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Книги з теми "Complex adaptive systems (CAS)"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Putera, Prakoso Bhairawa. Studi complex adaptive system (CAS) proses pembentukan klaster industri pendukung sistem inovasi di Indonesia. Jakarta: LIPI Press, 2011.

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2

Carmichael, Ted, Andrew J. Collins, and Mirsad Hadžikadić, eds. Complex Adaptive Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20309-2.

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3

Ang, Yang, and Shan Yin, eds. Intelligent complex adaptive systems. Hershey: IGI Pub., 2008.

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4

Gros, Claudius. Complex and Adaptive Dynamical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36586-7.

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5

Gros, Claudius. Complex and Adaptive Dynamical Systems. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16265-2.

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6

Gros, Claudius. Complex and Adaptive Dynamical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04706-0.

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7

Gros, Claudius. Complex and Adaptive Dynamical Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-71874-1.

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8

Ang, Yang, and Shan Yin, eds. Applications of complex adaptive systems. Hershey, PA: IGI Pub., 2008.

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9

Mitleton-Kelly, Eve. Complex adaptive systems in an organisational context: Organisations as co-evolving complex adaptive systems. Coventry: ESRC Business Processes Resource Centre, 1998.

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10

Mitleton-Kelly, Eve. @Organisations as co-evolving complex adaptive systems. Coventry: Business Processes Resource Centre, 1998.

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11

Fradkov, Alexander L., Iliya V. Miroshnik, and Vladimir O. Nikiforov. Nonlinear and Adaptive Control of Complex Systems. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9261-1.

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12

service), SpringerLink (Online, ed. Complex and Adaptive Dynamical Systems: A Primer. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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13

Fradkov, A. L. Nonlinear and adaptive control of complex systems. Dordrecht: Kluwer Academic Publishers, 1999.

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14

J, Morowitz Harold, and Singer Jerome L, eds. The mind, the brain, and complex adaptive systems. Reading, Mass: Addison-Wesley Pub. Co., 1995.

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15

Stonier, Russel J., and Xing Huo Yu. Complex systems: Mechanism of adaptation. Amsterdam: IOS Press, 1994.

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16

Signals and boundaries: Building blocks for complex adaptive systems. Cambridge, Mass: MIT Press, 2012.

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17

Oost, John van der, and Rodolphe Barrangou. CRISPR-Cas systems: RNA-mediated adaptive immunity in bacteria and archaea. Heidelberg: Springer, 2013.

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18

Adaptive software development: A collaborative approach to managing complex systems. New York: Dorset House Pub., 2000.

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19

Saunders, Sita Johanna. CRISPRmap: An automated classification of repeat conservation in prokaryotic adaptive immune systems. Freiburg: Universität, 2013.

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20

Complex adaptive innovation systems: Relatedness and transversality in the evolving region. New York: Routledge, 2011.

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21

Miller, John H. Complex adaptive systems: An introduction to computational models of social life. Princeton, NJ: Princeton University Press, 2006.

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22

Andreev, R. D. (Rumen D.), ed. Decision control, management, and support in adaptive and complex systems: Quantitative models. Hershey, PA: Information Science Reference, 2013.

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23

Holland, John H. 3. Complex adaptive systems (CAS). Oxford University Press, 2014. http://dx.doi.org/10.1093/actrade/9780199662548.003.0003.

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Анотація:

Complex adaptive systems (CAS) are composed of elements, called agents, that learn or adapt in response to interactions with other agents. ‘Complex adaptive systems’ shows that all CAS exhibit lever points, points where a small directed action causes large predictable changes in aggregate behaviour. All CAS agents have three levels of activity: performance (moment-by-moment capabilities), credit-assignment (rating the usefulness of available capabilities), and rule-discovery (generating new capabilities). The behaviour of a CAS is always generated by the adaptive interactions of its components; and the hierarchical structure characteristic of CAS is also generated—particular combinations of agents at one level become agents at the next higher level.

