A meta-model of general systems processes and their linkages hyperlinked to supporting peer-reviewed research materials (poster, abstract) (original) (raw)
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Basic Notions and Models in Systems Science
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2009
The development of the idea of seeing parts of the world as 'related objects' or the 'systemic view' and its relation to conventional science is briefly described. Concepts in the systemic view regarded as fundamental and their expression as linguistic and mathematical models which would turn this view into 'systems science', are introduced. Products are represented as sets and linguistic networks of ordered pairs. Semantic diagrams describe the dynamics of change. A case study to illustrate the basic notions and models is given.
Defining " System " : a Comprehensive Approach
Over the past decades, a common definition of the term system has eluded researchers and practitioners alike. We reviewed over 100 current and historical definitions of system in an effort to understand perspectives and to propose the most comprehensive definition of this term. There is much common ground in different families of definition of system, but there are also important and significant differences. Some stem from different belief systems and worldviews, while others are due to a pragmatic desire to establish a clear definition for system within a particular community, disregarding wider considerations. In either case, it limits the effectiveness of various system communities' efforts to communicate, collaborate, and learn from the experience of other communities. We discovered that by considering a wide typology of systems, Bertalanffy's General Systems Theory provides a basis for a general, self-consistent sensible framework, capable of accommodating and showing the relationships amongst the variety of different definitions of and belief systems pertaining to system. Emergence, the appearance of a new phenomenon or capability as a result of relation or interaction between objects, is key in differentiating between objects that are systems and those that are not. Hence we propose a family of definitions, related by the common theme of emergence, which is in line with both the realist and constructivist worldviews, and covers real and conceptual systems. We believe this better reflects the current scope of systems engineering and is required to support the aspirations expressed in INCOSE SE Vision 2025. Motivation There is a need to clarify the meaning and usage of the word system, because current differences in interpretation by individuals and communities are leading to miscommunication. As this term serves different and important purposes, misinterpretations should be avoided, because they can lead to potentially adverse consequences. Our effort is to synthesize a definition, or a family of definitions, which can be shared by all those who use the term system. A well-conceived definition should enable the following objectives: communicate the meaning of system more effectively across communities of research and practice to achieve common goals,
A Disciplinary Field Model for Systemology
Translational Systems Sciences
The field of systems is still a nascent academic discipline, with a high degree of fragmentation, no common perspective on the disciplinary structure of the systems domain, and many ambiguities in its use of the term "General Systems Theory". In this chapter we develop a generic model for the structure of a discipline (of any kind) and of disciplinary fields of all kinds, and use this to develop a Typology for the domain of systems. We identify the domain of systems as a transdisciplinary field, and reiterate proposals to call it "Systemology" and its unifying theory GST* (pronounced "G-S-Tstar"). We propose names for other major components of the field, and present a tentative map of the systems field, highlighting key gaps and shortcomings. We argue that such a model of the systems field can be helpful for guiding the development of Systemology into a fully-fledged academic field, and for understanding the relationships between Systemology as a transdisciplinary field and the specialized disciplines with which it is engaged.
An introduction to systems and models
Ecological Modelling, 1983
Hartley (1965) quoted that theories pass through three stages during the search for principles, and that the search constitutes a conflict between imagination and an austere regard for truths. According to Stark (1966), these stages consist of a practical simplified theory aspiring for elegance of form, a dissonant assemblage of recalcitrant facts and finally, if ever attained, an intricate web elucidating a scientific principle or, at least, describing a scientific phenomenon. Such an approach and the theoretical pursuit to search for some general biological laws which govern the behaviour and evolution in living matter-in a way Mesarovi6 (1968) considered as being analogous to the relation between physical laws and non-living matter-are characterized by the terms systems, models and axioms. With the help of an abstract quantitative model, the constituents of a phenomenon or process can be magnified, and therefore complex biological phenomena are made easier to understand, in a more realistic way. THE SYSTEM A system is a part of reality that contains interrelated elements (de Wit and Rabbinge, 1979) of various specifications, some of which might have close links with observed behaviour, and therefore a system ought to be most useful in giving insight into true biological mechanisms (Mesarovir, 1968). In the light of Mesarovir's thinking that the behaviour of a system is input-dependent-i.e, its input-output relation depends upon the type of stimulus and amplitude-the operational definition advanced by Watt (1968) holds promise: he viewed the system as being an interlocking complex of processes characterized by many reciprocal cause-effect pathways. Furthermore, a system is not merely an interaction; Anokhin (1968) thought it also to be the integration of the activity of all its components in order to provide an effective response appropriate to the input at a given moment.
