A. Banks, Casey E. Eaton, Meredith Bates, Lisa Matsuyama, Giulia Palma, Amy Guerin, Kristin Weger, Bryan L. Mesmer, Dan Friedrich
The objective of this research is to develop a methodology for an arts‐based intervention that can effectively promote employee voice. An arts‐based intervention was developed to promote employee voice, supported by literature and expert knowledge. This intervention was applied at the 2019 NASA Cost and Schedule Symposium. Participants indicated that the intervention was effective in eliciting “hidden truths.” Several topics were revealed that participants felt uncomfortable discussing in the workplace. The intervention methodology is evaluated, considering limitations and future changes.
{"title":"Insights from developing improvisational theatre intervention at NASA","authors":"A. Banks, Casey E. Eaton, Meredith Bates, Lisa Matsuyama, Giulia Palma, Amy Guerin, Kristin Weger, Bryan L. Mesmer, Dan Friedrich","doi":"10.1002/sys.21665","DOIUrl":"https://doi.org/10.1002/sys.21665","url":null,"abstract":"The objective of this research is to develop a methodology for an arts‐based intervention that can effectively promote employee voice. An arts‐based intervention was developed to promote employee voice, supported by literature and expert knowledge. This intervention was applied at the 2019 NASA Cost and Schedule Symposium. Participants indicated that the intervention was effective in eliciting “hidden truths.” Several topics were revealed that participants felt uncomfortable discussing in the workplace. The intervention methodology is evaluated, considering limitations and future changes.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46050987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Collier, Brett Briglia, Thomas Finkelston, Mark C. Manasco, David L. Slutzky, J. Lambert
The security risks posed by electronics are numerous. There are typically a variety of risk‐reducing countermeasures for a given system or across an enterprise. Each countermeasure is associated with both a level of risk reduction and its lifecycle costs. Given budgetary constraints, risk managers and systems engineers must determine what combinations of countermeasures cost‐effectively maximize risk reduction, and what metrics best guide the investment process. In this paper, we seek to answer these questions through exploration of risk reduction metrics from the field of security economics, including the benefit/cost ratio, return on security investment (ROSI), expected benefit of information security (EBIS), and expected net benefit of information security (ENBIS). The results suggest that ratio‐based metrics are not strongly correlated with risk reduction, while EBIS is equivalent to risk reduction and ENBIS is equal to risk reduction minus cost.
{"title":"On metrics and prioritization of investments in hardware security","authors":"Z. Collier, Brett Briglia, Thomas Finkelston, Mark C. Manasco, David L. Slutzky, J. Lambert","doi":"10.1002/sys.21667","DOIUrl":"https://doi.org/10.1002/sys.21667","url":null,"abstract":"The security risks posed by electronics are numerous. There are typically a variety of risk‐reducing countermeasures for a given system or across an enterprise. Each countermeasure is associated with both a level of risk reduction and its lifecycle costs. Given budgetary constraints, risk managers and systems engineers must determine what combinations of countermeasures cost‐effectively maximize risk reduction, and what metrics best guide the investment process. In this paper, we seek to answer these questions through exploration of risk reduction metrics from the field of security economics, including the benefit/cost ratio, return on security investment (ROSI), expected benefit of information security (EBIS), and expected net benefit of information security (ENBIS). The results suggest that ratio‐based metrics are not strongly correlated with risk reduction, while EBIS is equivalent to risk reduction and ENBIS is equal to risk reduction minus cost.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44254034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software reliability is one of the standard critical inherent characteristics of software systems. The testing coverage function (TCF) is a significant parameter for identifying the completeness and effectiveness of software testing. It is defined as the proportion of the code that has been tested up to time t. To capture the dynamic behavior of the number of faults detected over a period of time, several distributions, namely S‐shaped, inflection S‐shaped, logistic, log‐logistic, Weibull, Rayleigh, Erlang, and logarithmic exponentiated, have been used as TCF in literature. However, these distributions are not sufficient to describe TCF's practical behavior due to complexity and vagueness in the collected data. This study proposes two software reliability growth models (SRGMs), which incorporate the generalized inflection S‐shaped (GISS) distribution as TCF. The models have been developed in perfect and imperfect debugging environments while considering fault removal efficiency, error generation, and uncertainty in the operating environment. To analyze the effectiveness, the proposed models are then tested with six failure data sets. The choice of GISS distribution as a TCF improves the software reliability estimation in comparison with the existing models in the literature. Finally, single and multiple parameters sensitivity analysis also has been done and based on it, the critical parameters have been detected. The proposed models may be helpful for the system analyst to predict various parameters about some software systems.
