Journal articles: 'Complexity science framework'

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Curran, Sara R. "The Global Complexity Framework." Globalizations 5, no. 2 (June 2008): 107–9. http://dx.doi.org/10.1080/14747730802057431.

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Avanzini, Martin, and Georg Moser. "A combination framework for complexity." Information and Computation 248 (June 2016): 22–55. http://dx.doi.org/10.1016/j.ic.2015.12.007.

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Maldonado, Carlos Eduardo. "Death and Complexity." Revista Latinoamericana de Bioética 21, no. 1 (July 23, 2021): 113–26. http://dx.doi.org/10.18359/rlbi.5376.

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There is no such thing as a science of death, although there is a science of life, as it happens. Death is not so much the subject matter of science but an experience, and death experiences we find abundantly in the literature. Now, experience is told not so much in a scientific tenure but as a narrative. Within the framework of bioethics, death comes closer, particularly what is usually known as end-of-life dilemmas, i.e., palliative care, a most sensitive arena, if there is any at all. This paper argues about the interplay or dialogue between death and complexity science. It claims that the knowledge of death is truly the knowledge of life and provides three arguments that lead to the central claim. The first argument is very much close to a kind of heuristic for knowing about death, while the second shows the challenge of knowing death. The third one consists of a reappraisal of death within an extensive cultural or civilizing framework. Lastly, some open-ended conclusions are drawn.

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James, Kia M. G. "Incorporating Complexity Science Theory Into Nursing Curricula." Creative Nursing 16, no. 3 (August 2010): 137–42. http://dx.doi.org/10.1891/1078-4535.16.3.137.

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Complexity science theory is a natural framework for nursing educators and nurse leaders to use in leading and solving complex, unpredictable problems in highly complex organizations and evolving health care systems. This article explores the basic tenets that must be embraced by nurse leaders and educators to integrate complexity science theory into nursing education curricula.

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Khan, Suhel Ahmad, and Raees Ahmad Khan. "Security assessment framework: a complexity perspective." Computer Fraud & Security 2014, no. 7 (July 2014): 13–17. http://dx.doi.org/10.1016/s1361-3723(14)70513-5.

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Sismondo, Sergio. "Bourdieu’s Rationalist Science of Science: Some Promises and Limitations." Cultural Sociology 5, no. 1 (January 31, 2011): 83–97. http://dx.doi.org/10.1177/1749975510389728.

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At several points over his career, Pierre Bourdieu articulated a framework for a sociology of science, derived mostly from a priori reasoning about scientific actors in competition for capital. This article offers a brief overview of Bourdieu’s framework, placing it in the context of dominant trends in Science and Technology Studies. Bourdieu provides an excellent justification for the project of the sociology of science, and some starting points for analysis. However, his framework suffers from his commitment to a vague evolutionary epistemology, and from his correlative and surprising neglect of science’s habituses, with their particular practices, boundaries, and political economies. To be productive, Bourdieu’s sociology of science would have to abandon its narrow rationalism and embrace the material complexity of the sciences.

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Serrano-Santoyo, Arturo. "Exploring a Framework for Innovation from the Perspective of Complexity Science." Procedia Technology 9 (2013): 139–45. http://dx.doi.org/10.1016/j.protcy.2013.12.015.

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Durmus, Deniz. "Complexity in economics and beyond: Review paper." Heritage and Sustainable Development 3, no. 1 (April 8, 2021): 34–43. http://dx.doi.org/10.37868/hsd.v3i1.51.

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Complexity and complexity economics are relatively new fields of science, both of which started at the beginning of the 1980s. As it had emerged, questions have been raised regarding complexity’s applicability on human-involved systems and its predictive powers. Economics has been in the spotlight in recent years within the framework of complexity, since economics is one of the most well-established fields in social sciences. Within this framework, the field is referred to as complexity economics. While in its early years, complexity economics research had distanced itself from the reductionist neoclassic tradition of economics that has been identified by its use of, and reliance on, descriptive equations. One of the salient feature of complexity economics is its somewhat unorthodox approach to economic systems, as in its emphasis on non-equilibria. However in recent years, the proponents have become more assertive that complexity economics needs to be more modest and symbiotically co-exist with well-established mainstream economics. Although we focus on economics under the prism of complexity, our underlying interest is in the investigation of how other disciplines, such as industrial engineering and operations research, may benefit from a similar complexity-oriented perspective. Keywords: Complexity, complexity economics, mainstream economics, equation-based economics, non-equilibrium

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Dmitriev, Andrey, Vasily Kornilov, and Svetlana Maltseva. "Complexity of a Microblogging Social Network in the Framework of Modern Nonlinear Science." Complexity 2018 (December 2, 2018): 1–11. http://dx.doi.org/10.1155/2018/4732491.

