Literatura académica sobre el tema "Complex systems learning"
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Artículos de revistas sobre el tema "Complex systems learning"
ELMS, DAVID G. "LEARNING ABOUT COMPLEX SYSTEMS". Civil Engineering Systems 9, n.º 3 (octubre de 1992): 265–72. http://dx.doi.org/10.1080/02630259208970653.
Texto completoWong, K. Y. Michael, S. Li y Y. W. Tong. "Complex dynamics in learning systems". Physica A: Statistical Mechanics and its Applications 288, n.º 1-4 (diciembre de 2000): 397–401. http://dx.doi.org/10.1016/s0378-4371(00)00436-2.
Texto completoSterman, John D. "Learning In and About Complex Systems". Reflections: The SoL Journal 1, n.º 3 (1 de marzo de 2000): 24–51. http://dx.doi.org/10.1162/152417300570050.
Texto completoSterman, John D. "Learning in and about complex systems". System Dynamics Review 10, n.º 2-3 (1994): 291–330. http://dx.doi.org/10.1002/sdr.4260100214.
Texto completoLawson, Hal A., Dawn Anderson-Butcher, Nancy Petersen y Carenlee Barkdull. "Design Teams as Learning Systems for Complex Systems Change". Journal of Human Behavior in the Social Environment 7, n.º 1-2 (enero de 2003): 159–79. http://dx.doi.org/10.1300/j137v07n01_11.
Texto completoHerrington, Jessica, Ted Maddess, Dominique Coy, Corinne Carle, Faran Sabeti y Marconi Barbosa. "Learning Complex Texture Discrimination". Journal of Vision 18, n.º 10 (1 de septiembre de 2018): 260. http://dx.doi.org/10.1167/18.10.260.
Texto completoKim, Jaehun. "Increasing trust in complex machine learning systems". ACM SIGIR Forum 55, n.º 1 (junio de 2021): 1–3. http://dx.doi.org/10.1145/3476415.3476435.
Texto completoKempe, Vera y Patricia J. Brooks. "Second Language Learning of Complex Inflectional Systems". Language Learning 58, n.º 4 (diciembre de 2008): 703–46. http://dx.doi.org/10.1111/j.1467-9922.2008.00477.x.
Texto completoAllen, Peter M. y Mark Strathern. "Evolution, Emergence, and Learning in Complex Systems". Emergence 5, n.º 4 (diciembre de 2003): 8–33. http://dx.doi.org/10.1207/s15327000em0504_4.
Texto completoHuang, Xueqin, Xianqiang Zhu, Xiang Xu, Qianzhen Zhang y Ailin Liang. "Parallel Learning of Dynamics in Complex Systems". Systems 10, n.º 6 (15 de diciembre de 2022): 259. http://dx.doi.org/10.3390/systems10060259.
Texto completoTesis sobre el tema "Complex systems learning"
Sullivan, John P. "Emergent Learning: Three Learning Communities as Complex Adaptive Systems". Thesis, Boston College, 2009. http://hdl.handle.net/2345/663.
Texto completoIn the 2007-2008 school year, the author conducted a collaborative case study (Stake, 2000) with the goal of discovering and describing "emergent learning" in three high school classrooms. Emergent learning, defined as the acquisition of new knowledge by an entire group when no individual member of the group possessed it before, is implied by the work of many theorists working on an educational analog of a natural phenomenon called a complex adaptive system. Complex adaptive systems are well networked collectives of agents that are non-linear, bounded and synergistic. The author theorized that classes that maximized the features of complex adaptive systems could produce emergent learning (a form of synergy), and that there was a continuum of this complexity, producing a related continuum of emergence. After observing a co-curricular jazz group, an English class, and a geometry class for most of one academic year, collecting artifacts and interviewing three students and a teacher from each class, the author determined that there was indeed a continuum of complexity. He found that the actively complex nature of the Jazz Rock Ensemble produced an environment where emergence was the norm, with the ensemble producing works of music, new to the world, with each performance. The English section harnessed the chaotic tendencies of students to optimize cognitive dissonance and frequently produce emergent learning, while the mathematics section approached the learning process in a way that was too rigidly linear to allow detectable emergence to occur
Thesis (PhD) — Boston College, 2009
Submitted to: Boston College. Lynch School of Education
Discipline: Teacher Education, Special Education, Curriculum and Instruction
Attebo, Edvin. "Safe learning and control in complex systems". Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-178164.
