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Complexity and the Arrow of Time (by Charles H. Lineweaver, Paul C. W. Davies, Michael Ruse)

There is a widespread assumption that the universe in general, and life in particular, is 'getting more complex with time'. This book brings together a wide range of experts in science, philosophy and theology and unveils their joint effort in exploring this idea. They confront essential problems behind the theory of complexity and the role of life within it: what is complexity? When does it increase, and why? Is the universe evolving towards states of ever greater complexity and diversity? If so, what is the source of this universal enrichment? This book addresses those difficult questions, and offers a unique cross-disciplinary perspective on some of the most profound issues at the heart of science and philosophy. Readers will gain insights in complexity that reach deep into key areas of physics, biology, complexity science, philosophy and religion.

 

 


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Exact solution for a metapopulation version of Schelling’s model

Exact solution for a metapopulation version of Schelling’s model | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
More than 40 y ago, Schelling introduced one of the first agent-based models in the social sciences. The model showed that even if people only have a mild preference for living with neighbors of the same color, complete segregation will occur. This model has been much discussed by social scientists and analyzed by physicists using analogies with spin-1 Ising models and other systems. Here, we study the metapopulation version of the model, which mimics the division of a city into neighborhoods, and we present the first analysis to our knowledge that gives detailed information about the structure of equilibria and explicit formulas for their densities.

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Link Prediction in Complex Networks: A Mutual Information Perspective

Link Prediction in Complex Networks: A Mutual Information Perspective | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.


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How structurally stable are global socioeconomic systems?

The stability analysis of socioeconomic systems has been centred on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. Therefore, a key stability question that has been under-investigated is how strongly the conditions of a system itself can change before the system moves to a qualitatively different behaviour, i.e. how structurally stable the systems is. Here, we introduce a framework to investigate the structural stability of socioeconomic systems formed by a network of interactions among agents competing for resources. We measure the structural stability of the system as the range of conditions in the distribution and availability of resources compatible with the qualitative behaviour in which all the constituent agents can be self-sustained across time. To illustrate our framework, we study an empirical representation of the global socioeconomic system formed by countries sharing and competing for multinational companies used as proxy for resources. We demonstrate that the structural stability of the system is inversely associated with the level of competition and the level of heterogeneity in the distribution of resources. Importantly, we show that the qualitative behaviour of the observed global socioeconomic system is highly sensitive to changes in the distribution of resources. We believe that this work provides a methodological basis to develop sustainable strategies for socioeconomic systems subject to constantly changing conditions.

 

How structurally stable are global socioeconomic systems?
Serguei Saavedra, Rudolf P. Rohr, Luis J. Gilarranz, Jordi Bascompte

http://dx.doi.org/10.1098/ rsif.2014.0693
J. R. Soc. Interface 6 November 2014 vol. 11 no. 100 20140693


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Eli Levine's curator insight, September 14, 5:18 PM

There are most likely a plurality of stable socio-economic systems with different dynamics and levels of short term system stability.  It's likely that, even if there are periods of short term instability, that long term stability will hold, even if instability is a stable feature. 

 

Very interesting points here. 

 

Enjoy! 

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Modeling social-ecological problems in coastal ecosystems: A case study - Forrester - 2014 - Complexity - Wiley Online Library

Modeling social-ecological problems in coastal ecosystems: A case study - Forrester - 2014 - Complexity - Wiley Online Library | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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Review of Watts, Christopher and Gilbert, Nigel: Simulating Innovation: Computer-Based Tools for Rethinking Innovation

Review of Watts, Christopher and Gilbert, Nigel: Simulating Innovation: Computer-Based Tools for Rethinking Innovation | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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The Time Scale of Evolutionary Innovation

The Time Scale of Evolutionary Innovation | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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CS-DC Newsletter #1 - September 201

CS-DC Newsletter #1 - September 201 | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

Contents


* Editorial by Paul Bourgine
* Call for new CS-DC e-Laboratories and e-Departments
* Meeting of the UNESCO UniTwin CS-DC at ECCS’14: Science, Policy, and Applications
* International workshop on Contagion Dynamics in Socio economic Systems
* Tools: CS DC web conferencing tool
* News from the CS-DC e-Laboratories
* e-Laboratory on Education
* e-Laboratory on Climate System / Human System Interaction
* e-Laboratory on Human-trace
* e-Laboratory on Situated Collective Intelligence


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Conditions for the Emergence of Shared Norms in Populations with Incompatible Preferences