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Recent developments in nonlinear science have caused the formation of a new paradigm called the paradigm of complexity. The self-organized criticality theory constitutes the foundation of this paradigm. To estimate the complexity of a microblogging social network, we used one of the conceptual schemes of the paradigm, namely, the system of key signs of complexity of the external manifestations of the system irrespective of its internal structure. Our research revealed all the key signs of complexity of the time series of a number of microposts. We offer a new model of a microblogging social network as a nonlinear random dynamical system with additive noise in three-dimensional phase space. Implementations of this model in the adiabatic approximation possess all the key signs of complexity, making the model a reasonable evolutionary model for a microblogging social network. The use of adiabatic approximation allows us to model a microblogging social network as a nonlinear random dynamical system with multiplicative noise with the power-law in one-dimensional phase space.

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Mitic, Peter. "A complexity framework for consensus and conflict." International Journal of Design & Nature and Ecodynamics 13, no. 3 (August 30, 2018): 281–93. http://dx.doi.org/10.2495/dne-v13-n3-281-293.

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Mitic, Peter. "A complexity framework for consensus and conflict." International Journal of Design & Nature and Ecodynamics 13, no. 3 (August 30, 2018): 283–95. http://dx.doi.org/10.2495/dne-v13-n3-283-295.

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Ambrosino, Angela, Mario Cedrini, and John B. Davis. "The unity of science and the disunity of economics." Cambridge Journal of Economics 45, no. 4 (June 17, 2021): 631–54. http://dx.doi.org/10.1093/cje/beab014.

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Abstract In the article, we propose a general theoretical framework to distinguish a set of possible options for integration between social sciences. Adopting the so-called ‘nation’ metaphor in order to investigate relationships between disciplines, the framework uses an analogy with Dani Rodrik’s ‘world political trilemma’ (whereby democracy—here self-determination of science—national sovereignty—here disciplines—and global economic integration—here disciplinary integration—are mutually incompatible) to distinguish three different roads to the realisation of the unity of social science (‘reductionism’, ‘integration’ and ‘complexity’). The framework is then applied to recent proposals for unifying the social sciences that have originated within the economics profession at a time of pervasive specialisation and increasing fragmentation. While discussing the origins, the feasibility and the desirability of disciplinary integration projects, we concentrate on the issue of pluralism in both social sciences and within economics and on the ‘structural’ conditions that would permit economics to participate in the development of a transdisciplinary behavioural science.

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Possas, Cristina de Albuquerque. "Social ecosystem health: confronting the complexity and emergence of infectious diseases." Cadernos de Saúde Pública 17, no. 1 (February 2001): 31–41. http://dx.doi.org/10.1590/s0102-311x2001000100003.

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The emergence and re-emergence of infectious diseases and their rapid dissemination worldwide are challenging national health systems, particularly in developing countries affected by extreme poverty and environmental degradation. The expectations that new vaccines and drugs and global surveillance would help reverse these trends have been frustrated thus far by the complexity of the epidemiological transition, despite promising prospects for the near future in biomolecular research and genetic engineering. This impasse raises crucial issues concerning conceptual frameworks supporting priority-setting, risk anticipation, and the transfer of science and technology's results to society. This article discusses these issues and the limitations of social and economic sciences on the one hand and ecology on the other as the main theoretical references of the health sciences in confronting the complexity of these issues on their own. The tension between these historically dissociated paradigms is discussed and a transdisciplinary approach is proposed, that of social ecosystem health, incorporating these distinct perspectives into a comprehensive framework.

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Riscos-Núñez, Agustín. "A Framework for Complexity Classes in Membrane Computing." Electronic Notes in Theoretical Computer Science 225 (January 2009): 319–28. http://dx.doi.org/10.1016/j.entcs.2008.12.083.

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Austriaco, Nicanor P. G. "Causality Within Complexity." Journal of Interdisciplinary Studies 11, no. 1 (1999): 141–56. http://dx.doi.org/10.5840/jis1999111/27.

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By nature, every man is a philosopher who continuously seeks explanations for both the universe and the human condition. In the modern era, scientific explanations based on the scientific method and its accompanying philosophical framework of quantification, naturalism, and reductionism have obscured other approaches to explaining the world. Curiously, the emerging science of complexity and complex systems is challenging scientists to develop a more holistic approach to nature. The resulting more comprehensive view of nature combines traditional modeling based on the scientific method and empirical verification, complemented by modeling based upon philosophical principles. Aristotle's philosophy of nature suggests a model of complex systems which is both intellectually satisfying and complementary to the mathematical models already in use. The rediscovery of a philosophy of nature would contribute to a holistic worldview, providing a neutral middle ground in the science-religion dialogue.

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Potts, Jason. "Complexity, economics, and innovation policy: How two kinds of science lead to two kinds of economics and two kinds of policy." Complexity, Governance & Networks, no. 1 (January 16, 2017): 22. http://dx.doi.org/10.20377/cgn-40.

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One interpretation of complexity science is that it distinguishes two types of science—an equilibrium science of forces, as begun by Newton, and a complexity science of rules, as exemplified by Wolfram (2002). If you accept that argument, then there are also two types of economics—an equilibrium economics in which forces move resources around the economy, and a complexity economics in which generic rules structure knowledge in an economy (Dopfer & Potts, 2008). However, this also implies two types of economic policy—a policy framework based on reallocating resources and a policy framework based on redesigning rules (Colander & Kupers, 2014). Modern economic policy generally engages in both, but we argue that this reflects the idea that modern economic policy has not caught up with complexity science. We illustrate how this difference plays out in the particular domain of innovation policy.

