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Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook

A crucial task in the analysis of on-line social-networking systems is to identify important people --- those linked by strong social ties --- within an individual's network neighborhood. Here we investigate this question for a particular category of strong ties, those involving spouses or romantic partners. We organize our analysis around a basic question: given all the connections among a person's friends, can you recognize his or her romantic partner from the network structure alone? Using data from a large sample of Facebook users, we find that this task can be accomplished with high accuracy, but doing so requires the development of a new measure of tie strength that we term `dispersion' --- the extent to which two people's mutual friends are not themselves well-connected. The results offer methods for identifying types of structurally significant people in on-line applications, and suggest a potential expansion of existing theories of tie strength.

 

Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook
Lars Backstrom, Jon Kleinberg

http://arxiv.org/abs/1310.6753


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How Do Agents Make Decisions?

When designing an agent-based simulation, an important question to answer is how to model the decision making processes of the agents in the system. A large number of agent decision making models can be found in the literature, each inspired by different aims and research questions. In this paper we provide a review of 14 agent decision making architectures that have attracted interest. They range from production-rule systems to psychologically- and neurologically-inspired approaches. For each of the architectures we give an overview of its design, highlight research questions that have been answered with its help and outline the reasons for the choice of the decision making model provided by the originators. Our goal is to provide guidelines about what kind of agent decision making model, with which level of simplicity or complexity, to use for which kind of research question.by Tina Balke and Nigel Gilbert


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Lorien Pratt's curator insight, November 12, 2:13 PM

For those note familiar with agent-based social simulation, the idea is to simulate the individual decisions of many "agents", each representing a person, in some situation.  For example, we might model decisions that people make as they move through a train station or amusement park, in order to design those places to best handle traffic flow. 


This is a terrific, comprehensive review article, covering a number of ways of approaching the task of modeling those individual decisions made by simulated agents.  Each is designed with somewhat different constraints in mind.  For instance, some models like SOAR, are meant to mimic certain cognitive processes.


From a Decision Intelligence point of view, these approaches are complementary.  If we want a sophisticated model of the emergent behavior of a large number of people, as one building block in a decision model for what to do about it, an agent-based simulation can be very helpful. For instance, we might be trying to decide which new medicine to launch, taking into account our competitors, our price to produce the medicine, and its impact on disease.  Part of that decision model might be based on an agent-based simulation of the decisions that people make to take the medicine, and their behavior that leads to contracting the disease. 


To continue the example, we could either use an agent-based simulation to learn from the agent-based simulation, or have it run in the same simulation as a decision model. For an example of the first, an agent-based simulation might show that the disease is expected to grow geographically according to a certain pattern. That spread pattern would then be an external input to a decision model. 


The second way to use this is to use the decision model to interactively move certain assumptions about the decision-making process, and to immediately observe the impact on agents. For instance, we may adjust the number of dollars invested in a public health campaign and observe how that impacts the emergent properties of an agent-based simulation.  There are a lot of possibilities here!



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Modeling Social Dynamics in a Collaborative Environment

Wikipedia is a prime example of today's value production in a collaborative environment. Using this example, we model the emergence, persistence and resolution of severe conflicts during collaboration by coupling opinion formation with article editing in a bounded confidence dynamics. The complex social behavior involved in editing articles is implemented as a minimal model with two basic elements; (i) individuals interact directly to share information and convince each other, and (ii) they edit a common medium to establish their own opinions. Opinions of the editors and that represented by the article are characterised by a scalar variable. When the pool of editors is fixed, three regimes can be distinguished: (a) a stable mainstream article opinion is continuously contested by editors with extremist views and there is slow convergence towards consensus, (b) the article oscillates between editors with extremist views, reaching consensus relatively fast at one of the extremes, and (c) the extremist editors are converted very fast to the mainstream opinion and the article has an erratic evolution. When editors are renewed with a certain rate, a dynamical transition occurs between different kinds of edit wars, which qualitatively reflect the dynamics of conflicts as observed in real Wikipedia data.


Modeling Social Dynamics in a Collaborative Environment

Gerardo Iñiguez, János Török, Taha Yasseri, Kimmo Kaski, János Kertész

http://arxiv.org/abs/1403.3568


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Double Percolation Phase Transition in Clustered Complex Networks

Double Percolation Phase Transition in Clustered Complex Networks | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

The percolation properties of networks are strongly affected by their topological features. A new study shows that percolation can proceed at different rates in the core and periphery of a complex, clustered network.

