A Framework For Complex System Development

Industry, government, and academic efforts to create a generalized systems engineering process have repeatedly fallen short. The outcome? Systems engineering failures that produce losses like the September 1999 destruction of the Mars Climate Orbiter. A simple information transfer error between teams motivated far-reaching managerial and technical

Industry, government, and academic efforts to create a generalized systems engineering process have repeatedly fallen short. The outcome? Systems engineering failures that produce losses like the September 1999 destruction of the Mars Climate Orbiter. A simple information transfer error between teams motivated far-reaching managerial and technical changes at NASA's Jet Propulsion Laboratory-evidence of systems engineering complexity. Struck by the amount of chaos that can quickly develop from such intricacy, the author has devoted several years to the development and refinement of the framework delineated in this work to help you "control the chaos". A Framework for Complex System Development develops a generalized process that distinguishes between "time" and "logical" domains-how I/O evolves over time versus the instantaneous program state. Explicitly characterized and identified, they preserve the framework. By combining these views, you get an application specific process, versatile enough for many different contexts. It also defines the technical activities that constitute the system development process and how they connect and interact with managerial activities. You will be able to integrate these activities and realize the maximum potential for success. A key element to success in today's paradigm of "faster, better, cheaper" systems and decreasing resource levels is a clear, workable plan that can be easily implemented. A Framework for Complex System Development illustrates such a plan, distilling the essential aspects of system design into a logical process for a well-organized development program. With A Framework for Complex System Development, you can use the author's approach-developed in the crucible of the real world-to develop sound complex systems in an organized and efficient manner.

Industry, government, and academic efforts to create a generalized systems engineering process have repeatedly fallen short. The outcome? Systems engineering failures that produce losses like the September 1999 destruction of the Mars Climate Orbiter. A simple information transfer error between teams motivated far-reaching managerial and technical

Robert Siegfried presents a framework for efficient agent-based modeling and simulation of complex systems. He compares different approaches for describing structure and dynamics of agent-based models in detail. Based on this evaluation the author introduces the “General Reference Model for Agent-based Modeling and Simulation” (GRAMS). Furthermore he presents parallel and distributed simulation approaches for execution of agent-based models –from small scale to very large scale. The author shows how agent-based models may be executed by different simulation engines that utilize underlying hardware resources in an optimized fashion.

Artificial intelligence (AI) is increasingly introduced into many systems that modern society rely on and is often portrayed as a savior that can contribute to finding solutions to societal challenges e.g., social, and ecological sustainability. Many of these systems can be classified as complex systems, with interdependencies, emergent behaviors and a diversity of actors involved. As AI is increasingly introduced into these systems, we witness a transformation from complex systems into complex intelligent systems. At the same time caution is invoked toward the risks of AI regarding e.g., biases and loss of control as more tasks are transferred to AI. Hence, the introduction of AI into complex systems is associated with uncertainties around management of AI initiatives and their influence on future systems. Challenges like this can affect many different functions and professions and thus need to be understood collectively. The aim of this thesis is to examine how the actors’ prospective collective sensemaking processes in developing complex systems are affected by AI introduction. Previous research within complex systems literature shows important aspects regarding sensemaking of the system and situations within operations of complex systems. However, sensemaking in the development process of complex systems has been less studied. By examining the introduction of AI in complex systems development this thesis explores collective prospective sensemaking processes in the development of complex intelligent systems. To study an emerging phenomenon like AI introduction in complex systems, an explorative case study was found suitable. The case chosen for the study was a cross-organizational development project of an AI-tool based on Machine Learning for planning of energy systems to be used in the urban planning process of new city districts. This setting revealed plenty prospective and collective sensemaking occasions around AI introduction and exhibited continuous engagement in prospective collective sensemaking relating to the development of the AI tool and the imagined use of the AI tool in the system, which have been reported in the two appended papers. The first paper showed misalignment between actors’ sensemaking processes that alternated between seeking and disengaging behaviors. It also identified the use of boundary objects to retain disengaged actors and raised considerations around the level of detail of the boundary objects in relation to the sensemaking behaviors. The second paper identified dependencies between near- and distant-sensemaking loops that highlight challenges to connect retrospective insights with prospective imaginations by action in the present. In the creation of complex intelligent systems, human involvement seems inevitable, and the second paper exposes how AI can augment human cognition and organizational capabilities for creative imagination around possible and desirable distant-future scenarios. This thesis extends previous research on prospective and collective sensemaking in the development process of complex intelligent systems by presenting a framework of near and distant future sensemaking and internal and external complexity. This provided new insights of how knowledge flows over system levels and how to use boundary objects throughout such projects. Insight that can be useful for management of purposeful AI introduction in complex systems and society. Moreover, it contributes with an empirical case of AI introduction in complex systems to the innovation management literature. Artificiell intelligens (AI) integreras alltmer i de system som vårt moderna samhälle vilar på och framställs ofta som en viktig faktor för att lösa de utmaningar som samhället står inför, till exempel social och ekologisk hållbarhet. Många av dessa system kan definieras som komplexa system, med ömsesidiga beroenden, oförutsedda beteenden och en mångfald av inblandade aktörer. När AI introduceras i sådana system kan vi skönja en transformation från komplexa system till komplexa intelligenta system. Samtidigt påtalas ofta riskerna med AI avseende till exempel partiskhet och minskad kontroll när uppgifter tas över av AI. Införandet av AI i samhällskritiska system förknippas därmed med osäkerheter kring hanteringen av AI-initiativ och dess påverkan på framtiden. Detta leder till utmaningar som berör många olika funktioner och professioner och därmed behöver förstås kollektivt. Syftet med avhandlingen är att undersöka aktörernas framtidsorienterade, gemensamma, meningsskapande processer under utveckling av komplexa system och hur de påverkas av introduktionen av AI. Tidigare forskning inom komplexa system har visat på viktiga aspekter gällande människors förståelse av systemet och situationer framförallt inom driften av komplexa system. Meningsskapande i utvecklingsprocessen av komplexa system har dock hittills inte uppmärksammats i samma utsträckning. Genom att undersöka introduktionen av AI i utvecklingen av komplexa system utforskar denna avhandling kollektiva, framåtblickande, meningsskapande processer inom utvecklingen av komplexa intelligenta system. För studier av ett framväxande fenomen som AI-introduktion i komplexa system ansågs en explorativ fallstudie lämplig. Det valda fallet var ett utvecklingsprojekt av ett AI-verktyg baserat på maskininlärning med syfte att användas i planeringen av energisystem inom stadsplaneringsprocessen av nya stadsdelar och hade flera medverkande organisationer. Fallet visade på flera framtidsorienterade och gemensamma meningsskapande situationer kring AI-introduktion. Därmed synliggjordes de medverkandes kontinuerliga deltagande i framåtblickande gemensamt meningsskapande relaterat till utvecklingen av AI-verktyget och dess tänkta användningen i systemet, vilket rapporterats i de två bilagda artiklarna. Den första artikeln visade att de meningsskapande processerna var ur fas mellan aktörerna, vilka växlade mellan sökande och oengagerade beteenden, och att användningen av gränsöverskridande objekt med rätt detaljnivå kan involvera oengagerade aktörer. Den andra artikeln identifierade beroenden mellan olika meningsskapande cykler, närliggande respektive avlägsen framtid, vilket belyser utmaningar med att koppla tillbakablickande insikter till framåtblickande föreställningar om systemet genom handling i nuet. Vid skapande av komplexa intelligenta system framstår mänsklig inblandning som oundviklig, och den andra artikeln belyser även hur AI kan förstärka mänsklig kognition och organisatoriska förmågor för att främja kreativ föreställningsförmåga kring möjliga och önskvärda scenarier av en avlägsen framtid. Avhandlingen bidrar genom att bredda tidigare forskning om framtidsorienterat och kollektivt meningsskapande i utvecklingsprocessen av komplexa intelligenta system genom att presentera ett ramverk av närliggande och avlägset framåtblickande och intern och extern komplexitet. Det visar på nya insikter om hur kunskap flödar över systemnivåer och gränsöverskridande objekt kan användas under sådana utvecklingsprojekt. Sådana insikter kan vara praktiskt användbara för en välgrundad AI-introduktion i komplexa system och i samhället i stort. Dessutom bidrar den med en empirisk fallstudie av resan mot AI-introduktion i komplexa system till litteraturen inom innovationsledning.