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Lane, Justin E. "Rethinking Complexity and Culture: Cognitive Science as Explanatory Framework for Cultural Phenomena." Journal of Cognition And Culture 15, no. 5 (November 11, 2015): 435–41. http://dx.doi.org/10.1163/15685373-12342160.

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Birner, Jack. "Complexity and social sciences." On the Horizon 23, no. 2 (May 11, 2015): 100–106. http://dx.doi.org/10.1108/oth-02-2015-0007.

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Purpose – The purpose of this paper is to give an outline of the main topics of an introductory course in complexity and social sciences. Design/methodology/approach – This paper consists of a survey of the main issues and some of the classical literature for an audience with no background in philosophy of science, social philosophy, the literature on complex systems and social choice. Findings – In the didactical framework of the article, it would be more accurate to speak of learning objectives rather than findings. The learning objectives are the acquisition of the basic knowledge for understanding the features, the possibilities and the limitations of scientific explanations and predictions and their applications in the long-term perspective of complex social systems. Research limitations/implications – Again, the implications are didactic. The basic knowledge that constitutes the learning objective of the course serves to give students the instruments for recognizing the main opportunities and obstacles in social forecasting. Practical implications – The practical implications of this paper include making students aware of complexity-related problems in their working environment and of the opportunities and constraints involved in solving them. Social implications – Operators who are aware of the main issues involved can contribute to a more balanced approach to social forecasting: avoiding to raise unrealistic expectations and making more efficient use of the available instruments. Originality/value – This paper summarizes an original combination of elements from the philosophy of science, epistemology, social philosophy and social choice.

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MAINZER, KLAUS. "Symmetry and complexity in dynamical systems." European Review 13, S2 (August 22, 2005): 29–48. http://dx.doi.org/10.1017/s1062798705000645.

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Historically, static symmetric bodies and ornaments are geometric idealizations in the Platonic tradition. Actually, symmetries are locally and globally broken by phase transitions of instability in dynamical systems generating a variety of new order and partial symmetries with increasing complexity. The states of complex dynamical systems can refer to, for example, atomic clusters, crystals, biomolecules, organisms and brains, social and economic systems. The paper discusses dynamical balance as dynamical symmetry in dynamical systems, which can be simulated by computational systems. Its emergence is an interdisciplinary challenge of nonlinear systems science. The philosophy of science analyses the common methodological framework of symmetry and complexity.

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Liubchenko, Vira, Nataliia Olegovna Komleva, Svetlana L. Zinovatnaya, and Katherine A. Pysarenko. "FRAMEWORK FOR SYSTEMATIZATION OF DATA SCIENCE METHODS." Applied Aspects of Information Technology 4, no. 1 (April 10, 2021): 80–90. http://dx.doi.org/10.15276/aait.01.2021.7.

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The rapid development of data science has led to the accumulation of many models, methods, and techniques that had been successfully applied. As the analysis of publications has shown, the systematization of data science methods and techniques is an urgent task. However, in most cases, the results are relevant to applications in a particular problem domain. The paper develops the framework for the systematization of data science methods, neither domain-oriented nor task-oriented. The metamodel-method-technique hierarchy organizes the relationships between existing methods and techniques and reduces the complexity of their understanding. The first level of the hierarchy consists of metamodels of data preprocessing, data modeling, and data visualization. The second level comprises methods corresponded to metamodels. The third level collects the main techniques grouped according to methods. The authors describe the guiding principles of the framework use. It provides a possibility to define the typical process of problem-solving with data science methods. A case study is used to verify the framework’s appropriateness. Four cases of applying data science methods to solve practical problems described in publications are examined. It is shown that the described solutions are entirely agreed with the proposed framework. The recommended directions for applying the framework are defined. The constraint of the framework applying is structured or semi-structured data that should be analyzed. Finally, the ways of further research are given.

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Paarlberg, Laurie E., and Wolfgang Bielefeld. "Complexity Science—An Alternative Framework for Understanding Strategic Management in Public Serving Organizations." International Public Management Journal 12, no. 2 (May 20, 2009): 236–60. http://dx.doi.org/10.1080/10967490902865180.

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Chae, Bongsug (Kevin). "An evolutionary framework for service innovation: Insights of complexity theory for service science." International Journal of Production Economics 135, no. 2 (February 2012): 813–22. http://dx.doi.org/10.1016/j.ijpe.2011.10.015.

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Crawford, G. Christopher, and Patrick M. Kreiser. "Corporate entrepreneurship strategy: extending the integrative framework through the lens of complexity science." Small Business Economics 45, no. 2 (February 13, 2015): 403–23. http://dx.doi.org/10.1007/s11187-015-9637-1.

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Klempe, Sven Hroar. "Implicit polyphony: A framework for understanding cultural complexity." Culture & Psychology 24, no. 1 (July 3, 2017): 60–79. http://dx.doi.org/10.1177/1354067x17716390.

