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A Multi-Level Geographical Study of Italian Political Elections from Twitter Data

A Multi-Level Geographical Study of Italian Political Elections from Twitter Data | Edgar Analytics & Complex Systems | Scoop.it

In this paper we present an analysis of the behavior of Italian Twitter users during national political elections. We monitor the volumes of the tweets related to the leaders of the various political parties and we compare them to the elections results. Furthermore, we study the topics that are associated with the co-occurrence of two politicians in the same tweet. We cannot conclude, from a simple statistical analysis of tweet volume and their time evolution, that it is possible to precisely predict the election outcome (or at least not in our case of study that was characterized by a “too-close-to-call” scenario). On the other hand, we found that the volume of tweets and their change in time provide a very good proxy of the final results. We present this analysis both at a national level and at smaller levels, ranging from the regions composing the country to macro-areas (North, Center, South).

 

http://dx.doi.org/10.1371/journal.pone.0095809

Multi-Level Geographical Study of Italian Political Elections from Twitter Data

Caldarelli G, Chessa A, Pammolli F, Pompa G, Puliga M, et al.

PLoS ONE 9(5): e95809 (2014)


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The future cities agenda

Suddenly, ‘cities’ have become the hottest topic on the planet. National research institutes and local governments as well as various global agencies are all scrambling to get a piece of the action as cities become the places where it is considered future economic prosperity firmly lies while also offering the prospect of rescuing a developed world mired in recession.

 

Batty M, 2013, "The future cities agenda" Environment and Planning B: Planning and Design 40(2) 191 – 194 

http://dx.doi.org/10.1068/b4002ed


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Logistic Growth and Ergodic Properties of Urban Forms

Urban morphology has presented significant intellectual challenges to mathematicians and physicists ever since the eighteenth century, when Euler first explored the famous Konigsberg bridges problem. Many important regularities and allometries have been observed in urban studies, including Zipf's law and Gibrat's law, rendering cities attractive systems for analysis within statistical physics. Nevertheless, a broad consensus on how cities and their boundaries are defined is still lacking. Applying percolation theory to the street intersection space, we show that growth curves for the maximum cluster size of the largest cities in the UK and in California collapse to a single curve, namely the logistic. Subsequently, by introducing the concept of the condensation threshold, we show that natural boundaries of cities can be well defined in a universal way. This allows us to study and discuss systematically some of the allometries that are present in cities, thus casting light on the concept of ergodicity as related to urban street networks.

 

Logistic Growth and Ergodic Properties of Urban Forms
A. Paolo Masucci, Elsa Arcaute, Jiaqiu Wang, Erez Hatna, Kiril Stanilov, Michael Batty

http://arxiv.org/abs/1504.07380


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Evolutionary games on multilayer networks: A colloquium

Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling through the utilities of players, through the flow of information, as well as through the popularity of different strategies on different network layers. The colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.

 

Evolutionary games on multilayer networks: A colloquium
Zhen Wang, Lin Wang, Attila Szolnoki, Matjaz Perc

http://arxiv.org/abs/1504.04359


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Information-Theoretic Inference of Common Ancestors

A directed acyclic graph (DAG) partially represents the conditional independence structure among observations of a system if the local Markov condition holds, that is if every variable is independent of its non-descendants given its parents. In general, there is a whole class of DAGs that represents a given set of conditional independence relations. We are interested in properties of this class that can be derived from observations of a subsystem only. To this end, we prove an information-theoretic inequality that allows for the inference of common ancestors of observed parts in any DAG representing some unknown larger system. More explicitly, we show that a large amount of dependence in terms of mutual information among the observations implies the existence of a common ancestor that distributes this information. Within the causal interpretation of DAGs, our result can be seen as a quantitative extension of Reichenbach’s principle of common cause to more than two variables. Our conclusions are valid also for non-probabilistic observations, such as binary strings, since we state the proof for an axiomatized notion of “mutual information” that includes the stochastic as well as the algorithmic version.