Texto completoBondorowicz, Stefan. "Adaptive control of complex dynamic systems". Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302787.
Texto completoEagle, Nathan Norfleet. "Machine perception and learning of complex social systems". Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32498.
Texto completoIncludes bibliographical references (p. 125-136).
The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months has generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify self- report survey data, and study social network dynamics. By combining theoretical models with rich and systematic measurements, we show it is possible to gain insight into the underlying behavior of complex social systems.
by Nathan Norfleet Eagle.
Ph.D.
Al-Jubouri, Bassma. "Multi-criteria optimisation for complex learning prediction systems". Thesis, Bournemouth University, 2018. http://eprints.bournemouth.ac.uk/30857/.
Texto completoTong, Xiao Thomas. "Statistical Learning of Some Complex Systems: From Dynamic Systems to Market Microstructure". Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10917.
Texto completoStatistics
Passey, Jr David Joseph. "Growing Complex Networks for Better Learning of Chaotic Dynamical Systems". BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8146.
Texto completoCERBONI, BAIARDI LORENZO. "Adaptive models of learning in complex physical and social systems". Doctoral thesis, Urbino, 2016. http://hdl.handle.net/11576/2630552.
Texto completoTopcu, Taylan Gunes. "Management of Complex Sociotechnical Systems". Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97844.
Texto completoDoctor of Philosophy
A system is an integrated set of elements that achieve a purpose or goal. An autonomous system (ADS) is an engineered element that often substitutes for a human decision-maker, such as in the case of an autonomous vehicle. Sociotechnical systems (STSs) are systems that involve the collaboration of a human decision-maker with an ADS to fulfill their objectives. Historically, STSs have been used primarily for handling safety critical tasks, such as management of nuclear power plants. By design, STSs rely heavily on a collaboration between humans and ADS decision-makers. Therefore, the overall characteristics of a STS, such as system safety, performance, or reliability; is fully dependent on human decisions. The problem with that is that people are independent entities, who can be influenced by operational conditions. Unlike their engineered counterparts, people can be cognitively challenged, tired, or distracted, and consequently make mistakes. The current dependency on human decisions, incentivize business owners and engineers alike to increase the level of automation in engineered systems. This allows them to reduce operational costs, increase performance, and minimize human errors. However, the recent commercial aircraft accidents (e.g., Boeing 737-MAX) have indicated that increasing the level of automation is not always the best strategy. Given that increasing technological capabilities will spread the adoption of STSs, vast majority of existing jobs will either be fully replaced by an ADS or will change from a manual set-up into a STS. Therefore, we need a better understanding of the relationships between social (human) and engineered elements. This dissertation, brings together management science with systems thinking to investigate the dependencies between people and the autonomous systems they collaborate within complex socio-technical enterprises. The dissertation is organized in three mutually exclusive essays, each investigating a distinct facet of STSs: safe management, collaboration, and efficiency measurement. The first essay investigates the amount of work handled by safety-critical decision makers in STSs. Primary contribution of this study is to use an analytic method to quantify the amount of work a person could safely handle within a STSs. This method also allows to capture the aggregate impact of the social and technical factors that originate from operational conditions on workload. The second essay studies how teams of humans and their autonomous partners share work, given their preferences and operational conditions. This study presents a novel integration of machine learning algorithms to understand operational influences that propel a human-decision maker to handle the work manually or delegate it to ADSs. The results demonstrate that autonomous units successfully handle simple operational conditions. More complex conditions require both workers and their autonomous counterparts to collaborate towards common objectives. The third essay explores the complementary and contrasting roles of data-driven analytical management approaches that deal with the operational factors and investigates their sensitivity to sample size. The results are organized based on their fundamental assumptions, limitations, mathematical structure, sensitivity to sample size, and their practical usefulness. To summarize, this dissertation provides an interdisciplinary and pragmatic research approach that benefits from the strengths of both theoretical and data-driven empirical approaches. Broader impacts of this dissertation are disseminated among the literatures of systems engineering, operations research, management science, and mechanical design.