On the Architecture of Systemology and the Typology of Its Principles
Systems, 2018
Systems engineering is increasingly challenged by the rising complexity of projects undertaken, resulting in increases in costs, failure rates, and negative unintended consequences. This has resulted in calls for more scientific principles to underpin the methods of systems engineering. In this paper, it is argued that our ability to improve systems Engineering's methods depends on making the principles of systemology, of which systems engineering is a part, more diverse and more scientific. An architecture for systemology is introduced, which shows how the principles of systemology arise from interdependent processes spanning multiple disciplinary fields, and on this basis a typology is introduced, which can be used to classify systems principles and systems methods. This framework, consisting of an architecture and a typology, can be used to survey and classify the principles and methods currently in use in systemology, map vocabularies referring to them, identify key gaps, and expose opportunities for further development. It may, thus, serve as a tool for coordinating collaborative work towards advancing the scope and depth of systemology.
A Conceptual Model of Systems Engineering
INCOSE International Symposium, 2018
Systems engineering is widely perceived as an empirical discipline, with a need for theoretical foundations that can facilitate reasoning about practice. This is an attempt to help build such foundations by systems-theoretic inquiry into the nature of the relationship between knowledge and engineering. We conceptualize this relationship in terms of four worlds: the real world, the world of systems models, a world of aspect knowledge, and a world of wholes knowledge: knowledge that indicates how aspects come together and also how wholes relate to each other. This leads us to a generative understanding of systems engineering: synthesizing aspects to develop blocks; and generating the network of blocks that form a system, through recursive performance of three activities: decomposition, dependency closure and refinement. The problem of systems engineering practice involves augmenting this core with the concerns of problem formulation, design of the supporting ecosystem, and the need for closing gaps between the model world and real world. We derive some initial confidence in the validity and value of this strawman model by examining its ability to explain some aspects of current systems engineering practice, and the insights it provides into how we can integrate system modeling across knowledge domains. Introduction: Objectives and Motivation System engineering applies to various domains, enterprise application domains such as banking and insurance, and engineering domains such as infrastructure and operations. Systems engineering as a discipline is responsible for bringing multiple such domains together into a unified system that addresses a set of objectives. A central issue in systems engineering, therefore, is how knowledge from various domains come together to generate a system. Over time, engineering has developed a fabric of concepts about the nature of systems. This includes the notion of blocks (modules, components, subsystems, systems) with structures (entities with attributes, relationships among them, and operations that can be performed on them), and processes (sequences of operations) enabled by these structures that produce behavior. It also includes the notion of block composition, and associated concepts such as interfaces, dependencies, and interactions between blocks and their context. This is a general fabric of concepts that applies to all systems, thereby enabling the discipline of systems engineering, and an associated body of practice knowledge about how to engineer systems that have desired characteristics. Systems theory and systems science have delved deeper into the nature and behavior of systems, leading to concepts such as variety, dynamics and emergence, and bodies of knowledge about the nature and types of systems, relationships between structure, processes and behavior, and the behavior of networks of processes. We also have bodies of knowledge in scientific domains, enterprise (human endeavor) domains such as telecom and medicine, technology domains such as power electronics and scripting languages, and aspect domains such as security, chemistry and performance that focus on particular kinds of system characteristics and properties.