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24

Sole, Richard, and Santiago F. Elena. Viruses as Complex Adaptive Systems. Princeton University Press, 2018. http://dx.doi.org/10.23943/princeton/9780691158846.001.0001.

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Viruses are everywhere, infecting all sorts of living organisms, from the tiniest bacteria to the largest mammals. Many are harmful parasites, but viruses also play a major role as drivers of our evolution as a species and are essential regulators of the composition and complexity of ecosystems on a global scale. This book draws on complex systems theory to provide a fresh look at viral origins, populations, and evolution, and the coevolutionary dynamics of viruses and their hosts. New viruses continue to emerge that threaten people, crops, and farm animals. Viruses constantly evade our immune systems, and antiviral therapies and vaccination campaigns can be powerless against them. These unique characteristics of virus biology are a consequence of their tremendous evolutionary potential, which enables viruses to quickly adapt to any environmental challenge. This book presents a unified framework for understanding viruses as complex adaptive systems. It shows how the application of complex systems theory to viral dynamics has provided new insights into the development of AIDS in patients infected with HIV-1, the emergence of new antigenic variants of the influenza A virus, and other cutting-edge advances. The book also extends the analogy of viruses to the evolution of other replicators such as computer viruses, cancer, and languages.

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25

Brunner, Ronald D., and Amanda H. Lynch. Adaptive Governance. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.601.

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Adaptive governance is defined by a focus on decentralized decision-making structures and procedurally rational policy, supported by intensive natural and social science. Decentralized decision-making structures allow a large, complex problem like global climate change to be factored into many smaller problems, each more tractable for policy and scientific purposes. Many smaller problems can be addressed separately and concurrently by smaller communities. Procedurally rational policy in each community is an adaptation to profound uncertainties, inherent in complex systems and cognitive constraints, that limit predictability. Hence planning to meet projected targets and timetables is secondary to continuing appraisal of incremental steps toward long-term goals: What has and hasn’t worked compared to a historical baseline, and why? Each step in such trial-and-error processes depends on politics to balance, if not integrate, the interests of multiple participants to advance their common interest—the point of governance in a free society. Intensive science recognizes that each community is unique because the interests, interactions, and environmental responses of its participants are multiple and coevolve. Hence, inquiry focuses on case studies of particular contexts considered comprehensively and in some detail.Varieties of adaptive governance emerged in response to the limitations of scientific management, the dominant pattern of governance in the 20th century. In scientific management, central authorities sought technically rational policies supported by predictive science to rise above politics and thereby realize policy goals more efficiently from the top down. This approach was manifest in the framing of climate change as an “irreducibly global” problem in the years around 1990. The Intergovernmental Panel on Climate Change (IPCC) was established to assess science for the Conference of the Parties (COP) to the U.N. Framework Convention on Climate Change (UNFCCC). The parties negotiated the Kyoto Protocol that attempted to prescribe legally binding targets and timetables for national reductions in greenhouse gas emissions. But progress under the protocol fell far short of realizing the ultimate objective in Article 1 of the UNFCCC, “stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference in the climate system.” As concentrations continued to increase, the COP recognized the limitations of this approach in Copenhagen in 2009 and authorized nationally determined contributions to greenhouse gas reductions in the Paris Agreement in 2015.Adaptive governance is a promising but underutilized approach to advancing common interests in response to climate impacts. The interests affected by climate, and their relative priorities, differ from one community to the next, but typically they include protecting life and limb, property and prosperity, other human artifacts, and ecosystem services, while minimizing costs. Adaptive governance is promising because some communities have made significant progress in reducing their losses and vulnerability to climate impacts in the course of advancing their common interests. In doing so, they provide field-tested models for similar communities to consider. Policies that have worked anywhere in a network tend to be diffused for possible adaptation elsewhere in that network. Policies that have worked consistently intensify and justify collective action from the bottom up to reallocate supporting resources from the top down. Researchers can help realize the potential of adaptive governance on larger scales by recognizing it as a complementary approach in climate policy—not a substitute for scientific management, the historical baseline.