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Musical terms like ‘polyphony’ are often applied in psychology and other disciplines in a more or less metaphorical way. However, this article investigates how polyphony can be applied in a non-metaphorical manner, i.e. in the same way, as it is understood in musicology. The fundamental hypothesis is that music represents a basic capacity of the human mind, and that this has impact on other human capacities, like language. If so, this should be traceable in different ways in different cultures. To investigate this, ‘implicit polyphony’ is launched as a term that refers to music, which is melodic, but at the same time reveals a more or less hidden polyphonic structure. This musical phenomenon is demonstrated by examples from Bach and Ravel. It is demonstrated that polyphony is at the core of music, not only in Western classical music, but also African and other ethnical music. Implicit polyphony defined as two voices condensed into one is also found in Norwegian Sámi music. The latter leads to a conclusion, which says that continuity in music is related to verticality. Investigations in linguistics show that the oral use of language is highly comparable with implicit polyphony in music. The same is modernistic literature where the aim has been to turn language into music, as in parts of James Joyce’s novel Ulysses. By bringing in examples of lexical and conceptual blending, the final conclusion is that ‘implicit polyphony’ may serve as a tool for understanding the complexity in human thinking and culture.

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YAŞAR, Okan. "INTERACTION OF SCIENCE PARADIGM, MANAGEMENT AND RESEARCH METHODS." IEDSR Association 6, no. 11 (February 24, 2021): 138–47. http://dx.doi.org/10.46872/pj.230.

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Science paradigms determine the way scientists view concepts, phenomena and events and the way they solve problems. Scientific research methods are naturally the result of scientific paradigms. It is inevitable that developments in science will affect scientific research methods and tools. In this study, fundamental paradigm changes in science and their reflections on management sciences and research methods are discussed. The developments in disciplines such as complexity, big data analytics and neuroscience, and the developments in management fields and the reflections of the emerging theories on research methods form the framework of the research. The areas subject to these developments can be characterized by "revolutionary science" within the framework defined by Kuhn. In the framework of new science, complexity, unpredictability, nonlinear systems, uncertain cause and effect relationships have been identified as prominent concepts from analytics to theory. As a result, it has been observed that control and planning functions in the management and organizational field lost their importance and autonomous organizations with uncertain vision were born. As a result of the research, it has been observed that in this age where information is instantly produced and consumed, research methods must also keep up with changes. The new dynamics require an explanation of the process rather than the estimation of outputs. The new science is in a holistic structure formed by the information produced by different disciplines such as physics and biology. The application areas of the new science show rapid development in the field of management as in every field. It is considered that the study will contribute to the researchers on subjects that do not have sufficient knowledge in the national literature.

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Lubell, Mark. "Governing Institutional Complexity: The Ecology of Games Framework." Policy Studies Journal 41, no. 3 (August 2013): 537–59. http://dx.doi.org/10.1111/psj.12028.

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Creignou, N., J. Schmidt, and M. Thomas. "Complexity Classifications for Propositional Abduction in Post's Framework." Journal of Logic and Computation 22, no. 5 (July 7, 2011): 1145–70. http://dx.doi.org/10.1093/logcom/exr012.

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Ortman, Scott G., José Lobo, and Michael E. Smith. "Cities: Complexity, theory and history." PLOS ONE 15, no. 12 (December 8, 2020): e0243621. http://dx.doi.org/10.1371/journal.pone.0243621.

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In recent decades researchers in a variety of disciplines have developed a new “urban science,” the central goal of which is to build general theory regarding the social processes underlying urbanization. Much work in urban science is animated by the notion that cities are complex systems. What does it mean to make this claim? Here we adopt the view that complex systems entail both variation and structure, and that their properties vary with system size and with respect to where and how they are measured. Given this, a general framework regarding the social processes behind urbanization needs to account for empirical regularities that are common to both contemporary cities and past settlements known through archaeology and history. Only by adopting an explicitly historical perspective can such fundamental structure be revealed. The identification of shared properties in past and present systems has been facilitated by research traditions that define cities (and settlements more broadly) as networks of social interaction embedded in physical space. Settlement Scaling Theory (SST) builds from these insights to generate predictions regarding how measurable properties of cities and settlements are related to their population size. Here, we focus on relationships between population and area across past settlement systems and present-day world cities. We show that both patterns and variations in these measures are explicable in terms of SST, and that the framework identifies baseline infrastructural area as an important system-level property of urban systems that warrants further study. We also show that predictive theory is helpful even in cases where the data do not conform to model predictions.

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Öry, Zsolt. "An integrating common framework for measuring cognitive software complexity." Software Engineering Journal 8, no. 5 (1993): 263. http://dx.doi.org/10.1049/sej.1993.0032.

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Doak, Joe, and Nikos Karadimitriou. "(Re)development, Complexity and Networks: A Framework for Research." Urban Studies 44, no. 2 (February 2007): 209–29. http://dx.doi.org/10.1080/00420980601074953.

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Tsekeris, Charalambos, and Olga Papadopoulou. "Complexity, Relatedness and Uncertainty in Contemporary Social Science: A Preliminary Reflection." tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 11, no. 2 (October 27, 2013): 475–79. http://dx.doi.org/10.31269/triplec.v11i2.515.

