Relevant bibliographies by topics / Modeling Complex Systems

Academic literature on the topic 'Modeling Complex Systems'

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Published: 10 December 2022
Last updated: 30 January 2023

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Journal articles on the topic "Modeling Complex Systems"

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Xepapadeas, Anastasios. "Modeling complex systems." Agricultural Economics 41 (November 2010): 181–91. http://dx.doi.org/10.1111/j.1574-0862.2010.00499.x.

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Schreckenberg, M. "Modeling Complex Systems." Journal of Physics A: Mathematical and General 37, no. 40 (September 23, 2004): 9603. http://dx.doi.org/10.1088/0305-4470/37/40/b01.

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Costanza, Robert, Lisa Wainger, and Carl Folke. "Modeling Complex Ecological Economic Systems." BioScience 43, no. 8 (September 1993): 545–55. http://dx.doi.org/10.2307/1311949.

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Kondratiev, Y. "Stochastic modeling of complex systems." Мiждисциплiнарнi дослiдження складних систем, no. 1 (2012): 9–13. http://dx.doi.org/10.31392/2307-4515/2012-1.1.

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Mirchandani, Chandru. "Resilience Modeling in Complex Systems." Procedia Computer Science 168 (2020): 232–40. http://dx.doi.org/10.1016/j.procs.2020.02.262.

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Rachdi, Nabil, Jean-Claude Fort, Thierry Klein, and Fabien Mangeant. "Modeling uncertainties in complex systems." Procedia - Social and Behavioral Sciences 2, no. 6 (2010): 7728–29. http://dx.doi.org/10.1016/j.sbspro.2010.05.200.

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Makowski, Marek, Yoshiteru Nakamori, and Hans-Jürgen Sebastian. "Advances in complex systems modeling." European Journal of Operational Research 166, no. 3 (November 2005): 593–96. http://dx.doi.org/10.1016/j.ejor.2004.07.001.

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Shlesinger, Michael F. "Book Review: Modeling Complex Systems." Journal of Statistical Physics 119, no. 3-4 (May 2005): 949–50. http://dx.doi.org/10.1007/s10955-004-2132-8.

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Filev, Dimiter. "Fuzzy modeling of complex systems." International Journal of Approximate Reasoning 5, no. 3 (May 1991): 281–90. http://dx.doi.org/10.1016/0888-613x(91)90013-c.

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Weisbuch, Gérard. "Modeling complex systems: Do it!" Complexity 11, no. 3 (January 2006): 25–26. http://dx.doi.org/10.1002/cplx.20113.

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Dissertations / Theses on the topic "Modeling Complex Systems"

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Müller, Thorsten G. "Modeling complex systems with differential equations." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10236319.

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Azevedo, Kyle Kellogg. "Modeling sustainability in complex urban transportation systems." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37289.

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This thesis proposes a framework to design and analyze sustainability within complex urban transportation systems. Urban transit systems have large variability in temporal and spatial resolution, and are common in lifecycle analyses and sustainability studies. Unlike analyses with smaller scope or broader resolution, these systems are composed of numerous interacting layers, each intricate enough to be a complete system on its own. In addition, detailed interaction with the system environment is often not accounted for in lifecycle studies, despite its strong potential effects on the problem domain. To manage such complexity, this thesis suggests a methodology that focuses on integrating existing modeling constructs in a transparent manner, and capturing structural and functional relationships for efficient model reuse. The Systems Modeling Language (OMG SysML ) is used to formally implement the modeling framework. To demonstrate the method, it is applied to a large scale multi-modal transportation network. Analysis of key network parameters such as emissions output, well-to-wheel energy use, and system capacity are presented in a case study of the Atlanta, Georgia metropolitan area. Results of the case study highlight several areas that differ from more traditional lifecycle analysis research. External influences such as regional electricity generation are found to have extremely large effects on environmental impact of a regional mobility system. The model is used to evaluate various future scenarios and finds that existing policy measures for curbing energy use and emissions are insufficient for reducing impact in a growing urban region.
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Guan, Jinyan. "Bayesian generative modeling for complex dynamical systems." Thesis, The University of Arizona, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10109036.

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This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis.

In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, We construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations.

Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples.

To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.

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Guan, Jinyan. "Bayesian Generative Modeling of Complex Dynamical Systems." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612950.

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This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis. In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, we construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations. Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples. To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.
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Valenzuela, Vega Rene Cristian. "Compact reliability and maintenance modeling of complex repairable systems." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51850.