Understanding norms is a key challenge in sociology. Nevertheless, there is a lack of dynamical models explaining how one of several possible behaviors is established as a norm and under what conditions. Analysing an agent-based model, we identify interesting parameter dependencies that imply when two behaviors will coexist or when a shared norm will emerge in a heterogeneous society, where different populations have incompatible preferences. Our model highlights the importance of randomness, spatial interactions, non-linear dynamics, and self-organization. It can also explain the emergence of unpopular norms that do not maximize the collective benefit. Furthermore, we compare behavior-based with preference-based punishment and find interesting results concerning hypocritical punishment. Strikingly, pressuring others to perform the same public behavior as oneself is more effective in promoting norms than pressuring others to meet one’s own private preference. Finally, we show that adaptive group pressure exerted by randomly occuring, local majorities may create norms under conditions where different behaviors would normally coexist.

 

Helbing D, Yu W, Opp K-D, Rauhut H (2014) Conditions for the Emergence of Shared Norms in Populations with Incompatible Preferences. PLoS ONE 9(8): e104207. http://dx.doi.org/10.1371/journal.pone.0104207


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JIDT: An information-theoretic toolkit for studying the dynamics of complex systems

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave and Python. We present the principles behind the code design, and provide several examples to guide users

 

"JIDT: An information-theoretic toolkit for studying the dynamics of complex systems"
Joseph T. Lizier, arXiv:1408.3270, 2014
http://arxiv.org/abs/1408.3270


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Eli Levine's curator insight, August 19, 11:11 AM

This could be useful.

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Social Dynamics (by Brian Skyrms)

Social Dynamics

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Brian Skyrms presents eighteen essays which apply adaptive dynamics (of cultural evolution and individual learning) to social theory. Altruism, spite, fairness, trust, division of labor, and signaling are treated from this perspective. Correlation is seen to be of fundamental importance. Interactions with neighbors in space, on static networks, and on co-evolving dynamics networks are investigated. Spontaneous emergence of social structure and of signaling systems are examined in the context of learning dynamics.

 

 


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Complexity at the social science interface

This article introduces a special issue of Complexity dedicated to the increasingly important element of complexity science that engages with social policy. We introduce and frame an emerging research agenda that seeks to enhance social policy by working at the interface between the social sciences and the physical sciences (including mathematics and computer science), and term this research area the “social science interface” by analogy with research at the life sciences interface. We locate and exemplify the contribution of complexity science at this new interface before summarizing the contributions collected in this special issue and identifying some common themes that run through them.

 

Complexity at the social science interface
Nigel Gilbert and Seth Bullock

Complexity
Volume 19, Issue 6, pages 1–4, July/August 2014

http://dx.doi.org/10.1002/cplx.21550

 

Special Issue on Complexity Science and Social Policy

http://onlinelibrary.wiley.com/doi/10.1002/cplx.v19.6/issuetoc


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The Collective Intelligence Handbook

Thomas W. Malone and Michael S. Bernstein (Editors)

Collective intelligence has existed at least as long as humans have, because families, armies, countries, and companies have all--at least sometimes--acted collectively in ways that seem intelligent. But in the last decade or so a new kind of collective intelligence has emerged: groups of people and computers, connected by the Internet, collectively doing intelligent things. In order to understand the possibilities and constraints of these new kinds of intelligence, a new interdisciplinary field is emerging.


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Coevolution can reverse predator–prey cycles

The abundances of predators and their prey can oscillate in time. Mathematical theory of predator–prey systems predicts that in predator–prey cycles, peaks in prey abundance precede peaks in predator abundance. However, these models do not consider how the evolution of predator and prey traits related to offense and defense will affect the ordering and timing of peaks. Here we show that predator–prey coevolution can effectively reverse the ordering of peaks in predator–prey cycles, i.e., peaks in predator abundance precede peaks in prey abundance. We present examples from three distinct systems that exhibit reversed cycles, suggesting that coevolution may be an important driver of cycles in those systems.

 

http://dx.doi.org/10.1073/pnas.1317693111
Coevolution can reverse predator–prey cycles

Michael H. Cortez and Joshua S. Weitz

PNAS vol. 111 no. 20, pp. 7486–7491





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Introduction to Computational Social Science, by Claudio Cioffi-Revilla

Introduction to Computational Social Science, by Claudio Cioffi-Revilla | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies.

This reader-friendly textbook/reference is the first work of its kind to provide a comprehensive and unified Introduction to Computational Social Science. Four distinct methodological approaches are examined in particular detail, namely automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. The coverage of each of these approaches is supported by a discussion of the historical context and motivations, as well as by a list of recommended texts for further reading.