 

Information-Theoretic Inference of Common Ancestors
Bastian Steudel and Nihat Ay

Entropy 2015, 17(4), 2304-2327; http://dx.doi.org/10.3390/e17042304 ;


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25 Years of Self-Organized Criticality: Concepts and Controversies

Introduced by the late Per Bak and his colleagues, self-organized criticality (SOC) has been one of the most stimulating concepts to come out of statistical mechanics and condensed matter theory in the last few decades, and has played a significant role in the development of complexity science. SOC, and more generally fractals and power laws, have attacted much comment, ranging from the very positive to the polemical. The other papers in this special issue (Aschwanden et al, 2014; McAteer et al, 2014; Sharma et al, 2015) showcase the considerable body of observations in solar, magnetospheric and fusion plasma inspired by the SOC idea, and expose the fertile role the new paradigm has played in approaches to modeling and understanding multiscale plasma instabilities. This very broad impact, and the necessary process of adapting a scientific hypothesis to the conditions of a given physical system, has meant that SOC as studied in these fields has sometimes differed significantly from the definition originally given by its creators. In Bak's own field of theoretical physics there are significant observational and theoretical open questions, even 25 years on (Pruessner, 2012). One aim of the present review is to address the dichotomy between the great reception SOC has received in some areas, and its shortcomings, as they became manifest in the controversies it triggered. Our article tries to clear up what we think are misunderstandings of SOC in fields more remote from its origins in statistical mechanics, condensed matter and dynamical systems by revisiting Bak, Tang and Wiesenfeld's original papers.

 

25 Years of Self-Organized Criticality: Concepts and Controversies
Nicholas Watkins, Gunnar Pruessner, Sandra Chapman, Norma Bock Crosby, Henrik Jensen

http://arxiv.org/abs/1504.04991


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Why You Shouldn't Try to Be Everywhere in Social Media

Why You Shouldn't Try to Be Everywhere in Social Media | Edgar Analytics & Complex Systems | Scoop.it
Jason Keath, Founder & CEO of Social Fresh, drops by the Content Pros Podcast today to discuss content via conference presentations and the advantages of keeping things simple.

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donhornsby's curator insight, April 28, 7:35 AM

(From the article): Don’t try to put content out on all social channels; it won’t do you any good. (highlight to tweet) Look at your community—everyone you do business with in one way or another. Where are they? If they aren’t on a particular social network, don’t spend your time and energy there.

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Evolutionary games on multilayer networks: A colloquium

Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling through the utilities of players, through the flow of information, as well as through the popularity of different strategies on different network layers. The colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.

 

Evolutionary games on multilayer networks: A colloquium
Zhen Wang, Lin Wang, Attila Szolnoki, Matjaz Perc

http://arxiv.org/abs/1504.04359


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Nonlinear Dynamics of Financial Crises: How to Predict Discontinuous Decisions (by Ionut Purica)

When just a handful of economists predicted the 2008 financial crisis, people should wonder how so many well educated people with enormous datasets and computing power can be so wrong. In this short book Ionut Purica joins a growing number of economists who explore the failings of mainstream economics and propose solutions developed in other disciplines, such as sociology and evolutionary biology. While it might be premature to call for a revolution, Dr. Purica echoes John Maynard Keynes in believing that economic ideas are "dangerous for good or evil." In recent years evil seems to have had the upper hand. "Nonlinear Dynamics of Financial Crises" points to their ability to do good.

Makes complex economics ideas accessible by carefully explaining technical terms and minimizing mathematics and equationsDelivers easily-understood perspectives about the global economy by constructing broad assumptions and conclusions in the face of its infinitely complexityChallenges received economic ideas by focusing on human behavior and the roles it plays in easily-observable recent trends and events

 

 


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Structural Determinants of Criticality in Biological Networks

Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behaviour in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organisation can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system towards criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality.

