Relevant bibliographies by topics / Complex adaptive systems

Academic literature on the topic 'Complex adaptive systems'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Complex adaptive systems"

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Lansing, J. Stephen. "Complex Adaptive Systems." Annual Review of Anthropology 32, no. 1 (October 2003): 183–204. http://dx.doi.org/10.1146/annurev.anthro.32.061002.093440.

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Holland, John H. "Studying Complex Adaptive Systems." Journal of Systems Science and Complexity 19, no. 1 (March 2006): 1–8. http://dx.doi.org/10.1007/s11424-006-0001-z.

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Clymer, John R. "Mathematics of complex adaptive systems." International Journal of Design & Nature and Ecodynamics 12, no. 3 (September 30, 2017): 377–84. http://dx.doi.org/10.2495/dne-v12-n3-377-384.

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Rosenboom, David. "Complex adaptive systems in music." Journal of the Acoustical Society of America 92, no. 4 (October 1992): 2403. http://dx.doi.org/10.1121/1.404732.

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Comfort, Louise K., and Sandra L. Resodihardjo. "Leadership in Complex Adaptive Systems." International Review of Public Administration 18, no. 1 (April 2013): 1–5. http://dx.doi.org/10.1080/12294659.2013.10805236.

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Paley, John. "Complex adaptive systems and nursing." Nursing Inquiry 14, no. 3 (September 2007): 233–42. http://dx.doi.org/10.1111/j.1440-1800.2007.00359.x.

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White, B. E. "Complex adaptive systems engineering (CASE)." IEEE Aerospace and Electronic Systems Magazine 25, no. 12 (December 2010): 16–22. http://dx.doi.org/10.1109/maes.2010.5638784.

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Holden, Lela M. "Complex adaptive systems: concept analysis." Journal of Advanced Nursing 52, no. 6 (December 2005): 651–57. http://dx.doi.org/10.1111/j.1365-2648.2005.03638.x.

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Spivey, Michael J. "Discovery in complex adaptive systems." Cognitive Systems Research 51 (October 2018): 40–55. http://dx.doi.org/10.1016/j.cogsys.2018.05.001.

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Schoening, Bill. "Boundaries and Complex Adaptive Systems." INSIGHT 11, no. 1 (January 2008): 22–23. http://dx.doi.org/10.1002/inst.200811122.

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Dissertations / Theses on the topic "Complex adaptive systems"

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Bondorowicz, Stefan. "Adaptive control of complex dynamic systems." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302787.

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Kingston, Kenneth Samuel. "Applications of complex adaptive systems approaches to coastal systems." Thesis, University of Plymouth, 2003. http://hdl.handle.net/10026.1/474.

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This thesis investigates the application of complex adaptive systems approaches (e.g. Artificial Neural Networks and Evolutionary Computation) to the study of coastal hydrodynamic and morphodynamic behaviour. Traditionally, nearshore morphological coastal system studies have developed an understanding of those physical processes occurring on both short temporal, and small spatial scales with a large degree of success. The associated approaches and concepts used to study the coastal system at these scales have primarily been linear in nature. However, when these approaches to studying the coastal system are extended to investigating larger temporal and spatial scales, which are commensurate with the aims of coastal management, results have had less success. The lack of success in developing an understanding of large scale coastal behaviour is to a large extent attributable to the complex behaviour associated with the coastal system. This complexity arises as a result of both the stochastic and chaotic nature of the coastal system. This allows small scale system understanding to be acquired but prevents the larger scale behaviour to be predicted effectively. This thesis presents four hydro-morphodynamic case studies to demonstrate the utility of complex adaptive system approaches for studying coastal systems. The first two demonstrate the application of Artificial Neural Networks, whilst the latter two illustrate the application of Evolutionary Computation. Case Study #1 considers the nature of the discrepancy between the observed location of wave breaking patterns over submerged sandbars and the actual sandbar locations. Artificial Neural Networks were able to quantitatively correct the observed locations to produce reliable estimates of the actual sand bar locations. Case Study #2 considers the development of an approach for the discrimination of shoreline location in video images for the production of intertidal maps of the nearshore region. In this case the system modelled by the Artificial Neural Network is the nature of the discrimination model carried out by the eye in delineating a shoreline feature between regions of sand and water. The Artificial Neural Network approach was shown to robustly recognise a range of shoreline features at a variety of beaches and hydrodynamic settings. Case Study #3 was the only purely hydrodynamic study considered in the thesis. It investigated the use of Evolutionary Computation to provide means of developing a parametric description of directional wave spectra in both reflective and nonreflective conditions. It is shown to provide a unifying approach which produces results which surpassed those achieved by traditional analysis approaches even though this may not strictly have been considered as a fiddly complex system. Case Study #4 is the most ambitious application and addresses the need for data reduction as a precursor when trying to study large scale morphodynamic data sets. It utilises Evolutionary Computation approaches to extract the significant morphodynamic variability evidenced in both directly and remotely sampled nearshore morphologies. Significant data reduction is achieved whilst reWning up to 90% of the original variability in the data sets. These case studies clearly demonstrate the ability of complex adaptive systems to be successfully applied to coastal system studies. This success has been shown to equal and sometimess surpass the results that may be obtained by traditional approaches. The strong performance of Complex Adaptive System approaches is closely linked to the level of complexity or non-linearity of the system being studied. Based on a qualitative evaluation, Evolutionary Computation was shown to demonstrate an advantage over Artificial Neural Networks in terms of the level of new insights which may be obtained. However, utility also needs to consider general ease of applicability and ease of implementation of the study approach. In this sense, Artificial Neural Networks demonstrate more utility for the study of coastal systems. The qualitative assessment approach used to evaluate the case studies in this thesis, may be used as a guide for choosing the appropriateness of either Artificial Neural Networks or Evolutionary Computation for future coastal system studies.
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Shenoy, Rajiv. "Overset adaptive strategies for complex rotating systems." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51796.