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War: Origins and Effects

The International System is a self-organized system and shows emergent behavior. During the timeframe (1495 - 1945), a finite-time singularity and four accompanying accelerating log-periodic cycles shaped the dynamics of the International System. The accelerated growth of the connectivity of the regulatory network of the International System, in combination with its anarchistic structure, produce and shape the war dynamics of the system. Accelerated growth of the connectivity of the International system is fed by population growth and the need for social systems to fulfill basic requirements. The finite-time singularity and accompanying log-periodic oscillations were instrumental in the periodic reorganization of the regulatory network of the International System, and contributed to a long-term process of social expansion and integration in Europa. The singularity dynamic produced a series of organizational innovations. At the critical time of the singularity (1939) the connectivity of the system reached a critical threshold, resulting in a critical transition. This critical transition caused a fundamental reorganization of the International System: Europe transformed from an anarchistic system to cooperative security community. This critical transition also marks the actual globalization of the International System. During the life span of cycles, the war dynamics show chaotic characteristics. Various early-warning signals can be identified, and can probably be used in the current International System. These findings have implications for the social sciences and historical research.

 

War: Origins and Effects
Ingo Piepers

http://arxiv.org/abs/1409.6163


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Eli Levine's curator insight, September 26, 11:31 AM

Thus we delve closer into the hidden language of our social world.

 

Way cool science!

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Modeling sustainability transitions on complex networks - Tran - 2014 - Complexity

Modeling sustainability transitions on complex networks - Tran - 2014 - Complexity | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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Review of Batty, Michael: The New Science of Cities

Review of Batty, Michael: The New Science of Cities | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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Modelling Society's Evolutionary Forces

Modelling Society's Evolutionary Forces | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
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Complex Networks: An Algorithmic Perspective (by Kayhan Erciyes)

Complex Networks: An Algorithmic Perspective

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Network science is a rapidly emerging field of study that encompasses mathematics, computer science, physics, and engineering. A key issue in the study of complex networks is to understand the collective behavior of the various elements of these networks.

Although the results from graph theory have proven to be powerful in investigating the structures of complex networks, few books focus on the algorithmic aspects of complex network analysis. Filling this need, Complex Networks: An Algorithmic Perspective supplies the basic theoretical algorithmic and graph theoretic knowledge needed by every researcher and student of complex networks.

This book is about specifying, classifying, designing, and implementing mostly sequential and also parallel and distributed algorithms that can be used to analyze the static properties of complex networks. Providing a focused scope which consists of graph theory and algorithms for complex networks, the book identifies and describes a repertoire of algorithms that may be useful for any complex network.

 

Provides the basic background in terms of graph theorySupplies a survey of the key algorithms for the analysis of complex networksPresents case studies of complex networks that illustrate the implementation of algorithms in real-world networks, including protein interaction networks, social networks, and computer networks

Requiring only a basic discrete mathematics and algorithms background, the book supplies guidance that is accessible to beginning researchers and students with little background in complex networks. To help beginners in the field, most of the algorithms are provided in ready-to-be-executed form.

 

 


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Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System

Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

Social systems have recently attracted much attention, with attempts to understand social behavior with the aid of statistical mechanics applied to complex systems. Collective properties of such systems emerge from couplings between components, for example, individual persons, transportation nodes such as airports or subway stations, and administrative districts. Among various collective properties, criticality is known as a characteristic property of a complex system, which helps the systems to respond flexibly to external perturbations. This work considers the criticality of the urban transportation system entailed in the massive smart card data on the Seoul transportation network. Analyzing the passenger flow on the Seoul bus system during one week, we find explicit power-law correlations in the system, that is, power-law behavior of the strength correlation function of bus stops and verify scale invariance of the strength fluctuations. Such criticality is probed by means of the scaling and renormalization analysis of the modified gravity model applied to the system. Here a group of nearby (bare) bus stops are transformed into a (renormalized) “block stop” and the scaling relations of the network density turn out to be closely related to the fractal dimensions of the system, revealing the underlying structure. Specifically, the resulting renormalized values of the gravity exponent and of the Hill coefficient give a good description of the Seoul bus system: The former measures the characteristic dimensionality of the network whereas the latter reflects the coupling between distinct transportation modes. It is thus demonstrated that such ideas of physics as scaling and renormalization can be applied successfully to social phenomena exemplified by the passenger flow.