This book contains all refereed papers accepted during the 14th International Conference on Complex Systems Design & Management CSD&M 2023 that took place in Beijing, People’s Republic of China by the end October 2023. Mastering complex systems requires an integrated understanding of industrial practices as well as sophisticated theoretical techniques and tools. This explains the creation of an annual go-between European and Asian forum dedicated to academic researchers and industrial actors working on complex industrial systems architecting, modeling and engineering. These proceedings cover the most recent trends in the emerging field of complex systems, both from an academic and professional perspective. A special focus was put this year on “New Trends in Complex Systems Engineering.” The CSD&M series of conferences were initiated under the guidance of CESAM Community in Europe, managed by CESAMES. Its Asian version took place in Singapore for three consecutive sessions during 2014 and 2018. The fourth Asian edition was held in Beijing in hybrid with the Chinese Society of Aeronautics and Astronautics (CSAA) as the co-organizer in 2021. Since 2023, its European and Asian conferences merge into one, taking place in China and Europe in turn. CESAM Community aims in organizing the sharing of good practices in systems architecting and model-based systems engineering (MBSE) and certifying the level of knowledge and proficiency in this field through the CESAM certification. The CESAM systems architecting, and model-based systems engineering (MBSE) certification is especially currently the most disseminated professional certification in the world in this domain through more than 3,000 real complex system development projects on which it was operationally deployed and around 10,000 engineers who were trained on the CESAM framework at international level.

This four-volume set (CCIS 643, 644, 645, 646) constitutes the refereed proceedings of the 16th Asia Simulation Conference and the First Autumn Simulation Multi-Conference, AsiaSim / SCS AutumnSim 2016, held in Beijing, China, in October 2016. The 265 revised full papers presented were carefully reviewed and selected from 651 submissions. The papers in this first volume of the set are organized in topical sections on modeling and simulation theory and methodology; model engineering for system of systems; high performance computing and simulation; modeling and simulation for smart city.

This book explores Complex System Governance (CSG)—an emerging field concerned with the design, execution, and evolution of essential functions necessary to ensure continued viability of a system. The book focuses on three primary development areas to better understand and utilize current developments CSG. First, the conceptual foundations for CSG are developed, from systems theory, management cybernetics, and governance. Second, a set of critical CSG topics are examined from conceptual as well as practice perspectives. Third, several development and application issues are discussed. Ultimately, CSG is positioned as an emerging field with strong theoretical grounding and significant implications for improving practices and performance to better address complex systems and their problems.