 

 

Double Percolation Phase Transition in Clustered Complex Networks
Pol Colomer-de-Simón and Marián Boguñá
Phys. Rev. X 4, 041020 (2014)

http://dx.doi.org/10.1103/PhysRevX.4.041020


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Economics in the age of big data

The advent of big data is already allowing for better measurement of economic effects and outcomes and is enabling novel research designs across a range of topics. Over time, these data are likely to affect the types of questions economists pose, by allowing for more focus on population variation and the analysis of a broader range of economic activities and interactions. We also expect economists to increasingly adopt the large-data statistical methods that have been developed in neighboring fields and that often may complement traditional econometric techniques.
These data opportunities also raise some important challenges. Perhaps the primary one is developing methods for researchers to access and explore data in ways that respect privacy and confidentiality concerns. This is a major issue in working with both government administrative data and private sector firms. Other challenges include developing the appropriate data management and programming capabilities, as well as designing creative and scalable approaches to summarize, describe, and analyze large-scale and relatively unstructured data sets. These challenges notwithstanding, the next few decades are likely to be a very exciting time for economic research.

 

Economics in the age of big data

Liran Einav, Jonathan Levin

 

Science 7 November 2014:
Vol. 346 no. 6210
http://dx.doi.org/10.1126/science.1243089


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A typology of street patterns

We propose a quantitative method to classify cities according to their street pattern. We use the conditional probability distribution of shape factor of blocks with a given area and define what could constitute the ‘fingerprint’ of a city. Using a simple hierarchical clustering method, these fingerprints can then serve as a basis for a typology of cities. We apply this method to a set of 131 cities in the world, and at an intermediate level of the dendrogram, we observe four large families of cities characterized by different abundances of blocks of a certain area and shape. At a lower level of the classification, we find that most European cities and American cities in our sample fall in their own sub-category, highlighting quantitatively the differences between the typical layouts of cities in both regions. We also show with the example of New York and its different boroughs, that the fingerprint of a city can be seen as the sum of the ones characterizing the different neighbourhoods inside a city. This method provides a quantitative comparison of urban street patterns, which could be helpful for a better understanding of the causes and mechanisms behind their distinct shapes.

 

A typology of street patterns
Rémi Louf, Marc Barthelemy

http://dx.doi.org/10.1098/rsif.2014.0924

J. R. Soc. Interface 6 December 2014 vol. 11 no. 101 20140924

Also at http://arxiv.org/abs/1410.2094


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EMERGENCE OF POWER-LAW AND TWO-PHASE BEHAVIOR IN FINANCIAL MARKET FLUCTUATIONS

EMERGENCE OF POWER-LAW AND TWO-PHASE BEHAVIOR IN FINANCIAL MARKET FLUCTUATIONS | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
ZVONKO KOSTANJČAR and BRANKO JEREN, Advs. Complex Syst. 16, 1350008 (2013) [12 pages] DOI: 10.1142/S0219525913500082
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Socioinformatics - The Social Impact of Interactions between Humans and IT (by Katharina Zweig et al.)

Socioinformatics is a new scientific approach to study the interactions between humans and IT. These proceedings are a collection of the contributions during a workshop of the Gesellschaft für Informatik (GI). Researchers in this emerging field discuss the main aspects of interactions between IT and humans with respect to; social connections, social changes, acceptance of IT and the social conditions affecting this acceptance, effects of IT on humans and in response changes of IT, structures of the society and the influence of IT on these structures, changes of metaphysics influenced by IT and the social context of a knowledge society.

 

 


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Can Government Be Self-Organized? A Mathematical Model of the Collective Social Organization of Ancient Teotihuacan, Central Mexico

Can Government Be Self-Organized? A Mathematical Model of the Collective Social Organization of Ancient Teotihuacan, Central Mexico | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it
PLOS ONE: an inclusive, peer-reviewed, open-access resource from the PUBLIC LIBRARY OF SCIENCE. Reports of well-performed scientific studies from all disciplines freely available to the whole world.
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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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Complexity and the Economy: W. Brian Arthur

Complexity and the Economy

~ W. Brian Arthur (author) More about this product
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Complexity and the Economy [W. Brian Arthur] on Amazon.com. *FREE* shipping on qualifying offers. 

 

Economics is changing. In the last few years it has generated a number of new approaches. One of the most promising -complexity economics - was pioneered in the 1980s and 1990s by a small team at the Santa Fe Institute. Economist and complexity theorist W. Brian Arthur led that team, and in this book he collects many of his articles on this new approach. The traditional framework sees behavior in the economy as in an equilibrium steady state. People in the economy face well-defined problems and use perfect deductive reasoning to base their actions on. The complexity framework, by contrast, sees the economy as always in process, always changing. People try to make sense of the situations they face using whatever reasoning they have at hand, and together create outcomes they must individually react to anew. The resulting economy is not a well-ordered machine, but a complex evolving system that is imperfect, perpetually constructing itself anew, and brimming with vitality. 