{"title":"Testing coverage‐based software reliability growth model considering uncertainty of operating environment","authors":"Vishal Pradhan, J. Dhar, Ajay Mahaputra Kumar","doi":"10.1002/sys.21671","DOIUrl":"https://doi.org/10.1002/sys.21671","url":null,"abstract":"Software reliability is one of the standard critical inherent characteristics of software systems. The testing coverage function (TCF) is a significant parameter for identifying the completeness and effectiveness of software testing. It is defined as the proportion of the code that has been tested up to time t. To capture the dynamic behavior of the number of faults detected over a period of time, several distributions, namely S‐shaped, inflection S‐shaped, logistic, log‐logistic, Weibull, Rayleigh, Erlang, and logarithmic exponentiated, have been used as TCF in literature. However, these distributions are not sufficient to describe TCF's practical behavior due to complexity and vagueness in the collected data. This study proposes two software reliability growth models (SRGMs), which incorporate the generalized inflection S‐shaped (GISS) distribution as TCF. The models have been developed in perfect and imperfect debugging environments while considering fault removal efficiency, error generation, and uncertainty in the operating environment. To analyze the effectiveness, the proposed models are then tested with six failure data sets. The choice of GISS distribution as a TCF improves the software reliability estimation in comparison with the existing models in the literature. Finally, single and multiple parameters sensitivity analysis also has been done and based on it, the critical parameters have been detected. The proposed models may be helpful for the system analyst to predict various parameters about some software systems.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42595391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Systems Modeling Language (SysML) has been applied in the past years to a variety of software and systems engineering projects, by hundreds of researchers, engineers, and other systems and software professionals. Thus, it is expected that all this experience has been described in research articles. Therefore, we propose a survey describing what practitioners and researchers think about this modeling language, the actual use of SysML, and how SysML is used in the software and system engineering life cycle. This article describes a survey on SysML, answering questions such as the most used diagrams for each phase of the development of a system, the most common domains, and other data about the participants. The survey was answered by 343 participants from 38 countries, mostly systems engineers, software and systems architects, and researchers. Industry and academia can use our results (i) for assisting researchers and engineers to select appropriate diagrams for each software and systems development phase, (ii) for a better understanding of which industry domains SysML is most commonly applied, (iii) as a reference for identifying which types of systems are modeled with SysML, (iv) for knowing which software tools are most used, and (v) which other modeling languages are most commonly integrated with SysML for software and systems development.
{"title":"A survey on what users think about SysML","authors":"Tauany L. S. Santos, M. S. Soares","doi":"10.1002/sys.21663","DOIUrl":"https://doi.org/10.1002/sys.21663","url":null,"abstract":"Systems Modeling Language (SysML) has been applied in the past years to a variety of software and systems engineering projects, by hundreds of researchers, engineers, and other systems and software professionals. Thus, it is expected that all this experience has been described in research articles. Therefore, we propose a survey describing what practitioners and researchers think about this modeling language, the actual use of SysML, and how SysML is used in the software and system engineering life cycle. This article describes a survey on SysML, answering questions such as the most used diagrams for each phase of the development of a system, the most common domains, and other data about the participants. The survey was answered by 343 participants from 38 countries, mostly systems engineers, software and systems architects, and researchers. Industry and academia can use our results (i) for assisting researchers and engineers to select appropriate diagrams for each software and systems development phase, (ii) for a better understanding of which industry domains SysML is most commonly applied, (iii) as a reference for identifying which types of systems are modeled with SysML, (iv) for knowing which software tools are most used, and (v) which other modeling languages are most commonly integrated with SysML for software and systems development.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47852674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lujin Zhao, Z. Szajnfarber, David A. Broniatowski, J. Helveston
Urban transportation systems involve thousands of individuals making choices between routes with multiple modes and transfers. For transportation system simulations to produce realistic results, modelers need to incorporate these users and their choices. Choice‐based conjoint surveys provide an attractive solution for obtaining flexible utility models that can be used to predict choices for a wide variety of trips. In this study, we demonstrate an example using conjoint survey data of commuter mode choice in the Washington, D.C. metro area (N = 1651). We sample commuters who primarily drive and those that take transit. We examine preferences for different types of multimodal trips, including those with intramodal and intermodel transfers. We find that trips involving a bus transfer are the least preferred while both drivers and transit users both value metro similarly to driving. We also find that walking during transit trips is an important barrier, with the travel time penalty for walking being 60% higher than that of time in a vehicle. Our findings highlight the significance of accounting for differences in modal transfer types in transportation system simulations. Reducing arrival time uncertainty was not a significant factor in commuter mode choice, and commuters' value of time was similar across all vehicle types, suggesting that increasing the relative speed of transit modes may only have a marginal effect on commuter substitution away from personal vehicles.