This paper revisits a question asked and debated widely over the past decade: are Systems of Systems (SoS) just traditional systems or are they a new class of systems? Many have argued that SoS are a new class of systems, but little research has been available to provide evidence of this. In this paper we share highlights of recent research to show SoS not only have a different structure than systems and thus need to be engineered differently, but also may possess different attributes for beyond first use properties (the "illities") such as flexibility and adaptability as compared to systems. By examining historical examples and by using a maritime security SoS as a research test bed, this paper shows that the "ility" called survivability had some design strategies that were directly mapped from systems and also allowed new strategies that only made sense for a SoS (e.g. vigilance). The paper also shows that some design strategies have a different implementation and meaning (e.g. margin) at the level of a system compared to SoS level. We conclude the answer to the question "Are SoS's just systems?" is both yes and no. They are manifestly systems but possess properties not found in traditional systems. This is shown to true of the meta-property of survivability as applied against a directed SoS. II. The US Department of Defense[7] defines systems of systems as "a set or arrangement of systems that results when independent and useful systems are integrated into a larger system that delivers unique capabilities", although there have been many different definitions of SoSs in the literature[8][9].. The independent and useful systems that make up a SoS are referred to as constituent systems. Not all of the components in a SoS are constituent systems; most systems of systems will still require some traditional components that are not systems themselves, but are required for the overall SoS to function properly. Some may argue that most "components" within a system, even within a traditional system like a stereo, are actually systems themselves. For example, a CD player can be considered a system because it is made up of components such as a motor, laser, and digital-to-audio-converter (DAC). Components of a traditional system that are composed themselves of subcomponents that interact with each other, are referred to as subsystems. Yet, what distinguishes a system of subsystems, from a system of systems? There are a number of key system of systems properties that have been identified in the literature[4] which distinguish systems of systems from traditional systems. They include: Operational Independence of the Elements. A system of systems is composed of constituent systems, which can independent and useful in their own right. They can exist and provide some value to stakeholders outside the SoS. In traditional systems, components are not likely to be valuable by themselves or be able to operate outside of the system. Managerial Independence of the Elements. The constituent systems have the ability to decide their own actions and behavior. Evolutionary Development. A system of systems goes through evolutionary development where components and constituent systems are added, removed, and modified in response to changes in context. Emergent Behavior. The US DoD[7] defines emergent behavior as "behavior which is unexpected or cannot be predicted from knowledge of the system's constituent parts", even though it acknowledges that there is no single, universally accepted definition of emergence. Regardless, the reason why system architects construct a system of systems in the first place, is so that the SoS can perform higher-level functions that are not possible by any single constituent system. Geographic Distribution. The span of the geographic distance between the component systems is so large, that they are usually only exchanging information and not useful quantities of mass and energy. Connections. Constituent systems tend not to be totally independent or totally dependent when interacting within a SoS. Rather, they are interdependent and loosely coupled. In traditional systems, there tends to be tight coupling and strong inter-operations between components. Multi-Functionality. The constituent systems within a system of systems tend to be able to perform multiple functions and roles within the SoS. Components within a traditional system tend to be uni-functional. Contextual Diversity. Due to the increased geographical separation, constituent systems tend to have more contextual diversity in a SoS than components in a traditional system. Unbounded. Systems of systems are often unbounded, meaning that components can be added, modified and removed independently of some central administrative control. Furthermore, the decision to add, remove or modify components is often done by stakeholders with a limited understanding of the entire SoS. Traditional systems tend to be bounded, where decisions about the addition, modification and removal of components are done by a central authority with complete knowledge of the system. Many authors acknowledge the following types of SoSs. Directed SoS. A directed SoS exists to meet a purpose given by a central authority. The component agents have their actions dictated by the central authority so as to accomplish a central purpose. An example is a joint military force composed of ships and planes under a single unified command. Collaborative SoS. A collaborative SoS has no central authority with coercive power over the constituents. The actions of the individual agents are governed both by their own needs as well as the needs of the SoS. SoS objectives are met by collective agreement of the agents to pursue an agenda. An example is the Internet. Virtual SoS. A Virtual SoS lacks any central authority. The behavior arises from the unplanned, un-coordinated interactions of the agents. An example is intermodal transport of freight via a train or truck network. Acknowledged SoS[10]. An Acknowledged SoS has a central authority but it does not have coercive power over the agents. The different agents retain their own budgets, decision-making and objectives.