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The resolution of the complex physics of rotating configurations is critical for any engineering analysis that requires multiple frames of reference. Two well-known applications are in the rotorcraft and wind energy industries. Rotor wake impingement from rotor-fuselage and wind turbine-tower interactions impact structural and acoustic characteristics. Additionally, parasite drag resulting from rotorcraft hubs may result in severe limitations on forward flight vehicle performance. Complex turbulent wakes from rotors and hubs impinging on downstream empennage can create adverse aeroelastic behavior and can affect handling qualities. Numerical simulations of these flows require state-of-the-art Navier Stokes methods using dynamic overset grids. However, many current methods typically used in industry result in wakes that dissipate essential features. In order to address these concerns, two advancements are introduced in this thesis. Feature-based grid adaptation on dynamic overset grids has been developed and demonstrated with an unstructured Navier Stokes solver. The unique feature of the adaptation technique is that it is applied globally on the overset grid system except within the boundary layer. In concert with grid adaptation, an efficient parallelized search algorithm for solution interpolation over massively distributed systems has been created. This results in cost-effective interpolation that retains the numerical order of accuracy and has been verified in both space and time. The improvements have been demonstrated for rotor-fuselage interaction and a generic rotating hub. Detailed analysis of convergence of the methodology and sensitivity of the results to relevant parameters have also been included.
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Morris, Paul John. "Modelling peatlands as a complex adaptive systems." Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/479.

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A new conceptual approach to modelling peatlands, DigiBog, involves a Complex Adaptive Systems consideration of raised bogs. A new computer hydrological model is presented, tested, and its capabilities in simulating hydrological behaviour in a real bog demonstrated. The hydrological model, while effective as a stand-alone modelling tool, provides a conceptual and algorithmic structure for ecohydrological models presented later. Using DigiBog architecture to build a cellular model of peatland patterning dynamics, various rulesets were experimented with to assess their effectiveness in predicting patterns. Contrary to findings by previous authors, the ponding model did not predict patterns under steady hydrological conditions. None of the rulesets presented offered an improvement over the existing nutrient-scarcity model. Sixteen shallow peat cores from a Swedish raised bog were analysed to investigate the relationship between cumulative peat decomposition and hydraulic conductivity, a relationship previously neglected in models of peatland patterning and peat accumulation. A multivariate analysis showed depth to be a stronger control on hydraulic conductivity than cumulative decomposition, reflecting the role of compression in closing pore spaces. The data proved to be largely unsuitable for parameterising models of peatland dynamics, due mainly to problems in core selection. However, the work showed that hydraulic conductivity could be expressed quantitatively as a function of other physical variables such as depth and cumulative decomposition. DigiBog architecture was used to build a simple, vertical, ecohydrological model of long-term peat accumulation. As model complexity was increased under a self-organisation approach, model predictions of peat accumulation rates and surface wetness changed dramatically, revealing the importance of feedbacks between peatland hydrological behaviour and peat physical properties. This work may have important implications for palaeoclimatic reconstructions which assume peatland surface wetness to be a reliable climatic indicator. The expansion of the model to include horizontal space altered model behaviour in quantitative and qualitative terms.
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Hammer, Roger Julius. "Strategy development process and complex adaptive systems." Thesis, Aston University, 2011. http://publications.aston.ac.uk/15812/.