{"title":"Using conjoint analysis to incorporate heterogeneous preferences into multimodal transit trip simulations","authors":"Lujin Zhao, Z. Szajnfarber, David A. Broniatowski, J. Helveston","doi":"10.1002/sys.21670","DOIUrl":"https://doi.org/10.1002/sys.21670","url":null,"abstract":"Urban transportation systems involve thousands of individuals making choices between routes with multiple modes and transfers. For transportation system simulations to produce realistic results, modelers need to incorporate these users and their choices. Choice‐based conjoint surveys provide an attractive solution for obtaining flexible utility models that can be used to predict choices for a wide variety of trips. In this study, we demonstrate an example using conjoint survey data of commuter mode choice in the Washington, D.C. metro area (N = 1651). We sample commuters who primarily drive and those that take transit. We examine preferences for different types of multimodal trips, including those with intramodal and intermodel transfers. We find that trips involving a bus transfer are the least preferred while both drivers and transit users both value metro similarly to driving. We also find that walking during transit trips is an important barrier, with the travel time penalty for walking being 60% higher than that of time in a vehicle. Our findings highlight the significance of accounting for differences in modal transfer types in transportation system simulations. Reducing arrival time uncertainty was not a significant factor in commuter mode choice, and commuters' value of time was similar across all vehicle types, suggesting that increasing the relative speed of transit modes may only have a marginal effect on commuter substitution away from personal vehicles.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43396630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Sanaei, Kevin Otto, Katja Hölttä-Otto, Kristin Wood
Modularization is an approach for system architecting and design simplification by encapsulating complex interactions among components within modules and reducing dependencies across modules. Design structure matrix (DSM) based clustering algorithms have proven helpful for such analysis, owing to their convenience in manipulating a large number of elements using conventional software. However, there are problems where constraints must be maintained in the modularization, for example, coping with functions or systems that either cannot or must be performed in regions with excessive heat, pressure, magnetic or other fields. Excluding such field boundary considerations can result in DSM computed modular architectural solutions that bundle field‐incompatible functions or components that are not practical. Such regional field constraint considerations are not taken into account using conventional DSM clustering algorithms. We introduce a DSM‐based clustering algorithm that incorporates these practical embodiment constraints through a constraint matrix indicating which elements can or cannot be placed in the same field region. We then employ reinforcement learning to allow the clustering algorithm to exploit its learnings from the previous attempts and during the clustering to facilitate the optimization under constraints. We demonstrate two examples of a medical contrast injector and the controller board of a three‐phase pump motor.