 

Emergence of Criticality in the Transportation Passenger Flow: Scaling and Renormalization in the Seoul Bus System

Goh S, Lee K, Choi M, Fortin J-Y 

PLoS ONE 9(3): e89980. (2014) 

http://dx.doi.org/10.1371/journal.pone.0089980


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Complexity and the Human Experience: Modeling Complexity in the Humanities and Social Sciences (edited by Paul A. Youngman & Mirsad Hadzikadic)

Questions of values, ontologies, ethics, aesthetics, discourse, origins, language, literature, and meaning do not lend themselves readily, or traditionally, to equations, probabilities, and models. However, with the increased adoption of natural science tools in economics, anthropology, and political science—to name only a few social scientific fields highlighted in this volume—quantitative methods in the humanities are becoming more common.

The theory of complexity holds significant promise for better understanding social and human phenomena based on interactions among the participating "agents," whatever they may be: a thought, a person, a conversation, a sentence, or an email. Such systems can exhibit phase transitions, feedback loops, self-organization, and emergent properties. These dynamic systems lend themselves naturally to the kind of analysis made possible by models and simulations developed with complex science tools. This volume offers a tour of quantitative analyses, models, and simulations of humanities and social science phenomena that have been historically the purview of qualitative methods.

 

 


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Information Entropy-Based Metrics for Measuring Emergences in Artificial Societies

Emergence is a common phenomenon, and it is also a general and important concept in complex dynamic systems like artificial societies. Usually, artificial societies are used for assisting in resolving several complex social issues (e.g., emergency management, intelligent transportation system) with the aid of computer science. The levels of an emergence may have an effect on decisions making, and the occurrence and degree of an emergence are generally perceived by human observers. However, due to the ambiguity and inaccuracy of human observers, to propose a quantitative method to measure emergences in artificial societies is a meaningful and challenging task. This article mainly concentrates upon three kinds of emergences in artificial societies, including emergence of attribution, emergence of behavior, and emergence of structure. Based on information entropy, three metrics have been proposed to measure emergences in a quantitative way. Meanwhile, the correctness of these metrics has been verified through three case studies (the spread of an infectious influenza, a dynamic microblog network, and a flock of birds) with several experimental simulations on the Netlogo platform. These experimental results confirm that these metrics increase with the rising degree of emergences. In addition, this article also has discussed the limitations and extended applications of these metrics.

 

Information Entropy-Based Metrics for Measuring Emergences in Artificial Societies
Mingsheng Tang  and Xinjun Mao

Entropy 2014, 16, 4583-4602; http://dx.doi.org/10.3390/e16084583


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Connecting Core Percolation and Controllability of Complex Networks

Connecting Core Percolation and Controllability of Complex Networks | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.

 

Connecting Core Percolation and Controllability of Complex Networks
• Tao Jia & Márton Pósfai

Scientific Reports 4, Article number: 5379 http://dx.doi.org/10.1038/srep05379


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Sibout Nooteboom's curator insight, July 13, 3:52 AM

Fascinating advances

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Searching for superspreaders of information in real-world social media

Searching for superspreaders of information in real-world social media | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for viral information dissemination in relevant applications.


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Quantitative comparison between crowd models for evacuation planning and evaluation

Crowd simulation is rapidly becoming a standard tool for evacuation planning and evaluation. However, the many crowd models in the literature are structurally different, and few have been rigorously calibrated against real-world egress data, especially in emergency situations. In this paper we describe a procedure to quantitatively compare different crowd models or between models and real-world data. We simulated three models: (1) the lattice gas model, (2) the social force model, and (3) the RVO2 model, and obtained the distributions of six observables: (1) evacuation time, (2) zoned evacuation time, (3) passage density, (4) total distance traveled, (5) inconvenience, and (6) flow rate. We then used the DISTATIS procedure to compute the compromise matrix of statistical distances between the three models. Projecting the three models onto the first two principal components of the compromise matrix, we find the lattice gas and RVO2 models are similar in terms of the evacuation time, passage density, and flow rates, whereas the social force and RVO2 models are similar in terms of the total distance traveled. Most importantly, we find that the zoned evacuation times of the three models to be very different from each other. Thus we propose to use this variable, if it can be measured, as the key test between different models, and also between models and the real world. Finally, we compared the model flow rates against the flow rate of an emergency evacuation during the May 2008 Sichuan earthquake, and found the social force model agrees best with this real data.

http://dx.doi.org/10.1140/epjb/e2014-40699-x

Quantitative comparison between crowd models for evacuation planning and evaluation V Viswanathan, CE Lee, MH Lees, SA Cheong, PMA Sloot
The European Physical Journal B, February 2014, 87:27


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