Systems Geometry: A Methodology For Analyzing Emergent System Of Systems Behaviors
2013
Recent advancements in technology have led to the increased use of integrated 'systems of systems' (SoS) which link together independently developed and usable capabilities into an integrated system that exhibits new, emergent capabilities. However, the resulting SoS is often not well understood, where secondary and tertiary effects of tying systems together are often unpredictable and present severe consequences. The complexities of the composed system stem not only from system integration, but from a broad range of areas such as the competing objectives of different constituent system stakeholders, mismatched requirements from multiple process models, and architectures and interface approaches that are incompatible on multiple levels. While successful SoS development has proven to be a valuable tool for a wide range of applications, there are significant problems that remain with the development of such systems that need to be addressed during the early stages of engineering development within such environments. The purpose of this research is to define and demonstrate a methodology called Systems Geometry (SG) for analyzing SoS in the early stages of development to identify areas of potential unintended emergent behaviors as candidates for the employment of risk management strategies. SG focuses on three dimensions of interest when planning the development of a SoS: operational, functional, and technical. For Department of Defense (DoD) SoS, the operational dimension addresses the warfighter environment and includes characteristics such as mission threads and related command and control or simulation activities required to support the mission. The functional dimension highlights different roles associated with the development and use of the SoS, which could include a participant warfighter using the system, an analyst collecting data iv for system evaluation, or an infrastructure engineer working to keep the SoS infrastructure operational to support the users. Each dimension can be analyzed to understand roles, interfaces and activities. Cross-dimensional effects are of particular interest since such effects are less detectable and generally not addressed with conventional systems engineering (SE) methods. The literature review and the results of this study have identified key characteristics or dimensions that should be examined during SoS analysis and design. Although many methods exist for exploring system dimensions, there is a gap in techniques to explore cross-dimensional interactions and their effect on emergent SoS behaviors. The study has resulted in a methodology for capturing dimensional information and recommended analytical methods for intra-dimensional as well as cross-dimensional analysis. A problem-based approach to the system analysis is recommended combined with the application of matrix methods, network analysis and modeling techniques to provide intra-and cross-dimensional insight. The results of this research are applicable to a variety of socio-technical SoS analyses with applications in analysis, experimentation, test and evaluation and training. v This dissertation is dedicated to my late mother, Arnolda Clara Mannie, who taught me that it is never too late in life to reinvent yourself. vi ACKNOWLEDGMENTS Anyone who has walked this path understands that such an endeavor is not possible without the generous support of a number of individuals. I would like to acknowledge the members of my doctoral committee, Dr.Waldemar Karwowski, Dr. Petros Xanthopoulos, and Dr. Naim Kapucu for all their support throughout the dissertation process at the University of Central Florida. A special thanks to Dr. David Pratt who provided helpful feedback on my research and writing along with friendly encouragement and advice during the more challenging moments in my dissertation development. I would like to express a special acknowledgment to my major professor, Dr. Jose Sepulveda, for his patient guidance while providing space for my intellectual "wandering through the wilderness" and for joining me on my path of academic discovery. To my daughters, Carolyn and Jamie, for their support while their mom spent many nights and weekends studying and researching. And lastly, to my partner in life who has always encouraged me to follow my dream to finish my Ph.D. and held me together when it looked like it would fall apart, to James Bouwens, my husband and best friend. vii