Rapid integration of advanced sensors onto legacy military aircraft is critical for maintaining technological advantage in warfighting domains. Integration of these sensors is accomplished through upgrade programs that often fail during integration due to defect discovery and interoperability issues. Existing Department of Defense initiatives related to open architectures have improved sensor integration but have not eliminated the need for custom interface software to account for behavioral disparities across different sensors. The subject research proposes that reinforcement machine learning algorithms can be applied to aircraft sensor interfaces during integration and verifies effectiveness by training and testing Greedy, Q‐Learning, Deep Q‐Learning, Double Deep Q‐Learning, and Instance‐Based Learning algorithms against modeled Global Positioning System (GPS), Optical, Light Detection and Ranging (LIDAR), and Infrared sensor functions. The results are useful to open architecture standards management groups, sensor vendors, and systems and software engineers who are developing strategies and designs to accelerate subsystem integration timelines by reducing failures discovered during integration.
{"title":"Machine learning approaches for improving integration of advanced sensors on legacy aircraft","authors":"Zachary Dennis, T. Holzer","doi":"10.1002/sys.21689","DOIUrl":"https://doi.org/10.1002/sys.21689","url":null,"abstract":"Rapid integration of advanced sensors onto legacy military aircraft is critical for maintaining technological advantage in warfighting domains. Integration of these sensors is accomplished through upgrade programs that often fail during integration due to defect discovery and interoperability issues. Existing Department of Defense initiatives related to open architectures have improved sensor integration but have not eliminated the need for custom interface software to account for behavioral disparities across different sensors. The subject research proposes that reinforcement machine learning algorithms can be applied to aircraft sensor interfaces during integration and verifies effectiveness by training and testing Greedy, Q‐Learning, Deep Q‐Learning, Double Deep Q‐Learning, and Instance‐Based Learning algorithms against modeled Global Positioning System (GPS), Optical, Light Detection and Ranging (LIDAR), and Infrared sensor functions. The results are useful to open architecture standards management groups, sensor vendors, and systems and software engineers who are developing strategies and designs to accelerate subsystem integration timelines by reducing failures discovered during integration.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45429560","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}
Collaboration enables multiple actors with different objectives to work together and achieve a goal beyond individual capabilities. However, strategic uncertainty from partners' actions introduces a potential for losses under failed collaboration relative to pursuing an independent system. The fundamental tradeoff between high‐value but uncertain outcomes from collaborative systems and lower‐value but more certain outcomes for independent systems induces a bistability strategic dynamic. Actors exhibit different risk attitudes that impact decisions under uncertainty which complicate shared understanding of collaborative dynamics. This paper investigates how risk attitudes affect design and strategy decisions in collaborative systems through the lens of game theory. First, an analytical model studies the effect of differential risk attitudes in a two‐actor problem with stag‐hunting strategic dynamics formulated as single‐ and bi‐level games. Next, a simulation model pairs actors with different risk attitudes in a 29‐game tournament based on a prior behavioral experiment. Results show that outcomes collaborative design problems change based on the risk attitudes of both actors. Results also emphasize that considering conservative lower‐level design options facilitates collaboration by providing risk‐averse actors with a safer solution. By accepting that decision‐making actors are not all risk‐neutral, future work seeks to develop new design methods to strengthen the adoption of efficient collaborative solutions.
