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“from Complexity To Clarity: Benefits Of Lawyers In Complicated Matters”

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Complexity Cheat #02: Creating Clarity Through Visualization

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By John R. Turner John R. Turner Scilit Preprints.org Google Scholar and Rose M. Baker Rose M. Baker Scilit Preprints.org Google Scholar *

Discrete Event Simulation For Engineering Systems

Received: 22 December 2018 / Revised: 10 January 2019 / Accepted: 15 January 2019 / Published: 19 January 2019

Systems theory has been challenged in recent literature because of its perceived disconnect from today’s research and practice needs. Moving away from reductionist frameworks and the complex domain dominated by known unknowns and order, a call is being made in the social sciences to begin using complexity theory and newer methods of connection that better address in complexity and open social systems. Scholars and scholar-practitioners will continue to find the need to apply complexity theory as wicked problems become more prevalent in the social sciences. This paper differentiates between general systems theory (GST) and complexity theory, as well as identifying the advantages for the social sciences of incorporating complexity theory as a formal theory. Complexity theory has been expanded and recognized as providing a new perspective and a new way of theorizing that can be done by disciplines within the social sciences. These additions can better position the social sciences to address the complexity associated with technological advances, globalization, complex markets, cultural change, and the myriad challenges and opportunities that lie ahead.

The implementation of new technological innovations in the workplace and globalization are just two indicators of the future, which requires a higher skilled workforce [1] and heralds an intensification of complexity in the workplace due to an increase in the rate of non expected change [2], information overload, globalization , and geopolitical unrest. Organizations need to manage this growing complexity with the human resources at their disposal, skilled or unskilled, through the adoption and diffusion of complexity science. Becoming more prevalent in many disciplines as a way of making sense of and managing such complexity, complexity science is often recognized as the “new science” [3] (p. 94), in which organizations are viewed as complex systems that cannot be observed using traditional linear methods.

DeMattos, Miller, and Park [4] describe three trends contributing to the growth of complexity science. First, dramatic changes are occurring for both organizations and governments in part due to “globalization, intense local and global competition, process re-engineering, workforce diversity, quality improvement, and continuous change” [4] (p. 1554). Second, we are in an information revolution; the productivity of information processes increases, and costs decrease (eg, information acquisition, processing, and storage). Third, organizations are dissolving at alarming rates [4].

Goal Setting Properties Ppt Design

Criticized by some because many of the existing project management tools and methodologies are reductionistic and more suited to single projects than to multiple projects [5], a call for new methods and techniques for multi-project management efforts project is done. Since multi-projects dominate project management, with some data estimating that multi-projects constitute as high as 90% of projects, Aritua et al. [5] challenged the discipline of project management to take research from complex, dynamic systems and from complexity theory to gain new insights into the development of new methods and techniques. Similarly, other disciplines have been called upon to take an ecosystem approach, as in Gregory, Atkins, Burdon, and Elliott’s [6] study of marine management. The ecosystem approach uses complexity science by calling for a shift from reductionistic research (eg, single-species research), compartmentalized decision-making, and policy formulation to one that recognizes complex systems. with multiple elements (eg, ecological, social, economic, and political ) [6].

Traditional sciences have used a reductionistic framework or a realist philosophy [7], where an entity is reduced to its smaller parts. By understanding the workings of smaller parts, the whole can be understood more comprehensively [4]. Although this reductionistic framework served science well in the past, such as during the Industrial Revolution [4], it does not serve science well today due to the complexities of the modern world (e.g., increasing bad problems, global warming, information overload , globalization, and geopolitical unrest). Complexity science expands on the reductionistic framework by not only understanding the parts that contribute to the whole but by understanding how each part interacts with all the other parts and emerges. into a new entity, thus developing a more comprehensive and complete understanding of the whole. Individual causal research in complex systems is close to futile; a comprehensive approach is necessary to consider the unpredictability found in complex systems [7]. New theoretical models that reflect “real-life complexity” are requested by researchers [8] (p. 162). To better understand such systems, complexity science offers complex adaptive systems (CAS) as “a framework for understanding these systems” [4] (p. 1550).

Although there are relatively clear distinctions between systems theory, complex adaptive systems, and complexity theory, the literature within some of the human resource (HR) disciplines (human resource; human resource development, HRD; and human resource management, HRM) has failed. to make this distinction. Today, for example, HRD still recognizes systems theory as one of its foundational theories even though many disciplines have moved to complexity theory through complex adaptive systems due to the changing and complex environment in which they operate. Disciplines have been forced to address open systems and more complex problems (eg, wicked problems) as opposed to using the traditional reductionistic methods used in the last century. In contrast, highlighted within the HR and other social science literatures, systems theory has been recognized as more of a myth than a foundational theory, partly because of its disconnect between practice and theory and in the overwhelming use of linear methods when analyzing social systems [9] .

The present article identifies the differences between general systems theory (GST), complex adaptive systems, and complexity theory. It begins with a brief discussion that varies between systems, followed by an explanation of what GST entails. Then, clarification of open and closed systems is presented along with a discussion that defines the basic principles of complex adaptive systems (CAS) and complexity theory. The article provides some current examples of the use of complexity theory and concludes with new directions for social science disciplines, recommending the integration of complexity theory into future research efforts. In the concluding remarks, the authors argue for the integration of complexity theory with more non-reductionistic methods in the study of relationships and understanding in the social sciences in addition to traditional GST and reductionistic methods. Also, the present article extends the work presented by Haslberger [8] in describing complexity theory as a new potential method for explanation and theorizing. We suggest the same in the present article—that complexity theory be adopted as a new mode of explanation and theory for the social sciences.

Yin & Yang: A Dynamic Whole By Dr Gemma Jiang

Referring to the core concepts of GST, Kast and Rosenzweig [10] (p. 450) define a system as “consisting of interconnected parts or elements.” von Bertalanffy [11] (p. 416) defined a system as a “model of general nature, that is, a conceptual analog of some relatively universal properties of observed entities.” Perhaps the most relevant definition of a system is that it represents a whole composed of several parts/members [12]. This definition hits on the difference between a system and systems: while the former represents the whole (the system), the latter constitutes the whole (parts, systems, and subsystems). Components of a system also depend on other components [12].

When dealing with systems, researchers need to clearly define which level they are examining. Kast and Rosenzweig [10] (p. 455) emphasize defining both “the boundaries of the system under consideration and the level of … analysis [systems].” These boundaries vary and are usually set by the researcher or the theoretical system. Once the boundary and appropriate level of analysis (system, system, subsystems) have been determined, the structure of the system can be modeled, providing

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