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Maintenance models are critical for evaluation of the alternative maintenance policies for modern engineering systems. A poorly selected policy can result in excessive life-cycle costs as well as unnecessary risks for catastrophic failures of the system. Economic dependence refers to the difference between the cost of combining the maintenance of a number of components and the cost of performing the same maintenance actions individually. Maintenance that takes advantage of this difference is often called opportunistic. Large number of components and economic inter-dependence are two pervasive characteristics of modern engineering systems that make the modeling of their maintenance processes particularly challenging. Simulation is able to handle both of these characteristics computationally, but the complexity, especially from the model verification perspective, becomes overwhelming as the number of components increases. This research introduces a new procedure for maintenance models of multi-unit repairable systems with economic dependence among its components and under opportunistic maintenance policies. The procedure is based on the stochastic Petri net with aging tokens modeling framework and it makes use of a component-level model approach to overcome the state explosion of the model combined with a novel order-reduction scheme that effectively combines the impact of other components into a single distribution. The justification for the used scheme is provided, the accuracy is assessed, and applications for the systems of realistic complexity are considered.
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Le, Xuan Tuan. "Understanding complex systems through computational modeling and simulation." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEP003.

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Les approches de simulation classiques ne sont en général pas adaptées pour traiter les aspects de complexité que présentent les systèmes complexes tels que l'émergence ou l'adaptation. Dans cette thèse, l'auteur s'appuie sur ses travaux menés dans le cadre d'un projet de simulation sur l’épidémie de grippe en France associée à des interventions sur une population en considérant le phénomène étudié comme un processus diffusif sur un réseau complexe d'individus, l'originalité réside dans le fait que la population y est considérée comme un système réactif. La modélisation de tels systèmes nécessite de spécifier explicitement le comportement des individus et les réactions de ceux-cis tout en produisant un modèle informatique qui doit être à la fois flexible et réutilisable. Les diagrammes d'états sont proposés comme une approche de programmation reposant sur une modélisation validée par l'expertise. Ils correspondent également à une spécification du code informatique désormais disponibles dans les outils logiciels de programmation agent. L'approche agent de type bottom-up permet d'obtenir des simulations de scénario "what-if" où le déroulement des actions peut nécessiter que les agents s'adaptent aux changements de contexte. Cette thèse propose également l'apprentissage pour un agent par l'emploi d'arbre de décision afin d'apporter flexibilité et lisibilité pour la définition du modèle de comportement des agents et une prise de décision adaptée au cours de la simulation. Notre approche de modélisation computationnelle est complémentaire aux approches traditionnelles et peut se révéler indispensable pour garantir une approche pluridisciplinaire validable par l'expertise
Traditional approaches are not sufficient, and sometimes impossible in dealing with complexity issues such as emergence, self-organization, evolution and adaptation of complex systems. As illustrated in this thesis by the practical work of the author in a real-life project, the spreading of infectious disease as well as interventions could be considered as difusion processes on complex networks of heterogeneous individuals in a society which is considered as a reactive system. Modeling of this system requires explicitly specifying of each individual’s behaviors and (re)actions, and transforming them into computational model which has to be flexible, reusable, and ease of coding. Statechart, typical for model-based programming, is a good solution that the thesis proposes. Bottom-up agent based simulation finds emergence episodes in what-if scenarios that change rules governing agent’s behaviors that requires agents to learn to adapt with these changes. Decision tree learning is proposed to bring more flexibility and legibility in modeling of agent’s autonomous decision making during simulation runtime. Our proposition for computational models such as agent based models are complementary to traditional ones, and in some case they are unique solutions due to legal, ethical issues
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Ding, Limei. "On modeling and control of complex dynamic systems." Licentiate thesis, Luleå, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-26097.

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Nonlinear complex multi-input multi-output process is very troublesome to control. It is usually also ill-modeled. The problem of such process both in control and modeling requires a comprehensive utilization of various techniques. The thesis presents the methods for the modeling and control of complex systems. A typical example of complex processes is mineral flotation that characterized by multivariable, nonlinearities, strong interactions, stochastic disturbances, and large and variable delay times. The dynamic nonlinear model of a inverse continuous flotation process is developed based on the first order kinetics. The complexity of mineral flotation in modeling and control is shown by the analysis of the dynamic model and the relation between the process inputs and the process parameters. As an attractive alternative of conventional control technologies, fuzzy logic control is discussed for the control of an MIMO nonlinear flotation system and the simulation of the control system is carried out. LQG optimal control of the pulp level, a subsystem in a flotation process is studied and the robustness is analyzed based on the structured singular value. The control of infinite dimensional systems is known to all a knotty problem. Orthogonal collocation method, which is one of the methods of weighted residual, is employed to reduce the infinite dimensional model of the systems. In a case study a time delay system is approximated using the orthogonal collocation and a robust controller of the delay system is synthesized based on the method introduced by Glover and McFarlane in 1989. The robustness degradation caused by model reduction is discussed. The results show that the strategies studied in the thesis are available to solve the relevant modeling and control problems of complex systems.