{"title":"A reinforcement learning approach to system modularization under constraints","authors":"R. Sanaei, Kevin Otto, Katja Hölttä-Otto, Kristin Wood","doi":"10.1002/sys.21666","DOIUrl":"https://doi.org/10.1002/sys.21666","url":null,"abstract":"Modularization is an approach for system architecting and design simplification by encapsulating complex interactions among components within modules and reducing dependencies across modules. Design structure matrix (DSM) based clustering algorithms have proven helpful for such analysis, owing to their convenience in manipulating a large number of elements using conventional software. However, there are problems where constraints must be maintained in the modularization, for example, coping with functions or systems that either cannot or must be performed in regions with excessive heat, pressure, magnetic or other fields. Excluding such field boundary considerations can result in DSM computed modular architectural solutions that bundle field‐incompatible functions or components that are not practical. Such regional field constraint considerations are not taken into account using conventional DSM clustering algorithms. We introduce a DSM‐based clustering algorithm that incorporates these practical embodiment constraints through a constraint matrix indicating which elements can or cannot be placed in the same field region. We then employ reinforcement learning to allow the clustering algorithm to exploit its learnings from the previous attempts and during the clustering to facilitate the optimization under constraints. We demonstrate two examples of a medical contrast injector and the controller board of a three‐phase pump motor.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47258569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Systems engineers use the term sociotechnical system in academic literature and in their practice. Sociotechnical systems are gaining more attention as systems engineers aspire to address the social elements of their systems engineering practice as well as societal challenges, such as those included in the International Council on Systems Engineering (INCOSE) Vision 2035. Even though there is a basic working definition of sociotechnical system in the Systems Engineering Book of Knowledge (SEBoK), use of the term varies in systems engineering literature depending on application domain. As systems engineering research and practice venture into social domains, it is critical that systems engineers have a shared understanding of terms they use as a foundation of knowledge and practice. To contribute to this foundation, this study is a systematic literature review of how the term sociotechnical system is used in systems engineering literature. We used the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) methodological framework and Web of Science (WoS) for conducting the systematic literature review. We only included peer‐reviewed systems engineering academic papers in this study, and these papers had to either explicitly define the term or implicitly define it by context in the paper. In total, 61 papers were included after inclusion criteria were met, and these were evaluated and synthesized into definition categories. Evaluation and synthesis were conducted according to the PRISMA framework by the study authors in order to manage bias. We found that most papers use sociotechnical system generically as a system that includes social (people) and technical elements. Papers with more refined definitions of sociotechnical system stem from two distinct theoretical traditions: ergonomics/safety and philosophy of engineering. We do not aim to propose a single, normative definition of sociotechnical system. This study is limited by including only systems engineering literature since sociotechnical system has established definitions in other disciplines (e.g., social sciences disciplines). However, the outcome of this study provides systems engineers with documented understanding of how the term sociotechnical system is used within systems engineering.
{"title":"A systematic literature review of sociotechnical systems in systems engineering","authors":"Dana Polojärvi, E. Palmer, C. Dunford","doi":"10.1002/sys.21664","DOIUrl":"https://doi.org/10.1002/sys.21664","url":null,"abstract":"Systems engineers use the term sociotechnical system in academic literature and in their practice. Sociotechnical systems are gaining more attention as systems engineers aspire to address the social elements of their systems engineering practice as well as societal challenges, such as those included in the International Council on Systems Engineering (INCOSE) Vision 2035. Even though there is a basic working definition of sociotechnical system in the Systems Engineering Book of Knowledge (SEBoK), use of the term varies in systems engineering literature depending on application domain. As systems engineering research and practice venture into social domains, it is critical that systems engineers have a shared understanding of terms they use as a foundation of knowledge and practice. To contribute to this foundation, this study is a systematic literature review of how the term sociotechnical system is used in systems engineering literature. We used the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) methodological framework and Web of Science (WoS) for conducting the systematic literature review. We only included peer‐reviewed systems engineering academic papers in this study, and these papers had to either explicitly define the term or implicitly define it by context in the paper. In total, 61 papers were included after inclusion criteria were met, and these were evaluated and synthesized into definition categories. Evaluation and synthesis were conducted according to the PRISMA framework by the study authors in order to manage bias. We found that most papers use sociotechnical system generically as a system that includes social (people) and technical elements. Papers with more refined definitions of sociotechnical system stem from two distinct theoretical traditions: ergonomics/safety and philosophy of engineering. We do not aim to propose a single, normative definition of sociotechnical system. This study is limited by including only systems engineering literature since sociotechnical system has established definitions in other disciplines (e.g., social sciences disciplines). However, the outcome of this study provides systems engineers with documented understanding of how the term sociotechnical system is used within systems engineering.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41264710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lara Qasim, A. Hein, Sorin Olaru, Jean-Luc Garnier, M. Jankovic
System reconfiguration is essential in complex systems management, as it is an enabler of system flexibility and adaptability. It ensures system operation and increases reliability, availability, maintainability, testability, safety, and reuse of system entities and technologies. For the reconfiguration of a system in use, it is necessary to assess, in continuity, the system's state with regard to its context. Identifying data supporting system reconfiguration represents a major industrial challenge and is linked directly to the development of industrial reconfiguration tools. Reconfiguration tools are based on a data model, also called ontology, which represents key concepts of system reconfiguration and their relationships. A particular difficulty of developing the data model is the multi‐domain nature of reconfiguration. Furthermore, it needs to address a considerable diversity of system types. Few publications propose an ontology supporting data identification and tool development for the entire process. Hence, in this paper we propose to formalize the system reconfiguration process and propose an overarching ontology, which we call OSysRec. This ontology considers data at the management, dynamics, and structure level. The proposed ontology has been developed based upon expert knowledge and several industrial uses cases. The OSysRec ontology allowed a better understanding of the reconfiguration process, and hence it can be deployed for developing efficient and effective reconfiguration tools at the industrial scale. The ontology has been tested on an industrial case study to validate the proposed approach.