The new vision complements and widens the standard one, and it helps answer many questions: Why does the stock market show moods and a psychology? Why do high-tech markets tend to lock in to the dominance of one or two very large players? How do economies form, and how do they continually alter in structure over time? 

The papers collected here were among the first to use evolutionary computation, agent-based modeling, and cognitive psychology. They cover topics as disparate as how markets form out of beliefs; how technology evolves over the long span of time; why systems and bureaucracies get more complicated as they evolve; and how financial crises can be foreseen and prevented in the future.

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Global Civil Unrest: Contagion, Self-Organization, and Prediction

Global Civil Unrest: Contagion, Self-Organization, and Prediction | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of universal patterns of behavior. In contrast, the possibility that civil unrest activities, across countries and over long time periods, are governed by universal mechanisms has not been explored. Here, records of civil unrest of 170 countries during the period 1919–2008 are analyzed. It is demonstrated that the distributions of the number of unrest events per year are robustly reproduced by a nonlinear, spatially extended dynamical model, which reflects the spread of civil disorder between geographic regions connected through social and communication networks. The results also expose the similarity between global social instability and the dynamics of natural hazards and epidemics.

 

 


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Complexity, Selectivity and Asymmetry in the Conformation of the Power Phenomenon. Analysis of Chilean Society

In this work we analyzed the relationships between powerful politicians and businessmen of Chile in order to study the phenomenon of social power. We developed our study according to Complex Network Theory but also using traditional sociological theories of Power and Elites. Our analyses suggest that the studied network displays common properties of Complex Networks, such as scaling in connectivity distribution, properties of small-world networks, and modular structure, among others. We also observed that social power (a proposed metric is presented in this work) is also distributed inhomogeneously. However, the most interesting observation is that this inhomogeneous power and connectivity distribution, among other observed properties, may be the result of a dynamic and unregulated process of network growth in which powerful people tend to link to similar others. The compatibility between people, increasingly selective as the network grows, could generate the presence of extremely powerful people, but also a constant inequality of power where the difference between the most powerful is the same as among the least powerful. Our results are also in accordance with sociological theories.

 

Complexity, Selectivity and Asymmetry in the Conformation of the Power Phenomenon. Analysis of Chilean Society
Juan Pablo Cárdenas, Gerardo Vidal, Gastón Olivares

http://arxiv.org/abs/1409.7862


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On Ethical and Intellectual Failures in Contemporary Economics

Contemporary Anglo-American economics, which I admire, faces two major obstacles. First, in its drive at least since Milton Freedman to be a positive science free of normative issues, it ignores its own current intellectual foundations buried at the heart of its analysis of the “advantages of trade”: Fairness. Second, the major driver of economic growth in the past 50,000 years has been the explosion of goods and production capacities from perhaps 1,000 to 10,000 long ago, to perhaps 10 billion goods and production capacities today. Economics, lacking a theory for this explosion, deals with this explosion by ignoring it and treating it as “exogenous” to its theory.
The “Edgeworth Box” carries the heart of advantages of trade, demonstrating for properly curved isoutility curves a region where you and I are better-off trading some of my apples for some of your pears. The ratio of these in trade constitutes price. But spanning the region of advantages of trade is the famous CONTRACT CURVE, where we have exhausted all the advantages of trade. Different points on the curve correspond to different prices. But the Contract Curve is Pareto Optimal, motion on the curve can only make one of us better-off at the expense of the other. Critically, economics has NO THEORY for where we end up on the Contract Curve. Nor, since different points on the curve correspond to different prices, can PRICE settle the issue.
Using the Ultimatum Game I will show that FAIRNESS typically drives where we settle on the Contract Curve, as long as we do not have to trade with one another. Thus ethics enters economics at its foundation, yet cannot be mathematized, so is ignored in Freedman’s name of a positive science.
Perhaps more important, unlike physics, no laws entail the evolution of either the biosphere or the “econosphere.” There are no laws of motion whose integration would entail that evolution. Lacking an entailing theory of the growth of the economy in diversity, often of new goods and production capacities, economists ignore the most important feature of economic growth, wrongly treating it as “exogenous.”
The failures above are likely to play major roles in the lapse to mere greed in our major financial institutions, and in our inadequate capacities to help drive growth in much of the poverty-struck world.