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This concise preliminary reflection seeks to offer a fresh transdisciplinary lens to comprehensively discern and evaluate the various interlinks and overlaps between theory and methodology in current social scientific thought. A brief elaboration on the metatheoretical issues of complexity, relatedness and uncertainty encourages a suitable and sustainable analytic framework for generating, developing and cultivating a more open, dialogical and critical-reflexive way of perceiving social science and the precarious social world in general.

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Tsekeris, Charalambos, and Olga Papadopoulou. "Complexity, Relatedness and Uncertainty in Contemporary Social Science: A Preliminary Reflection." tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 11, no. 2 (October 27, 2013): 475–79. http://dx.doi.org/10.31269/vol11iss2pp475-479.

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This concise preliminary reflection seeks to offer a fresh transdisciplinary lens to comprehensively discern and evaluate the various interlinks and overlaps between theory and methodology in current social scientific thought. A brief elaboration on the metatheoretical issues of complexity, relatedness and uncertainty encourages a suitable and sustainable analytic framework for generating, developing and cultivating a more open, dialogical and critical-reflexive way of perceiving social science and the precarious social world in general.

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Koleros, Andrew, Sean Mulkerne, Mark Oldenbeuving, and Danielle Stein. "The Actor-Based Change Framework: A Pragmatic Approach to Developing Program Theory for Interventions in Complex Systems." American Journal of Evaluation 41, no. 1 (August 22, 2018): 34–53. http://dx.doi.org/10.1177/1098214018786462.

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Despite a wide body of literature on the importance of program theory and the need to tackle complexity to improve international development programming, the use of program theory to underpin interventions aimed at facilitating change in complex systems remains a challenge for many program practitioners. The actor-based change framework offers a pragmatic approach to address these challenges, integrating concepts and frameworks drawn from complexity science and behavioral change literature to develop robust program theory for complex interventions. This article presents the conceptual framework for the approach and describes how it has been applied in practice on an evaluation of a security and justice program in Nepal. It concludes with a discussion of the strengths and weaknesses of the approach in practice and how it can be applied more widely to improve program theory for interventions in complex systems.

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Rzevski, George. "Investigating Current Social, Economic and Educational Issues using Framework and Tools of Complexity Science." Journal of the World Universities Forum 1, no. 2 (2008): 75–84. http://dx.doi.org/10.18848/1835-2030/cgp/v01i02/59521.

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Heuckmann, Benedikt, and Albert Zeyer. "Science|Environment|Health, One Health, Planetary Health, Sustainability, and Education for Sustainable Development: How Do They Connect in Health Teaching?" Sustainability 14, no. 19 (September 29, 2022): 12447. http://dx.doi.org/10.3390/su141912447.

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In this paper, we explore Science|Environment|Health, One Health, Planetary Health, and Sustainability/Education for Sustainable Development in the context of the 2030 Agenda as four major frameworks that take a step toward investigating health from different angles to tackle the grand challenges that lie ahead of humanity. In most of these frameworks, the topic of health is no longer limited to the health of humans; it also encompasses the health of ecosystems and planetary systems. Therefore, our ways of teaching and discussing health in science education may need to be adjusted. To this aim, we first shortly characterize the four frameworks and then analyze the concepts of health, the contributing sciences, and the role of values in the frameworks. In our opinion, three main questions have to be settled: (1) Which concept of health lies at the root of each framework? (2) Which sciences should be considered when teaching about health, and what role will they take in an interdisciplinary, integrative approach and under complexity restraints? (3) What is the role of values in these frameworks, and how can the is–ought fallacy be avoided? We finally discuss our findings in light of the concept of two-eyed seeing in science education. This concept helps us disentangle and sharpen the three main questions and draw implications for teaching about health in school science.

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Hornung, Richard D., and Scott R. Kohn. "Managing application complexity in the SAMRAI object-oriented framework." Concurrency and Computation: Practice and Experience 14, no. 5 (2002): 347–68. http://dx.doi.org/10.1002/cpe.652.

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Witherington, David C., Timothy I. Vandiver, and Jacob A. Spinks. "Embracing variability and complexity and the explanatory reductionism of scientific realism." Theory & Psychology 31, no. 3 (June 2021): 417–22. http://dx.doi.org/10.1177/09593543211016095.

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We agree with Arocha’s criticism of psychological science’s reliance on statistical procedures that factor out intraindividual variability and the complex dynamics inherent to behavior, as well as with his call for the adoption of a metatheoretical framework that embraces such variability. However, we disagree that scientific realism provides such a framework, given its reductive privileging of certain forms of explanation over others. We advocate, instead, a process-relational paradigm and the explanatory pluralism that it supports, allowing psychological science to more dynamically, and realistically, model individual human behavior.

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Grandon Gill, T. "The complexity and the case method." Management Decision 52, no. 9 (October 14, 2014): 1564–90. http://dx.doi.org/10.1108/md-11-2013-0575.