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26

Lingel, Sherrill, Tim McDonald, Timothy R. Gulden, Matthew Sargent, and Parousia Rockstroh. Leveraging Complexity in Great-Power Competition and Warfare: An Initial Exploration of How Complex Adaptive Systems Thinking Can Frame Opportunities and Challenges. RAND Corporation, The, 2021.

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27

Godbole, Dattan, Shankar S. Sastry, and Lygeros. Complex Systems: Adaptive Hierarchial Control. Prentice Hall, 2001.

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28

Fidanboy, Mahsa. Organizations and Complex Adaptive Systems. Taylor & Francis Group, 2022.

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29

Viruses as Complex Adaptive Systems. Princeton University Press, 2018.

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30

Fidanboy, Mahsa. Organizations and Complex Adaptive Systems. Taylor & Francis Group, 2022.

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31

Fidanboy, Masha. Organizations and Complex Adaptive Systems. Routledge, 2022.

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32

Fidanboy, Mahsa. Organizations and Complex Adaptive Systems. Taylor & Francis Group, 2022.

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33

Organizations and Complex Adaptive Systems. Taylor & Francis Group, 2022.

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34

Solé, Ricard, and Santiago F. Elena. Viruses As Complex Adaptive Systems. Princeton University Press, 2018.

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35

Holland, John H. 4. Agents, networks, degree, and recirculation. Oxford University Press, 2014. http://dx.doi.org/10.1093/actrade/9780199662548.003.0004.

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‘Agents, networks, degree, and recirculation’ explains that when studying complex adaptive systems (CAS) in a grammar-like way, agents serve as the ‘alphabet’. The hierarchical organization of CAS implies different kinds of agents at different levels, with correspondingly different grammars. The interactions of signal-processing agents at a point in time can be specified by a network—a snapshot of the agents’ performance capability. The combination of high fanout (the richness of an agent’s interactions) and hierarchical organization results in complex networks that include large numbers of sequences that form loops. More complex loops allow the CAS to ‘look ahead’, examining the effects of various action sequences without actually executing the actions.

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36

Langton, Christopher G. Artificial Life: An Overview (Complex Adaptive Systems). The MIT Press, 1997.

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37

Gros, Claudius. Complex and Adaptive Dynamical Systems: A Primer. Springer, 2015.

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38

Gros, Claudius. Complex and Adaptive Dynamical Systems: A Primer. Springer, 2013.

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39

Kinnear, Kenneth E. Advances in Genetic Programming (Complex Adaptive Systems). The MIT Press, 1994.

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40

Singer, Jerome L., and Harold J. Morowitz. Mind the Brain and Complex Adaptive Systems. Taylor & Francis Group, 2019.

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41

Gros, Claudius. Complex and Adaptive Dynamical Systems: A Primer. Springer London, Limited, 2015.

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42

Gros, Claudius. Complex and Adaptive Dynamical Systems: A Primer. Springer, 2011.

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43

Complex and Adaptive Dynamical Systems: A Primer. Springer, 2008.

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44

Gros, Claudius. Complex and Adaptive Dynamical Systems: A Primer. Springer, 2013.

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45

Fradkov, A. L. Nonlinear and adaptive control of complex systems. 1999.

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46

Gros, Claudius. Complex and Adaptive Dynamical Systems: A Primer. Springer, 2008.

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47

Morowitz, Harold J. Mind, the Brain and Complex Adaptive Systems. Taylor & Francis Group, 2018.

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48

Gros, Claudius. Complex and Adaptive Dynamical Systems: A Primer. Springer London, Limited, 2013.

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49

Morowitz, Harold J. Mind, the Brain and Complex Adaptive Systems. Taylor & Francis Group, 2018.

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50

An Introduction to Natural Computation (Complex Adaptive Systems). The MIT Press, 1999.

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