{"title":"Effects of differential risk attitudes in collaborative systems design","authors":"Alkim Z. Avsar, P. Grogan","doi":"10.1002/sys.21687","DOIUrl":"https://doi.org/10.1002/sys.21687","url":null,"abstract":"Collaboration enables multiple actors with different objectives to work together and achieve a goal beyond individual capabilities. However, strategic uncertainty from partners' actions introduces a potential for losses under failed collaboration relative to pursuing an independent system. The fundamental tradeoff between high‐value but uncertain outcomes from collaborative systems and lower‐value but more certain outcomes for independent systems induces a bistability strategic dynamic. Actors exhibit different risk attitudes that impact decisions under uncertainty which complicate shared understanding of collaborative dynamics. This paper investigates how risk attitudes affect design and strategy decisions in collaborative systems through the lens of game theory. First, an analytical model studies the effect of differential risk attitudes in a two‐actor problem with stag‐hunting strategic dynamics formulated as single‐ and bi‐level games. Next, a simulation model pairs actors with different risk attitudes in a 29‐game tournament based on a prior behavioral experiment. Results show that outcomes collaborative design problems change based on the risk attitudes of both actors. Results also emphasize that considering conservative lower‐level design options facilitates collaboration by providing risk‐averse actors with a safer solution. By accepting that decision‐making actors are not all risk‐neutral, future work seeks to develop new design methods to strengthen the adoption of efficient collaborative solutions.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48341290","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}
Sharon Shoshany-Tavory, E. Peleg, A. Zonnenshain, G. Yudilevitch
Conceptual‐Design is an early development phase, where innovation and creativeness shape the future system/product. Model‐Based‐Conceptual‐Design (MBCD) attempts to use best‐practices of Model‐Based‐Systems‐Engineering (MBSE) to gain the envisioned benefits of model connectivity. Using MBSE supporting tools can transform Conceptual‐Design into a digital‐engineered process but may impede creativity and innovation. Concurrently, the design domain offers specific methods and tools for innovative Conceptual‐Design. In the current study, we explore an existing Conceptual‐Design framework and offer MBSE interpretation and tools extensions needed for its digital implementation. Through such exploration we highlight MBCD specific insights and discuss modeling‐innovation interrelations. The implementation was accomplished using a domain‐specific enabling software package on top of a market‐accepted UML/SysML platform, extending the language definitions, where appropriate. The framework guided extensions allow generation of innovative bottom‐up alternatives, solution integration, and solutions’ comparison. The use of modeling is shown to offer clearer process definition, specific methods assistance, and alternative ranking—both manually and automatically. Consequently, MBCD is accomplished, which supports innovation, while being digitally connected to full‐scale‐development models and the organizational assets at large. Through integration into the orderly Systems‐Engineering process, traceability is maintained, and repeated iterations are supported, where conceptual decisions may be revisited. Additionally, through the introduction of an assets’ catalog, cross‐organizational knowledge sharing is accomplished. The paper presents samples of the extensions, using a simplified example of technology design for Future Firefighting. The value of incorporating Conceptual‐Design specific methodology and tools is evaluated through feedback from multiple domain experts. Discussion and future research directions are offered.
{"title":"Model‐based‐systems‐engineering for conceptual design: An integrative approach","authors":"Sharon Shoshany-Tavory, E. Peleg, A. Zonnenshain, G. Yudilevitch","doi":"10.1002/sys.21688","DOIUrl":"https://doi.org/10.1002/sys.21688","url":null,"abstract":"Conceptual‐Design is an early development phase, where innovation and creativeness shape the future system/product. Model‐Based‐Conceptual‐Design (MBCD) attempts to use best‐practices of Model‐Based‐Systems‐Engineering (MBSE) to gain the envisioned benefits of model connectivity. Using MBSE supporting tools can transform Conceptual‐Design into a digital‐engineered process but may impede creativity and innovation. Concurrently, the design domain offers specific methods and tools for innovative Conceptual‐Design. In the current study, we explore an existing Conceptual‐Design framework and offer MBSE interpretation and tools extensions needed for its digital implementation. Through such exploration we highlight MBCD specific insights and discuss modeling‐innovation interrelations. The implementation was accomplished using a domain‐specific enabling software package on top of a market‐accepted UML/SysML platform, extending the language definitions, where appropriate. The framework guided extensions allow generation of innovative bottom‐up alternatives, solution integration, and solutions’ comparison. The use of modeling is shown to offer clearer process definition, specific methods assistance, and alternative ranking—both manually and automatically. Consequently, MBCD is accomplished, which supports innovation, while being digitally connected to full‐scale‐development models and the organizational assets at large. Through integration into the orderly Systems‐Engineering process, traceability is maintained, and repeated iterations are supported, where conceptual decisions may be revisited. Additionally, through the introduction of an assets’ catalog, cross‐organizational knowledge sharing is accomplished. The paper presents samples of the extensions, using a simplified example of technology design for Future Firefighting. The value of incorporating Conceptual‐Design specific methodology and tools is evaluated through feedback from multiple domain experts. Discussion and future research directions are offered.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41872027","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}