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Purpose – The purpose of this paper is to propose a framework that uses complexity as a means of better understanding the role that case studies can play in the classroom and in building bridges between research and practice. Design/methodology/approach – The paper synthesizes complexity theory and the practical classroom and case writing experiences of the author into a framework. Findings – A narrow view of the impact of case studies severely limits their widespread adoption. Rather than treating a case study as a document of very limited value to an academic career, the author needs to recognize their role in building relationships between research and practice. Through these relationships, opportunities for students and two-way flows of knowledge between academia and practice can be achieved. Research limitations/implications – The framework developed assumes that domain of study is complex – involving many interacting elements taking place in a context where an objectively “right” or “best” answer is unlikely to be realized. It is less clear that it is applicable to situations where optimal procedures are available and can be taught or learned. Practical implications – A key implication of the framework is that separating the use of case studies in the classroom (i.e. case facilitation) from the development of case studies in the field (i.e. case writing) can greatly diminish their value. Social implications – The proposed framework argues for greater interaction between the academic and practitioner communities. Originality/value – The paper offers a comprehensive perspective on cases that is rarely expressed. It should be of particular value to faculty and administrators seeking to justify the development and use of case studies.

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Cohen, Eli. "Building an Informing Science Model in Light of Fake News." Informing Science: The International Journal of an Emerging Transdiscipline 22 (2019): 095–114. http://dx.doi.org/10.28945/4486.

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Aim/Purpose: Many disciplines have addressed the issue of “fake news.” This topic is of central concern to the transdiscipline of Informing Science, which endeavors to understand all issues related to informing. This paper endeavors to build a model to address not only fake news but all informing and misin-forming. To do this, it explores how errors get into informing systems, the issue of bias, and the models previously created to explore the complexity of informing. That is, this paper examines models and frameworks proposed to explore informing in the presence of bias, misinformation, disinformation, and fake news from the perspective of Informing Science. It concludes by intro-ducing a more nuanced model that considers some of the topics explored in the paper Methodology: The issue of informing and disinforming crosses many disciplinary perspectives. Each discipline puts on blinders that limit what it can contribute to its understanding of research topics. It is like trying to study a forest by seeing only the trees and not the animals or the animals but not the trees. Research perspectives that cross disciplinary boundaries are needed to more fully understand complex phenomena. This paper lays out some fundamental cross-disciplinary issues including how errors find their way into informing systems, the issue of bias, and the frameworks used to model this phenomenon. Contribution: The paper introduces the competition framework for understanding informing and misinforming. This framework addresses many of the limitation of prior frameworks. Future Research: The concluding framework offers insights into understanding informing and disinforming. But this framework offers no insights into other forms of informing that are less well explored, such as song, dance, physical art, and architecture. Likewise, this framework does nothing to help the un-derstanding of informing via fideism or psychedelic revelation.

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Mainzer, Klaus. "Challenges of Complexity in the 21st Century. An Interdisciplinary Introduction." European Review 17, no. 2 (May 2009): 219–36. http://dx.doi.org/10.1017/s1062798709000714.

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The theory of nonlinear complex systems has become a proven problem-solving approach in the natural sciences from cosmic and quantum systems to cellular organisms and the brain. Even in modern engineering science self-organizing systems are developed to manage complex networks and processes. It is now recognized that many of our ecological, social, economic, and political problems are also of a global, complex, and nonlinear nature. Modern evolutionary economics can be modelled in the framework of complex systems and nonlinear dynamics. Historically, evolutionary economics was inspired by Schumpeterian concepts of business cycles and innovation dynamics. What are the laws of sociodynamics? What can we learn from nonlinear dynamics for complexity management in social, economic, financial and political systems? Is self-organization an acceptable strategy to handle the complexity in firms, institutions and organizations? The world-wide crisis of financial markets and economies is a challenge for complexity research. Misleading concepts of linear thinking and mild randomness (e.g. Gaussian distributions of Brownian motion) must be overcome by new approaches of nonlinear mathematics (e.g. non-Gaussian distribution), modelling the wild randomness of turbulence at the stock markets. Systemic crises need systemic answers. Nevertheless, human cognitive capabilities are often overwhelmed by the complexity of nonlinear systems they are forced to manage. Traditional mathematical decision theory assumed perfect rationality of economic agents (homo oeconomicus). Herbert Simon, Nobel Prize laureate of economics and one of the leading pioneers of systems science and cognitive science, introduced the principle of bounded rationality. Therefore, we need new insights into the factual microeconomic behaviour of economic agents by methods of humanities, cognitive and social sciences, which are sometimes called ‘experimental economics’. Social and economic dynamics are interdisciplinary challenges of modern complexity research.

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Nothhaft, Howard, and Stefan Wehmeier. "Coping with Complexity: Sociocybernetics as a Framework for Communication Management." International Journal of Strategic Communication 1, no. 3 (July 10, 2007): 151–68. http://dx.doi.org/10.1080/15531180701434785.

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Curticapean, Radu. "Block interpolation: A framework for tight exponential-time counting complexity." Information and Computation 261 (August 2018): 265–80. http://dx.doi.org/10.1016/j.ic.2018.02.008.