 

Stuart Kauffman (2014), On Ethical and Intellectual Failures in Contemporary Economics, in Steven Horwitz , Roger Koppl (ed.) Entangled Political Economy (Advances in Austrian Economics, Volume 18) Emerald Group Publishing Limited, pp.259 - 282

 

http://dx.doi.org/10.1108/S1529-213420140000018012


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ECONOMIC NETWORKS: WHAT DO WE KNOW AND WHAT DO WE NEED TO KNOW?

ECONOMIC NETWORKS: WHAT DO WE KNOW AND WHAT DO WE NEED TO KNOW?
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The Fourth Revolution: How the infosphere is reshaping human reality (by Luciano Floridi)

The Fourth Revolution: How the infosphere is reshaping human reality

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Who are we, and how do we relate to each other? Luciano Floridi, one of the leading figures in contemporary philosophy, argues that the explosive developments in Information and Communication Technologies (ICTs) is changing the answer to these fundamental human questions.

As the boundaries between life online and offline break down, and we become seamlessly connected to each other and surrounded by smart, responsive objects, we are all becoming integrated into an "infosphere". Personas we adopt in social media, for example, feed into our 'real' lives so that we begin to live, as Floridi puts in, "onlife". Following those led by Copernicus, Darwin, and Freud, this metaphysical shift represents nothing less than a fourth revolution.

"Onlife" defines more and more of our daily activity - the way we shop, work, learn, care for our health, entertain ourselves, conduct our relationships; the way we interact with the worlds of law, finance, and politics; even the way we conduct war. In every department of life, ICTs have become environmental forces which are creating and transforming our realities. How can we ensure that we shall reap their benefits? What are the implicit risks? Are our technologies going to enable and empower us, or constrain us? Floridi argues that we must expand our ecological and ethical approach to cover both natural and man-made realities, putting the 'e' in an environmentalism that can deal successfully with the new challenges posed by our digital technologies and information society.

 

 


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Social Collective Intelligence: Combining the Powers of Humans and Machines to Build a Smarter Society (by Daniele Miorandi et al.)

The book focuses on Social Collective Intelligence, a term used to denote a class of socio-technical systems that combine, in a coordinated way, the strengths of humans, machines and collectives in terms of competences, knowledge and problem solving capabilities with the communication, computing and storage capabilities of advanced ICT.
Social Collective Intelligence opens a number of challenges for researchers in both computer science and social sciences; at the same time it provides an innovative approach to solve challenges in diverse application domains, ranging from health to education and organization of work.
The book will provide a cohesive and holistic treatment of Social Collective Intelligence, including challenges emerging in various disciplines (computer science, sociology, ethics) and opportunities for innovating in various application areas.
By going through the book the reader will gauge insight and knowledge into the challenges and opportunities provided by this new, exciting, field of investigation. Benefits for scientists will be in terms of accessing a comprehensive treatment of the open research challenges in a multidisciplinary perspective. Benefits for practitioners and applied researchers will be in terms of access to novel approaches to tackle relevant problems in their field. Benefits for policy-makers and public bodies representatives will be in terms of understanding how technological advances can support them in supporting the progress of society and economy.

 

 


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tom cockburn's curator insight, December 2, 3:54 AM

About time we arrived at a smarter humanistic culture globally

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Complexity and Dynamical Depth

Complexity and Dynamical Depth | Libros y Papers sobre  Complejidad - Sistemas Complejos | Scoop.it

We argue that a critical difference distinguishing machines from organisms and computers from brains is not complexity in a structural sense, but a difference in dynamical organization that is not well accounted for by current complexity measures. We propose a measure of the complexity of a system that is largely orthogonal to computational, information theoretic, or thermodynamic conceptions of structural complexity. What we call a system’s dynamical depth is a separate dimension of system complexity that measures the degree to which it exhibits discrete levels of nonlinear dynamical organization in which successive levels are distinguished by local entropy reduction and constraint generation. A system with greater dynamical depth than another consists of a greater number of such nested dynamical levels. Thus, a mechanical or linear thermodynamic system has less dynamical depth than an inorganic self-organized system, which has less dynamical depth than a living system. Including an assessment of dynamical depth can provide a more precise and systematic account of the fundamental difference between inorganic systems (low dynamical depth) and living systems (high dynamical depth), irrespective of the number of their parts and the causal relations between them.

 

Complexity and Dynamical Depth
Terrence Deacon and Spyridon Koutroufinis

Information 2014, 5(3), 404-423; http://dx.doi.org/10.3390/info5030404


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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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