Digital transformation of engineering practice, paradigms, processes, and workforce engender agreement uncertainty among professionals, particularly in the critical industry control system field. Control systems are susceptible to cyber‐mediated changes that can uniquely affect the control of the physical world from data‐centric information systems. Change to the system can be introduced by any proposed or forced alteration that affects the acceptability, suitability, feasibility, or resiliency to perform its intended mission, either positively or negatively. The potential impact of the cybersecurity threat on control systems is difficult to quantify. Agreement among professionals about decision authority and Command and Control (C2) over this threat is even more challenging to quantify. Understanding what cybersecurity entails still needs to be widely understood by the critical infrastructure control system workforce, and the control system assets are not widely understood by the Information Technology (IT) workforce. This research introduces a model and methodology for measuring multi‐concern assurance through the statistical uncertainty analysis of Likert semantic differential scales. The model addresses agreement in priority, the lack of which means there might be competing aims, competing spending, and competing focus on different aspects of the cybersecurity governance or policy as examples. The outcome identifies where different types of professionals do not agree about cybersecurity readiness and best practices for critical infrastructure control systems.
{"title":"A model for measuring multi‐concern assurance of critical infrastructure control systems","authors":"Aleksandra Scalco, S. Simske","doi":"10.1002/sys.21684","DOIUrl":"https://doi.org/10.1002/sys.21684","url":null,"abstract":"Digital transformation of engineering practice, paradigms, processes, and workforce engender agreement uncertainty among professionals, particularly in the critical industry control system field. Control systems are susceptible to cyber‐mediated changes that can uniquely affect the control of the physical world from data‐centric information systems. Change to the system can be introduced by any proposed or forced alteration that affects the acceptability, suitability, feasibility, or resiliency to perform its intended mission, either positively or negatively. The potential impact of the cybersecurity threat on control systems is difficult to quantify. Agreement among professionals about decision authority and Command and Control (C2) over this threat is even more challenging to quantify. Understanding what cybersecurity entails still needs to be widely understood by the critical infrastructure control system workforce, and the control system assets are not widely understood by the Information Technology (IT) workforce. This research introduces a model and methodology for measuring multi‐concern assurance through the statistical uncertainty analysis of Likert semantic differential scales. The model addresses agreement in priority, the lack of which means there might be competing aims, competing spending, and competing focus on different aspects of the cybersecurity governance or policy as examples. The outcome identifies where different types of professionals do not agree about cybersecurity readiness and best practices for critical infrastructure control systems.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45024876","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}
João Barata, Jorge C. S. Cardoso, Paulo Rupino Cunha
After mass production and then mass customization, the time is almost ripe for mass personalization. The goal is to offer unique products designed for the needs of each customer. However, production in larger series of products also has its advantages, and the promise of “lot size one” is still far from being the norm in several sectors of the economy. As a result of an action research project in a small household ceramic producer, this paper explores the potential of a hybrid strategy. Augmented digital engineering is adopted to (1) ensure customer participation along the entire product design lifecycle, (2) maintain the benefits of modularization and low cost, (3) minimize the waste of time and materials during product design, and (4) seek a minimum trade‐off between customer desires and engineering strategy. For theory, our work describes Industry 4.0 technology's role in achieving individual customer interaction and value co‐creation in hybrid strategies of mass customization and mass personalization. For practice, we present an example of technological architecture to implement augmented digital engineering in Industry 4.0, accessible to scenarios of hand‐intensive work and creative design processes.