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Pastor, Luis, Sofia Bayona, Juan Brito, María Cuevas, Isabel Fernaud, Sergio Galindo, Juan García-Cantero, et al. "A Unified Framework for Neuroscience Morphological Data Visualization." Applied Sciences 11, no. 10 (May 19, 2021): 4652. http://dx.doi.org/10.3390/app11104652.

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The complexity of the human brain makes its understanding one of the biggest challenges that science is currently confronting. Due to its complexity, the brain has been studied at many different levels and from many disciplines and points of view, using a diversity of techniques for getting meaningful data at each specific level and perspective, producing sometimes data that are difficult to integrate. In order to advance understanding of the brain, scientists need new tools that can speed up this analysis process and that can facilitate integrating research results from different disciplines and techniques. Visualization has proved to be useful in the analysis of complex data, and this paper focuses on the design of visualization solutions adapted to the specific problems posed by brain research. In this paper, we propose a unified framework that allows the integration of specific tools to work together in a coordinated manner in a multiview environment, displaying information at different levels of abstraction and combining schematic and realistic representations. The two use cases presented here illustrate the capability of this approach for providing a visual environment that supports the exploration of the brain at all its organizational levels.

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Waldherr, Annie, Stephanie Geise, Merja Mahrt, Christian Katzenbach, and Christian Nuernbergk. "Toward a Stronger Theoretical Grounding of Computational Communication Science." Computational Communication Research 3, no. 2 (October 1, 2021): 1–28. http://dx.doi.org/10.5117/ccr2021.02.002.wald.

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Abstract Computational communication science (CCS) is embraced by many as a fruitful methodological approach to studying communication in the digital era. However, theoretical advances have not been considered equally important in CCS. Specifically, we observe an emphasis on mid-range and micro theories that misses a larger discussion on how macro-theoretical frameworks can serve CCS scholarship. With this article, we aim to stimulate such a discussion. Although macro frameworks might not point directly to specific questions and hypotheses, they shape our research through influencing which kinds of questions we ask, which kinds of hypotheses we formulate, and which methods we find adequate and useful. We showcase how three selected theoretical frameworks might advance CCS scholarship in this way: (1) complexity theory, (2) theories of the public sphere, and (3) mediatization theory. Using online protest as an example, we discuss how the focus (and the blind spots) of our research designs shifts with each framework.

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Smith-Nonini, Sandy. "Making Complexity Your Friend: Reframing Social Theory for the Anthropocene." Weather, Climate, and Society 9, no. 4 (August 23, 2017): 687–99. http://dx.doi.org/10.1175/wcas-d-16-0124.1.

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Abstract This article uses the dilemma of climate change as an entry point to explore the utility of a complexity framework for a more comprehensive social science of environmental sustainability. A theory of complex adaptive systems (CAS) is especially appropriate for the Anthropocene, a newly proposed geological period defined around humanity’s impact on the biosphere. Aspects of complexity theory have been entering public consciousness through popular accounts of climate “tipping points” and “emergent” change—the risk that Earth’s climate could shift into a new pattern in a relatively short time period. Social structures, including capitalism, are complex systems, as are social movements. The paper reviews CAS research with special attention to applications in social ecology. It discusses two case studies of exemplary research on human management of environmental resources and one case study of the antiglobalization movement, all conceived within a complexity framework. The central argument is that complexity thinking will enhance social studies of sustainability and efforts to create a more resilient economy and biosphere.

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Couraud, Benoit, Thibaut Deleruyelle, Remy Vauche, David Flynn, and Spyridon Nektarios Daskalakis. "A Low Complexity Design Framework for NFC-RFID Inductive Coupled Antennas." IEEE Access 8 (2020): 111074–88. http://dx.doi.org/10.1109/access.2020.3001610.

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Urlacher, Brian R. "Complexity, Causality, and Control in Statistical Modeling." American Behavioral Scientist 64, no. 1 (July 13, 2019): 55–73. http://dx.doi.org/10.1177/0002764219859641.

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Social scientists using statistical models and more qualitative techniques frequently employ divergent approaches to thinking about causality. Statistical methodologies tend to draw on probabilistic understandings of causality. Qualitative research traditions, however, have advanced a sophisticated framework around necessary and sufficient conditions. In particular, the qualitative comparative analysis approach has embraced theory development that emphasizes equifinality and complex causal relationships. This article reviews the two traditions and explores how a causal framework grounded in necessary and sufficient conditions can be adapted to statistical models. A logistic regression analysis of major contributions to peacekeeping missions is used to illustrate both the viability of blending the two traditions as well as the potential for more sophisticated theory development and testing.

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Akhromeyeva, Tatiana S., and Georgiy G. Malinetskiy. "New Pythagoreanism, Complexity, Artificial Intelligence." Russian Journal of Philosophical Sciences 64, no. 1 (June 2, 2021): 45–70. http://dx.doi.org/10.30727/0235-1188-2021-64-1-45-70.