Godkänd; 2000; 20070318 (ysko)

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Clark, Kenneth A. "Modeling single-event transients in complex digital systems." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FClark%5FKenneth%5Fphd.pdf.

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Sommerer, Christa. "Modeling complex adaptive systems and complexity for interactive art." Thesis, University of South Wales, 2002. https://pure.southwales.ac.uk/en/studentthesis/modeling-complex-adaptive-systems-and-complexity-for-interactive-art(3d7143e3-eb05-49b9-8965-0ffa53767eb9).html.

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Complex System Sciences, as a field of research, has emerged in the past decade. It studies how parts of a system give rise to the collective behaviours of the system and how the system interacts with its environment. It approaches the question of how life on earth could have appeared by searching for inherent structures in living systems and trying to define common patterns within these structures. Complex Systems are also often described as systems where the whole is more complex than the mere sum of its parts, and these systems are also considered to be at the point of maximum computational ability, maximum fitness and maximum evolvability. Several scientific models have simulated Complex Adaptive Systems. These try to model the emergence of complexity within computer-simulated environments inhabited by artificially evolving organisms. My objective in this thesis is to study the application of Complex Systems and Complex Adaptive Systems to Interactive Art and to test how one could construct interactive systems that can create dynamic and open-ended image structures that increase in complexity as users interact with them. Ideally, these interactive artworks should become comparable to Complex Adaptive Systems or even become Complex Systems themselves by satisfying some of the key properties of such systems.
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Ding, Limei. "Modeling control and analysis of complex dynamic chemical systems /." Luleå, 2003. http://epubl.luth.se/1402-1544/2003/28.

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Books on the topic "Modeling Complex Systems"

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Modeling complex systems. New York: Springer, 2004.

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Nebraska--Lincoln), Nebraska Symposium on Motivation (52nd 2007 University of. Modeling complex systems. Lincoln, [Neb.]: University of Nebraska Press, 2007.

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Modeling complex systems. 2nd ed. New York: Springer, 2010.

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Boccara, Nino. Modeling Complex Systems. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6562-2.

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Grunn, Emmanuel, and Anh Tuan Pham. Modeling of Complex Systems. West Sussex, England: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118579978.

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Rivail, Jean-Louis, Manuel Ruiz-Lopez, and Xavier Assfeld, eds. Quantum Modeling of Complex Molecular Systems. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21626-3.

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Hashimoto, Koichi, Yasuaki Oishi, and Yutaka Yamamoto, eds. Control and Modeling of Complex Systems. Boston, MA: Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0023-9.

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Siegfried, Robert. Modeling and Simulation of Complex Systems. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-07529-3.

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Haimes, Yacov Y. Modeling and Managing Interdependent Complex Systems of Systems. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119173670.

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Grigoʹevich, Ivakhnenko Alekseĭ, ed. Inductive learning algorithms for complex systems modeling. Boca Raton: CRC Press, 1994.

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Book chapters on the topic "Modeling Complex Systems"

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Mobus, George E., and Michael C. Kalton. "Systems Modeling." In Understanding Complex Systems, 645–98. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1920-8_13.

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Hester, Patrick T., and Kevin MacG Adams. "Complex Systems Modeling." In Systemic Decision Making, 101–25. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54672-8_5.

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Javarone, Marco Alberto. "Modeling Complex Systems." In SpringerBriefs in Complexity, 15–31. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70205-6_2.

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Hartley, Dean S. "Modeling Research." In Understanding Complex Systems, 39–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51935-7_3.

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Frank, Till D. "Modeling Interventions." In Understanding Complex Systems, 217–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97178-6_8.

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Liu, Sifeng, and Yi Lin. "Grey Systems Modeling." In Understanding Complex Systems, 107–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16158-2_4.

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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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Helbing, Dirk. "Agent-Based Modeling." In Understanding Complex Systems, 25–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24004-1_2.

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Hartley, Dean S. "Modeling Unconventional Conflict." In Understanding Complex Systems, 171–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51935-7_7.