{"title":"System reconfiguration ontology to support model‐based systems engineering: Approach linking design and operations","authors":"Lara Qasim, A. Hein, Sorin Olaru, Jean-Luc Garnier, M. Jankovic","doi":"10.1002/sys.21661","DOIUrl":"https://doi.org/10.1002/sys.21661","url":null,"abstract":"System reconfiguration is essential in complex systems management, as it is an enabler of system flexibility and adaptability. It ensures system operation and increases reliability, availability, maintainability, testability, safety, and reuse of system entities and technologies. For the reconfiguration of a system in use, it is necessary to assess, in continuity, the system's state with regard to its context. Identifying data supporting system reconfiguration represents a major industrial challenge and is linked directly to the development of industrial reconfiguration tools. Reconfiguration tools are based on a data model, also called ontology, which represents key concepts of system reconfiguration and their relationships. A particular difficulty of developing the data model is the multi‐domain nature of reconfiguration. Furthermore, it needs to address a considerable diversity of system types. Few publications propose an ontology supporting data identification and tool development for the entire process. Hence, in this paper we propose to formalize the system reconfiguration process and propose an overarching ontology, which we call OSysRec. This ontology considers data at the management, dynamics, and structure level. The proposed ontology has been developed based upon expert knowledge and several industrial uses cases. The OSysRec ontology allowed a better understanding of the reconfiguration process, and hence it can be deployed for developing efficient and effective reconfiguration tools at the industrial scale. The ontology has been tested on an industrial case study to validate the proposed approach.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46308117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rune Andre Haugen, Nils-Olav Skeie, G. Muller, Elisabet Syverud
Modern product development often generates systems of high complexity that are prone to emergent behavior. The industry has a need to establish better practices to detect inherent emergent behavior when engineering such systems. Philosophers and researchers have debated emergence throughout history, tracing to the time of the Greek philosopher Aristotle (384–322 B.C.) and current literature has both philosophical and practical examples of emergence in modern systems. In this review paper, we investigate the phenomenon of emergent behavior in engineered systems. Our aim is to describe emergence in engineered systems and propose methods to detect it, based on literature. Emergence is in general explained as dynamic behavior seen at macro level that cannot be traced back to the micro level. Emergence can be known or unknown in combination with positive or negative. We find that best practices to engineer complicated systems should contain a sensible suite of traditional approaches and methods, while best practices to engineer complex systems need extensions to this considering a new paradigm using incentives to guide system behavior rather than testing it up‐front.
{"title":"Detecting emergence in engineered systems: A literature review and synthesis approach","authors":"Rune Andre Haugen, Nils-Olav Skeie, G. Muller, Elisabet Syverud","doi":"10.1002/sys.21660","DOIUrl":"https://doi.org/10.1002/sys.21660","url":null,"abstract":"Modern product development often generates systems of high complexity that are prone to emergent behavior. The industry has a need to establish better practices to detect inherent emergent behavior when engineering such systems. Philosophers and researchers have debated emergence throughout history, tracing to the time of the Greek philosopher Aristotle (384–322 B.C.) and current literature has both philosophical and practical examples of emergence in modern systems. In this review paper, we investigate the phenomenon of emergent behavior in engineered systems. Our aim is to describe emergence in engineered systems and propose methods to detect it, based on literature. Emergence is in general explained as dynamic behavior seen at macro level that cannot be traced back to the micro level. Emergence can be known or unknown in combination with positive or negative. We find that best practices to engineer complicated systems should contain a sensible suite of traditional approaches and methods, while best practices to engineer complex systems need extensions to this considering a new paradigm using incentives to guide system behavior rather than testing it up‐front.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47339584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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