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Leibniz defined mathematics as the “science of possible worlds.” But what worlds were assumed as the possible ones by thinkers of different eras? It is this question that is considered in this article. According to Einstein, development of science requires “external justification” (observations and experiments that should be explained or predicted) and internal perfection (following the internal logic of this discipline). Mathematics has no “external justification” and experiment cannot refute mathematical theory. In this sense, mathematics is closer to creativity, to art than to natural science. Its connection with culture turns out to be more complex and mediated. To explain this connection, Daniel Bell, within the framework of his theory of social development, puts forward the “axial principle,” according to which the role of science is considered as the main characteristic of society. From this point of view, we single out traditional, industrial and post-industrial societies. Each of these phases has its own ideals, norms, and types of mathematical creativity. In traditional society, following the Pythagorean tradition, mathematics is focused on finding harmony in nature, on identifying unity on the basis of universal relationships determined by the numerical characteristics of the objects under study. As the industrial era approaches, the constructivist, or “project-based,” approach becomes increasingly important. And unity emerges at a higher “meta-level.” The forerunner of this direction is Descartes, who raised the question of finding a single, universal method for solving all mathematical problems. The work traces the change in the formulation of a number of “invariant,” “eternal” mathematical problems as well as the evolution of the concept of “complexity” in the historical retrospective. Close attention is paid to the post-industrial phase of civilizational development and to “computer mathematics,” which has become the basis for formation of virtual reality, which in many respects changes the very direction of progress. As a result of this, the “extraverted orientation” of humanity, the course toward new horizons was replaced by the “introvert” one, which prioritizes tasks associated with comfort, convenience, and consumption. The “change of milestones” that has taken place is traced in a comparison of “big projects” related to mathematics that were put forward in the 1960s, and those that are considered as priorities at present. In fact, we are faced with a “crisis of expectations.” We see a way out of this crisis in a revival of the “Pythagorean tradition,” at a new level. But if in a traditional society the goal of the development of mathematics in alliance with other sciences and arts was to reveal harmony in the natural world, then at the post-industrial phase, the priorities are different. They are associated with computer modeling, understanding, and identifying the foundations of harmony in the human world.

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Calitz, Andre, and Margaret Cullen. "The Application of a Knowledge Management Framework to Automotive Original Component Manufacturers." Interdisciplinary Journal of Information, Knowledge, and Management 12 (2017): 337–65. http://dx.doi.org/10.28945/3897.

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Aim/Purpose: This paper aims to present an example of the application of a Knowledge Man-agement (KM) framework to automotive original component manufacturers (OEMs). The objective is to explore KM according to the four pillars of a selected KM framework. Background: This research demonstrates how a framework, namely the George Washington University’s Four Pillar Framework, can be used to determine the KM status of the automotive OEM industry, where knowledge is complex and can influence the complexity of the KM system (KMS) used. Methodology: An empirical study was undertaken using a questionnaire to gather quantitative data. There were 38 respondents from the National Association of Automotive Component and Allied Manufacturers (NAACAM) and suppliers from three major automotive OEMs. The respondents were required to be familiar with the company’s KMS. Contribution: Currently there is a limited body of research available on the KM implementation frameworks for the automotive industry. This study presents a novel approach to the use of a KM framework to reveal the status of KM in automotive OEMs. At the time of writing, the relationship between the four pillars and the complexity of KMS had not yet been determined. Findings: The results indicate that there is a need to improve KM in the automotive OEM industry. According to the relationships investigated, the four pillars, namely leadership, organization, technology and learning, are considered important for KM, regardless of the level of KMS complexity, Recommendations for Practitioners: Automotive OEMs need to ensure that the KM aspects are established and should be periodically evaluated by using a KM framework such as the George Washington University’s Four Pillar Framework to identify KM weaknesses. Recommendation for Researchers: The establishment and upkeep of a successful KM environment is challenging due to the complexity involved with various influencing aspects. To ensure that all aspects are considered in KM environments, comprehensive KM frameworks, such as the George Washington University’s Four Pillar Framework, need to be applied. Impact on Society: The status of KM management and accessibility of knowledge in organizations needs to be periodically examined, in order to improve supplier and OEM knowledge sharing. Future Research: Although the framework used provides a process for KM status determination, this study could be extended by investigating a methodology that includes KMS best practice and tools. This study could be repeated at a national and international level to provide an indication of KM practice within the entire automotive industry.

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Krivovichev, S. V. "On the algorithmic complexity of crystals." Mineralogical Magazine 78, no. 2 (April 2014): 415–35. http://dx.doi.org/10.1180/minmag.2014.078.2.12.

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AbstractThe concept of the algorithmic complexity of crystals is developed for a particular class of minerals and inorganic materials based on orthogonal networks, which are defined as networks derived from the primitive cubic net (pcu) by the removal of some vertices and/or edges. Orthogonal networks are an important class of networks that dominate topologies of inorganic oxysalts, framework silicates and aluminosilicate minerals, zeolites and coordination polymers. The growth of periodic orthogonal networks may be modelled using structural automata, which are finite automata with states corresponding to vertex configurations and transition symbols corresponding to the edges linking the respective vertices. The model proposed describes possible relations between theoretical crystallography and theoretical computer science through the theory of networks and the theory of deterministic finite automata.

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Journal articles on the topic 'Complexity science framework'

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