{"title":"Mass customization and mass personalization meet at the crossroads of Industry 4.0: A case of augmented digital engineering","authors":"João Barata, Jorge C. S. Cardoso, Paulo Rupino Cunha","doi":"10.1002/sys.21682","DOIUrl":"https://doi.org/10.1002/sys.21682","url":null,"abstract":"After mass production and then mass customization, the time is almost ripe for mass personalization. The goal is to offer unique products designed for the needs of each customer. However, production in larger series of products also has its advantages, and the promise of “lot size one” is still far from being the norm in several sectors of the economy. As a result of an action research project in a small household ceramic producer, this paper explores the potential of a hybrid strategy. Augmented digital engineering is adopted to (1) ensure customer participation along the entire product design lifecycle, (2) maintain the benefits of modularization and low cost, (3) minimize the waste of time and materials during product design, and (4) seek a minimum trade‐off between customer desires and engineering strategy. For theory, our work describes Industry 4.0 technology's role in achieving individual customer interaction and value co‐creation in hybrid strategies of mass customization and mass personalization. For practice, we present an example of technological architecture to implement augmented digital engineering in Industry 4.0, accessible to scenarios of hand‐intensive work and creative design processes.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43994790","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}
A hybrid teaching approach that relied on combining Project Based Learning with Team Based Learning was developed in an engineering module during the past 5 years. Our motivation was to expose students to real‐world authentic engineering problems and to steer them away from the classical banking approach, with a view to developing their systems engineering skills via collaborative learning. Our third year module was called Team Design and Project Skills and was concerned with 320 students dividing themselves in teams to develop a smart electronics system. We reveal module design details and discuss the effectiveness of our teaching approach via analysis of student grades during the past 5 years, as well as data from surveys that were completed by 68 students. 64% of surveyed students agreed that the module helped broaden their perspective in electronic systems design. Moreover, 84% recognized that this module was a valuable component in their degree programme. Adopting this approach in an engineering curriculum enabled students to integrate knowledge in areas that included control systems, image processing, embedded systems, sensors, as well as team working, decision making, trouble shooting and project planning.
{"title":"Teaching undergraduate students to think like real‐world systems engineers: A technology‐based hybrid learning approach","authors":"R. Ghannam, C. Chan","doi":"10.1002/sys.21683","DOIUrl":"https://doi.org/10.1002/sys.21683","url":null,"abstract":"A hybrid teaching approach that relied on combining Project Based Learning with Team Based Learning was developed in an engineering module during the past 5 years. Our motivation was to expose students to real‐world authentic engineering problems and to steer them away from the classical banking approach, with a view to developing their systems engineering skills via collaborative learning. Our third year module was called Team Design and Project Skills and was concerned with 320 students dividing themselves in teams to develop a smart electronics system. We reveal module design details and discuss the effectiveness of our teaching approach via analysis of student grades during the past 5 years, as well as data from surveys that were completed by 68 students. 64% of surveyed students agreed that the module helped broaden their perspective in electronic systems design. Moreover, 84% recognized that this module was a valuable component in their degree programme. Adopting this approach in an engineering curriculum enabled students to integrate knowledge in areas that included control systems, image processing, embedded systems, sensors, as well as team working, decision making, trouble shooting and project planning.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42456495","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}
Modern software architectures such as microservices provide a high degree of scalability, changeability, and maintainability in application development. Furthermore, enabling controlled failure of microservices can provide abstract‐level solutions to design more resilient applications. In this paper, we introduce modularity vulnerability to analyze the vulnerability of a modular software design model under the failure of m top‐rank modules by the proposed structural metrics. The study analyzes the modularity quality coefficient (MQC) under the failure of the critical modules identified using the proposed parameter‐based greedy strategy. We conduct a comprehensive analysis of the software design generated by well‐known models and online datasets and provide a perspective for reasoning about the correlation between modularity metrics. The results show that the failure of the modules with the highest cluster factor (CF) value leads to a maximum decrease in the software modularity quality. Finally, we show a linear correlation between CF and the variations of the MQC, implying stability in the software modularity analysis (SMA) problem.