Urwin, Gerry. "Learning from complex information systems implementation : case studies in ERP projects". Thesis, Henley Business School, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268860.
Texto completoLibros sobre el tema "Complex systems learning"
Grigoʹevich, Ivakhnenko Alekseĭ, ed. Inductive learning algorithms for complex systems modeling. Boca Raton: CRC Press, 1994.
Buscar texto completo1948-, Prete Frederick R., ed. Complex worlds from simpler nervous systems. Cambridge, Mass: MIT Press, 2004.
Buscar texto completoRuan, Da. Computational intelligence in complex decision systems. Paris: Atlantis Press, 2010.
Buscar texto completoF, Chipman Susan y Meyrowitz Alan L, eds. Foundations of knowledge acquisition: Cognitive models of complex learning. Boston: Kluwer Academic, 1993.
Buscar texto completo1967-, Caballe Santi, ed. Architectures for distributed and complex M-learning systems: Applying intelligent technologies. Hershey, PA: Information Science Reference, 2010.
Buscar texto completo1951-, Kirschner Paul Arthur, ed. Ten steps to complex learning: A systematic approach to four-component instructional design. Mahwah, N.J: Lawrence Erlbaum Associates, 2007.
Buscar texto completo1951-, Kirschner Paul Arthur, ed. Ten steps to complex learning: A systematic approach to four-component instructional design. 2a ed. New York: Routledge, 2012.
Buscar texto completoTraining complex cognitive skills: A Four-Component Instructional Design model for technical training. Englewood Cliffs, N.J: Educational Technology Publications, 1997.
Buscar texto completoSocietal Learning and Change: How Governments, Business and Civil Society are Creating Solutions to Complex Multi-Stakeholder Problems. London: Taylor and Francis, 2017.
Buscar texto completoSocietal learning and change: How governments, business and civil society are creating solutions to complex multi-stakeholder problems. Sheffield: Greenleaf Pub., 2005.
Buscar texto completoCapítulos de libros sobre el tema "Complex systems learning"
Macy, Michael W., Stephen Benard y Andreas Flache. "Learning". En Understanding Complex Systems, 431–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-93813-2_17.
Texto completoMacy, Michael W., Steve Benard y Andreas Flache. "Learning". En Understanding Complex Systems, 501–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66948-9_20.
Texto completoMoser, Hubert Anton. "Systems Engineering and Learning". En Understanding Complex Systems, 11–57. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03895-7_2.
Texto completoHelbing, Dirk. "Learning of Coordinated Behavior". En Understanding Complex Systems, 211–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24004-1_12.
Texto completoNguyen, Dai Hai, Canh Hao Nguyen y Hiroshi Mamitsuka. "Machine Learning for Metabolic Identification". En Creative Complex Systems, 329–50. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4457-3_20.
Texto completoZanone, Pier-Giorgio y Viviane Kostrubiec. "Searching for (Dynamic) Principles of Learning". En Understanding Complex Systems, 57–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39676-5_4.
Texto completoTonsberg, Terje Andreas y Jeffrey Shawn Henderson. "Some A Priori Aspects of Learning". En Understanding Complex Systems, 153–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40445-5_19.
Texto completoRogers, Eric, Chris T. Freeman, Ann-Marie Hughes, Jane H. Burridge, Katie L. Meadmore y Tim Exell. "Iterative Learning Control as an Enabler for Robotic-Assisted Upper Limb Stroke Rehabilitation". En Complex Systems, 157–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28860-4_8.
Texto completoLee, Timothy D. "Intention in Bimanual Coordination Performance and Learning". En Understanding Complex Systems, 41–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39676-5_3.
Texto completoDimirovski, G. M., A. Dourado, E. Ikonen, U. Kortela, J. Pico, B. Ribeiro, M. J. Stankovski y E. Tulunay. "Learning Control of Thermal Systems". En Control of Complex Systems, 317–37. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0349-3_14.
Texto completoActas de conferencias sobre el tema "Complex systems learning"
Burdet, G. "Geometrical methods in learning theory". En Disordered and complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1358171.
Texto completoBuhot, Arnaud. "Storage capacity of the Tilinglike Learning Algorithm". En Disordered and complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1358157.