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Elmqvist, H., S. E. Mattsson, M. Otter, and K. J. Åström. "Modeling Complex Physical Systems." In Control of Complex Systems, 21–38. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0349-3_2.

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Conference papers on the topic "Modeling Complex Systems"

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Amit, Daniel J. "Modeling active memory: Experiment, theory and simulation." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386815.

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Campa, A. "Traveling bubbles in a model of DNA." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386846.

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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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Szabó, György. "On the role of external constraints in a spatially extended evolutionary prisoner’s dilemma game." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386817.

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Tella, J. L. "A model for predation pressure in colonial birds." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386818.

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Thurner, Stefan. "A dynamical thermostat approach to financial asset price dynamics." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386819.

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Ito, H. M. "Introduction to mathematical modeling of earthquakes." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386820.

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Marchetti, R. "The fractal properties of internet." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386821.

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Gabrielli, A. "Etching of random solids: Hardening dynamics and self-organized fractality." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386822.

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Ivanov, Plamen Ch. "Generating power-law tails in probability distributions." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386823.

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Reports on the topic "Modeling Complex Systems"

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Aceves, Alejandro, and Todd Kapitula. Modeling Complex Nonlinear Optical Systems. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada459336.

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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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Werner, Brad. Modeling Nearshore Processes as Complex Systems. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada416942.

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Cooper, Curtis S., Aaron L. Bramson, and Arlo L. Ames. Intrinsic Uncertainties in Modeling Complex Systems. Office of Scientific and Technical Information (OSTI), September 2014. http://dx.doi.org/10.2172/1156599.

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Mitter, Sanjoy K. Environments for Modeling and Simulation of Complex Systems. Fort Belvoir, VA: Defense Technical Information Center, June 1998. http://dx.doi.org/10.21236/ada394745.

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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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Soloviev, Volodymyr Mykolayovych, and Viktoriya Volodymyrivna Solovyova. Universal tools of modeling different nature complex systems. ФОП Однорог Т.В., 2018. http://dx.doi.org/10.31812/123456789/2865.

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It is shown that there is а powerful set of tools for the study of self-organization in complex systems, both natural and artificial origin. They characterize the multidimensional nature of complexity - multifractality, irreversibility, non-linearity, recurrence, nonstability, emeregence, etc., and quantitative evaluation of individual dynamical measures of complexity allows for monitoring, predicting and preventing unwanted critical or crisis. Particular attention is paid to measures of network complexity, which are fully applicable to build synergistic network of pedagogical systems.
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8

Beck, Thomas, Nimal Wijesekera, David Rogers, and Roman Petrenko. Multiscale Modeling of Complex Systems Conformational Transitions in Proteins. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada482296.

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9

Saptsin, Vladimir, and Володимир Миколайович Соловйов. Relativistic quantum econophysics – new paradigms in complex systems modelling. [б.в.], July 2009. http://dx.doi.org/10.31812/0564/1134.

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Abstract:
This work deals with the new, relativistic direction in quantum econophysics, within the bounds of which a change of the classical paradigms in mathematical modelling of socio-economic system is offered. Classical physics proceeds from the hypothesis that immediate values of all the physical quantities, characterizing system’s state, exist and can be accurately measured in principle. Non-relativistic quantum mechanics does not reject the existence of the immediate values of the classical physical quantities, nevertheless not each of them can be simultaneously measured (the uncertainty principle). Relativistic quantum mechanics rejects the existence of the immediate values of any physical quantity in principle, and consequently the notion of the system state, including the notion of the wave function, which becomes rigorously nondefinable. The task of this work consists in econophysical analysis of the conceptual fundamentals and mathematical apparatus of the classical physics, relativity theory, non-relativistic and relativistic quantum mechanics, subject to the historical, psychological and philosophical aspects and modern state of the socio-economic modeling problem. We have shown that actually and, virtually, a long time ago, new paradigms of modeling were accepted in the quantum theory, within the bounds of which the notion of the physical quantity operator becomes the primary fundamental conception(operator is a mathematical image of the procedure, the action), description of the system dynamics becomes discrete and approximate in its essence, prediction of the future, even in the rough, is actually impossible when setting aside the aftereffect i.e. the memory. In consideration of the analysis conducted in the work we suggest new paradigms of the economical-mathematical modeling.
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10

Mayo, Jackson R., and Robert C. Armstrong. Notes on "Modeling, simulation and analysis of complex networked systems". Office of Scientific and Technical Information (OSTI), March 2009. http://dx.doi.org/10.2172/984134.

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