterly2255-99222023-10-0103610.7250/csimq.2023-36.003385Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQErika Nazaruka0Tarmo Robal1Institute of Applied Computer Systems, Riga Technical University, 6A Kipsalas Street, Riga, LV-1048Tallinn University of Technology, 3, Ehitajate tee 5, 19086, TallinnModels 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.https://csimq-journals.rtu.lv/article/view/7960knowledge graphdesign science researchagent-based modelmetamodelland use modelingenterprise modelingfractal enterprise modelinginnovationcapabilitylow-code development |
| spellingShingle | Erika Nazaruka Tarmo Robal Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQ Complex Systems Informatics and Modeling Quarterly knowledge graph design science research agent-based model metamodel land use modeling enterprise modeling fractal enterprise modeling innovation capability low-code development |
| title | Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQ |
| title_full | Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQ |
| title_fullStr | Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQ |
| title_full_unstemmed | Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQ |
| title_short | Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQ |
| title_sort | overcoming system complexity using models and knowledge structures editorial introduction to issue 36 of csimq |
| topic | knowledge graph design science research agent-based model metamodel land use modeling enterprise modeling fractal enterprise modeling innovation capability low-code development |
| url | https://csimq-journals.rtu.lv/article/view/7960 |
| work_keys_str_mv | AT erikanazaruka overcomingsystemcomplexityusingmodelsandknowledgestructureseditorialintroductiontoissue36ofcsimq AT tarmorobal overcomingsystemcomplexityusingmodelsandknowledgestructureseditorialintroductiontoissue36ofcsimq |