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The two areas of theory upon which this research was based were 'strategy development process' (SDP) and 'complex adaptive systems' (CAS), as part of complexity theory, focused on human social organisations. The literature reviewed showed that there is a paucity of empirical work and theory in the overlap of the two areas, providing an opportunity for contributions to knowledge in each area of theory, and for practitioners. An inductive approach was adopted for this research, in an effort to discover new insights to the focus area of study. It was undertaken from within an interpretivist paradigm, and based on a novel conceptual framework. The organisationally intimate nature of the research topic, and the researcher's circumstances required a research design that was both in-depth and long term. The result was a single, exploratory, case study, which included use of data from 44 in-depth, semi-structured interviews, from 36 people, involving all the top management team members and significant other staff members; observations, rumour and grapevine (ORG) data; and archive data, over a 5½ year period (2005-2010). Findings confirm the validity of the conceptual framework, and that complex adaptive systems theory has potential to extend strategy development process theory. It has shown how and why the strategy process developed in the case study organisation by providing deeper insights to the behaviour of the people, their backgrounds, and interactions. Broad predictions of the 'latent strategy development' process and some elements of the strategy content are also possible. Based on this research, it is possible to extend the utility of the SDP model by including peoples' behavioural characteristics within the organisation, via complex adaptive systems theory. Further research is recommended to test limits of the application of the conceptual framework and improve its efficacy with more organisations across a variety of sectors.
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Kennedy, Cameron. "Mass media and media complex adaptive systems, towards a complex methodology." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0002/MQ43352.pdf.

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Kennedy, Cameron (Cameron John) Carleton University Dissertation Journalism and Communication. "Mass media and media complex adaptive systems; towards a complex methodology." Ottawa, 1999.

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Engler, Joseph John. "Innovation as a complex adaptive system." Thesis, University of Iowa, 2009. https://ir.uiowa.edu/etd/233.

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Innovation has long been considered crucial for companies to gain a competitive edge in the global marketplace. Unfortunately, a solid understanding of the system of innovation does not exist. The literature lacks formal definitions and methodologies for the system of innovation. Many surrogates for innovation metrics have been posited in past research but none have solidified the overall concept of an innovation system or science. It has been speculated that innovation as a system is complex. Additionally, some researchers have suggested that this innovation system is adaptive. In these instances, of the literature, surrogates were again utilized in place of solid modeling and hypothesis that is benchmarked against real world case studies. Surrogates, such as patent citation, do serve a useful purpose to assist in the understanding of the historic nature of the innovation system but they fall short of defining the system completely. This paper seeks to aid in the solidification of a hypothesis of the system of innovation as a complex adaptive system. Initial consideration is directed towards the historic interactions that have taken place in the system of innovation. These interactions are viewed through the surrogate of patent citation as there is little other record of innovation. The novelty of this paper is that patent citations form not the core but rather a starting point for the definition of innovation as a complex adaptive system. Various models are built using techniques of cellular automata as well as agent-based modeling to assist in the understanding of the principles at work in the innovation system. These models present startling evidence that there exists an upper bound on the number of interactions any one invention should utilize in its course towards being deemed an innovation. Additionally, the models describe the benefits of partnership between innovating entities in a rapidly changing marketplace such as the current technological markets. This paper asserts specific conclusions, from the models, that assist in understanding that the system of innovation is truly a complex adaptive system. The models are further supported through real world examples.
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Jefferies, Paul. "Emergent phenomena of complex adaptive systems : financial markets." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427625.

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Fonseca, Jose Manuel Lopes Da. "Innovation : a property of complex adaptive social systems." Thesis, University of Hertfordshire, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263031.

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Books on the topic "Complex adaptive systems"

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

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

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

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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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Book chapters on the topic "Complex adaptive systems"

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Gross, Thilo, and Hiroki Sayama. "Adaptive Networks." In Understanding Complex Systems, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01284-6_1.

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Niazi, Muaz A., and Amir Hussain. "Complex Adaptive Systems." In Cognitive Agent-based Computing-I, 21–32. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3852-2_3.

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da Silva Vieira, Ricardo. "Complex Adaptive Systems." In Industry, Innovation and Infrastructure, 1–11. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-71059-4_11-1.

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Dekkers, Rob. "Complex Adaptive Systems." In Applied Systems Theory, 169–90. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10846-9_8.

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Haefner, James W. "Complex Adaptive Systems." In Modeling Biological Systems, 424–37. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-4119-6_20.

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Whalen, Thomas B. "Complex adaptive systems." In Complexity, Society and Social Transactions, 61–67. Abingdon, Oxon ; NewYork, NY : Routledge, 2018. | Series: Routledge studies in social and political thought: Routledge, 2017. http://dx.doi.org/10.4324/9781315179919-9.

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Iba, Hitoshi. "Complex Adaptive Systems." In AI and SWARM, 137–59. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429027598-5.

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Dekkers, Rob. "Complex Adaptive Systems." In Applied Systems Theory, 211–33. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57526-1_9.

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da Silva Vieira, Ricardo. "Complex Adaptive Systems." In Industry, Innovation and Infrastructure, 139–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-95873-6_11.