Texto completoAxtell, Travis, Lucas A. Overbey y Lisa Woerner. "Machine learning in complex systems". En Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, editado por Tien Pham, Michael A. Kolodny y Dietrich M. Wiegmann. SPIE, 2018. http://dx.doi.org/10.1117/12.2309547.
Texto completoHamagami, Tomoki, Takashi Shibuya y Shingo Shimada. "Complex-Valued Reinforcement Learning". En 2006 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icsmc.2006.384789.
Texto completoLorscheid, Iris. "Learning Agents for Human Complex Systems". En 2014 IEEE 38th International Computer Software and Applications Conference Workshops (COMPSACW). IEEE, 2014. http://dx.doi.org/10.1109/compsacw.2014.73.
Texto completoZhang, Song-bao, Jin-cai Huang, Wei-ming Zhang y Zhong Liu. "Research on Parallel Decision Analyzing for Complex System of Systems". En 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258986.
Texto completoRajeswaran, Aravind, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov y Sergey Levine. "Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations". En Robotics: Science and Systems 2018. Robotics: Science and Systems Foundation, 2018. http://dx.doi.org/10.15607/rss.2018.xiv.049.
Texto completoOuali-Alami, Chaimae, Abdelali El Bdouri, Nisrine Elmarzouki, Ayoub Korchi y Younes Lakhrissi. "Approaches to Design Complex Software Systems". En INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010740200003101.
Texto completoJohnson, Michael y Betsy DiSalvo. "Learning about Complex Adaptive Systems in Makerspaces". En SIGCSE 2022: The 53rd ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3478432.3499243.
Texto completoDobre, Mihai S. y Alex Lascarides. "Exploiting action categories in learning complex games". En 2017 Intelligent Systems Conference (IntelliSys). IEEE, 2017. http://dx.doi.org/10.1109/intellisys.2017.8324210.
Texto completoInformes sobre el tema "Complex systems learning"
Glaser, Donald A. Hierarchical Learning of Complex Systems. Fort Belvoir, VA: Defense Technical Information Center, febrero de 1996. http://dx.doi.org/10.21236/ada312476.
Texto completoCrawford, Lara S. y S. S. Sastry. Learning Controllers for Complex Behavioral Systems. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 1996. http://dx.doi.org/10.21236/ada325516.
Texto completoFaissol, D. Learning Interactions in Complex Biological Systems. Office of Scientific and Technical Information (OSTI), octubre de 2019. http://dx.doi.org/10.2172/1573143.
Texto completoRupe, Adam. Learning Implicit Models of Complex Dynamical Systems From Partial Observations. Office of Scientific and Technical Information (OSTI), julio de 2021. http://dx.doi.org/10.2172/1808822.
Texto completoScheinberg, Katya. Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2015. http://dx.doi.org/10.21236/ada622645.
Texto completoSilberstein, Jason y Marla Spivack. Applying Systems Thinking to Education: Using the RISE Systems Framework to Diagnose Education Systems. Research on Improving Systems of Education (RISE), enero de 2023. http://dx.doi.org/10.35489/bsg-rise-ri_2023/051.
Texto completoBarjum, Daniel. PDIA for Systems Change: Tackling the Learning Crisis in Indonesia. Research on Improving Systems of Education (RISE), septiembre de 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/046.
Texto completoHayes, Anne M., Eileen Dombrowski, Allison H. Shefcyk y Jennae Bult. Learning Disabilities Screening and Evaluation Guide for Low- and Middle-Income Countries. RTI Press, abril de 2018. http://dx.doi.org/10.3768/rtipress.2018.op.0052.1804.
Texto completoKaffenberger, Michelle. The Role of Purpose in Education System Outcomes: A Conceptual Framework and Empirical Examples. Research on Improving Systems of Education (RISE), diciembre de 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/118.
Texto completoHwa, Yue-Yi y Lant Pritchett. Teacher Careers in Education Systems That Are Coherent for Learning: Choose and Curate Toward Commitment to Capable and Committed Teachers (5Cs). Research on Improving Systems of Education (RISE), diciembre de 2021. http://dx.doi.org/10.35489/bsg-rise-misc_2021/02.
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