{"title":"An empirical analysis for software robustness vulnerability in terms of modularity quality","authors":"M. Abadeh, Mansooreh Mirzaie","doi":"10.1002/sys.21686","DOIUrl":"https://doi.org/10.1002/sys.21686","url":null,"abstract":"Modern software architectures such as microservices provide a high degree of scalability, changeability, and maintainability in application development. Furthermore, enabling controlled failure of microservices can provide abstract‐level solutions to design more resilient applications. In this paper, we introduce modularity vulnerability to analyze the vulnerability of a modular software design model under the failure of m top‐rank modules by the proposed structural metrics. The study analyzes the modularity quality coefficient (MQC) under the failure of the critical modules identified using the proposed parameter‐based greedy strategy. We conduct a comprehensive analysis of the software design generated by well‐known models and online datasets and provide a perspective for reasoning about the correlation between modularity metrics. The results show that the failure of the modules with the highest cluster factor (CF) value leads to a maximum decrease in the software modularity quality. Finally, we show a linear correlation between CF and the variations of the MQC, implying stability in the software modularity analysis (SMA) problem.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46571901","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}
As unmanned vehicles become smaller and more autonomous, it is becoming feasible to use them in large groups with comparatively few human operators. Design and analysis of such distributed systems are complicated by the many interactions among agents and phenomena of human behavior. In particular, human susceptibility to fatigue and cognitive overload can introduce errors and uncertainty into the system. In this paper, we demonstrate how advanced computational tools can help to overcome these engineering difficulties by optimizing multirobot, multioperator surveillance systems for cost, speed, accuracy, and stealth according to diverse user preferences in multiple case studies. The tool developed is a graphical user interface that returns the optimal number and mix of diverse agent types as a function of the user's trade‐off preferences. System performance prediction relies on a multiagent simulation with submodels for human operators, fixed‐wing unmanned aerial vehicles (UAVs), quadrotor UAVs, and flapping wing UAVs combined in different numbers.
{"title":"Modeling, simulation, and trade‐off analysis for multirobot, multioperator surveillance","authors":"James Humann, T. Fletcher, J. Gerdes","doi":"10.1002/sys.21685","DOIUrl":"https://doi.org/10.1002/sys.21685","url":null,"abstract":"As unmanned vehicles become smaller and more autonomous, it is becoming feasible to use them in large groups with comparatively few human operators. Design and analysis of such distributed systems are complicated by the many interactions among agents and phenomena of human behavior. In particular, human susceptibility to fatigue and cognitive overload can introduce errors and uncertainty into the system. In this paper, we demonstrate how advanced computational tools can help to overcome these engineering difficulties by optimizing multirobot, multioperator surveillance systems for cost, speed, accuracy, and stealth according to diverse user preferences in multiple case studies. The tool developed is a graphical user interface that returns the optimal number and mix of diverse agent types as a function of the user's trade‐off preferences. System performance prediction relies on a multiagent simulation with submodels for human operators, fixed‐wing unmanned aerial vehicles (UAVs), quadrotor UAVs, and flapping wing UAVs combined in different numbers.","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47839041","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}
This paper studies how innovation teams can be optimally configured to yield the best possible performance at different stages of a certain technology's life cycle, which correspond to different levels of environmental complexity. To conduct our analysis, we have employed computational simulations of communities searching NK landscapes at varying levels of complexity. We studied how the relative proportion of exploring agents to exploiting agents in a community impacts the evolution of scores over time, and conducted additional investigations into the role of specialization (i.e., the agents' propensity to take their preferred action) and density (i.e., the expected width of social groups within the community).
{"title":"Design teams and industry life cycles: The interplay of innovation and complexity","authors":"S. Padhee, Nunzio Lore, Babak Heydari","doi":"10.1002/sys.21678","DOIUrl":"https://doi.org/10.1002/sys.21678","url":null,"abstract":"This paper studies how innovation teams can be optimally configured to yield the best possible performance at different stages of a certain technology's life cycle, which correspond to different levels of environmental complexity. To conduct our analysis, we have employed computational simulations of communities searching NK landscapes at varying levels of complexity. We studied how the relative proportion of exploring agents to exploiting agents in a community impacts the evolution of scores over time, and conducted additional investigations into the role of specialization (i.e., the agents' propensity to take their preferred action) and density (i.e., the expected width of social groups within the community).","PeriodicalId":54439,"journal":{"name":"Systems Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45825803","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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