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Alaimo, Leonardo Salvatore. "Complex Systems and Complex Adaptive Systems." In Encyclopedia of Quality of Life and Well-Being Research, 1–3. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-69909-7_104659-1.

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Conference papers on the topic "Complex adaptive systems"

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White, B. E. "Complex Adaptive Systems Engineering (CASE)." In 2009 3rd Annual IEEE Systems Conference. IEEE, 2009. http://dx.doi.org/10.1109/systems.2009.4815774.

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Varadan, Vijay K. "MEMS- and NEMS-based complex adaptive smart devices and systems." In Complex Adaptive Structures, edited by William B. Spillman, Jr. SPIE, 2001. http://dx.doi.org/10.1117/12.446770.

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Arshad, Saba, Hafsa Maryam, Humera Yasmeen, and Munam Ali Shah. "Complex Adaptive Systems." In the 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3206098.3206120.

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Sigmund, Karl. "Complex adaptive systems and the evolution of reciprocation." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386816.

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Vakili, Golnaz, and Siavash Khorsandi. "Engineering a peer to peer architecture: A complex adaptive system approach." In 2010 4th Annual IEEE Systems Conference. IEEE, 2010. http://dx.doi.org/10.1109/systems.2010.5482487.

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Bo, Zunwang, Wenlin Gong, and Shensheng Han. "Ghost Imaging of Complex-valued Objects." In Adaptive Optics: Analysis, Methods & Systems. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/aoms.2015.jt5a.20.

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DeRosa, Joseph K., and L. Keith McCaughin. "Combined Systems Engineering and Management in the Evolution of Complex Adaptive Systems." In 2007 1st Annual IEEE Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/systems.2007.374653.

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Ignatiev, M. B., and P. I. Makin. "Adaptive capabilities of complex systems." In 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM). IEEE, 2017. http://dx.doi.org/10.1109/scm.2017.7970688.

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Sherrington, David. "Statistical physics of adaptive correlation of agents in a market." In Disordered and complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1358169.

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Harmon, Jakob, and Stephen G. Wilson. "Complex nonlinear adaptive predistortion." In 2012 46th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2012. http://dx.doi.org/10.1109/ciss.2012.6310785.

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Reports on the topic "Complex adaptive systems"

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Chassin, David P., Joel M. Malard, Christian Posse, Asim Gangopadhyaya, Ning Lu, Srinivas Katipamula, and J. V. Mallow. Modeling Power Systems as Complex Adaptive Systems. Office of Scientific and Technical Information (OSTI), December 2004. http://dx.doi.org/10.2172/877087.

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Lichtblau, Dale E., Brian A. Haugh, Gregory N. Larsen, and Terry Mayfield. Analyzing Adversaries as Complex Adaptive Systems. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada464073.

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Detry, Richard Joseph, John Michael Linebarger, Patrick D. Finley, S. Louise Maffitt, Robert John, Jr Glass, Walter Eugene Beyeler, and Arlo Leroy Ames. Complex Adaptive Systems of Systems (CASOS) engineering environment. Office of Scientific and Technical Information (OSTI), February 2012. http://dx.doi.org/10.2172/1038222.

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Linebarger, John Michael, S. Louise Maffitt, Robert John, Jr Glass, Walter Eugene Beyeler, and Arlo Leroy Ames. Complex Adaptive System of Systems (CASoS) Engineering Applications. Version 1.0. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1038214.

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Moore, Thomas W., Tu-Thach Quach, Richard Joseph Detry, Stephen Hamilton Conrad, Andjelka Kelic, Shirley J. Starks, Walter Eugene Beyeler, et al. Phoenix : Complex Adaptive System of Systems (CASoS) engineering version 1.0. Office of Scientific and Technical Information (OSTI), August 2011. http://dx.doi.org/10.2172/1038215.

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Busch, Timothy E., and Dawn A. Trevisani. Modeling of Complex Adaptive Systems in Air Operations. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada457738.

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Brodsky, Nancy S., Patrick D. Finley, Walter Eugene Beyeler, John Michael Linebarger, Thomas W. Moore, Robert John, Jr Glass, S. Louise Maffitt, Michael David Mitchell, and Arlo Leroy Ames. Complex Adaptive Systems of Systems (CASoS) engineering and foundations for global design. Office of Scientific and Technical Information (OSTI), January 2012. http://dx.doi.org/10.2172/1035333.

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HARRIS, DAVID L. Echo Implemented: A Model for Complex Adaptive Systems Computer Experimentation. Office of Scientific and Technical Information (OSTI), July 2001. http://dx.doi.org/10.2172/786628.

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Colvin, Nathan M. A Complex Adaptive Systems Approach to the Future Operational Environment. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada611766.

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Whitesides, George. Dynamic, Adaptive, Systems and Materials: Complex, Simple and Emergent Behaviors. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1888751.

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