Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty
Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/13/10/469 |
| _version_ | 1797472629269987328 |
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| author | Andreas Tolk |
| author_facet | Andreas Tolk |
| author_sort | Andreas Tolk |
| collection | DOAJ |
| description | Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about the system and the outcomes of interest. Complex adaptive systems under deep uncertainty must integrate the search for robust solutions by conducting exploratory modeling and analysis. This article visits both domains, shows what the new challenges are, and provides a framework to apply methods from operational research and complexity science to address them. With such extensions, simulation-based approaches will be able to support these new areas as well, although optimal solutions may no longer be obtainable. Instead, robust and sufficient solutions will become the objective of optimization processes. |
| first_indexed | 2024-03-09T20:04:05Z |
| format | Article |
| id | doaj.art-b32f9a94b6f845bd82a804aba8366a7f |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T20:04:05Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-b32f9a94b6f845bd82a804aba8366a7f2023-11-24T00:36:01ZengMDPI AGInformation2078-24892022-09-01131046910.3390/info13100469Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep UncertaintyAndreas Tolk0The MITRE Corporation, Charlottesville, VA 22911, USAWithin the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about the system and the outcomes of interest. Complex adaptive systems under deep uncertainty must integrate the search for robust solutions by conducting exploratory modeling and analysis. This article visits both domains, shows what the new challenges are, and provides a framework to apply methods from operational research and complexity science to address them. With such extensions, simulation-based approaches will be able to support these new areas as well, although optimal solutions may no longer be obtainable. Instead, robust and sufficient solutions will become the objective of optimization processes.https://www.mdpi.com/2078-2489/13/10/469optimizationheuristicsuncertaintycomplex adaptive systemsdeep uncertainty |
| spellingShingle | Andreas Tolk Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty Information optimization heuristics uncertainty complex adaptive systems deep uncertainty |
| title | Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty |
| title_full | Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty |
| title_fullStr | Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty |
| title_full_unstemmed | Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty |
| title_short | Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty |
| title_sort | simulation based optimization implications of complex adaptive systems and deep uncertainty |
| topic | optimization heuristics uncertainty complex adaptive systems deep uncertainty |
| url | https://www.mdpi.com/2078-2489/13/10/469 |
| work_keys_str_mv | AT andreastolk simulationbasedoptimizationimplicationsofcomplexadaptivesystemsanddeepuncertainty |