 

Valverde S, Ohse S, Turalska M, Garcia-Ojalvo J and West BJ (2015). Structural Determinants of Criticality in Biological Networks. Front. Physiol. 6:127. http://dx.doi.org/10.3389/fphys.2015.00127 ;


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Computational Models of Consumer Confidence from Large-Scale Online Attention Data: Crowd-Sourcing Econometrics

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

 

Dong X, Bollen J (2015) Computational Models of Consumer Confidence from Large-Scale Online Attention Data: Crowd-Sourcing Econometrics. PLoS ONE 10(3): e0120039. http://dx.doi.org/10.1371/journal.pone.0120039 ;


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Responding to Complexity in Socio-­Economic Systems: How to Build a Smart and Resilient Society?

The world is changing at an ever-increasing pace. And it has changed in a much more fundamental way than one would think, primarily because it has become more connected and interdependent than in our entire history. Every new product, every new invention can be combined with those that existed before, thereby creating an explosion of complexity: structural complexity, dynamic complexity, functional complexity, and algorithmic complexity. How to respond to this challenge?

 

Responding to Complexity in Socio-­Economic Systems: How to Build a Smart and Resilient Society?

Dirk Helbing

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2583391


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Requisite Variety, Autopoiesis, and Self-organization

Ashby's law of requisite variety states that a controller must have at least as much variety (complexity) as the controlled. Maturana and Varela proposed autopoiesis (self-production) to define living systems. Living systems also require to fulfill the law of requisite variety. A measure of autopoiesis has been proposed as the ratio between the complexity of a system and the complexity of its environment. Self-organization can be used as a concept to guide the design of systems towards higher values of autopoiesis, with the potential of making technology more "living", i.e. adaptive and robust.

 

Requisite Variety, Autopoiesis, and Self-organization
Carlos Gershenson

http://arxiv.org/abs/1409.7475


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Constructing a Continuous Futures Series From Quandl

Constructing a Continuous Futures Series From Quandl | Edgar Analytics & Complex Systems | Scoop.it
(This article was first published on Revolutions, and kindly contributed to R-bloggers)
by Ilya Kipnis
In this post, I will demonstrate how to obtain, stitch together, and clean data for backtesting using futures data from Quandl.

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How a well-adapted immune system is organized

The adaptive immune system uses the experience of past infections to prepare its limited repertoire of specialized receptors to protect organisms from future threats. What is the best way of doing this? Building a theoretical framework from first principles, we predict the composition of receptor repertoires that are optimally adapted to minimize the cost of infections from a given pathogenic environment. A naive repertoire can reach these optima through a biologically plausible competitive mechanism. Our findings explain how limited populations of immune receptors can self-organize to provide effective immunity against highly diverse pathogens. Our results also inform the design and interpretation of experiments surveying immune repertoires.

 

How a well-adapted immune system is organized
Andreas Mayer, Vijay Balasubramanian, Thierry Mora, and Aleksandra M. Walczak

http://dx.doi.org/10.1073/pnas.1421827112 ;


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Neural Computations Mediating One-Shot Learning in the Human Brain

There are at least two distinct learning strategies for identifying the relationship between a cause and its consequence: (1) incremental learning, in which we gradually acquire knowledge through trial and error, and (2) one-shot learning, in which we rapidly learn from only a single pairing of a potential cause and a consequence. Little is known about how the brain switches between these two forms of learning. In this study, we provide evidence that the amount of uncertainty about the relationship between cause and consequence mediates the transition between incremental and one-shot learning. Specifically, the more uncertainty there is about the causal relationship, the higher the learning rate that is assigned to that stimulus. By imaging the brain while participants were performing the learning task, we also found that uncertainty about the causal association is encoded in the ventrolateral prefrontal cortex and that the degree of coupling between this region and the hippocampus increases during one-shot learning. We speculate that this prefrontal region may act as a “switch,” turning on and off one-shot learning as required.

 

Lee SW, O’Doherty JP, Shimojo S (2015) Neural Computations Mediating One-Shot Learning in the Human Brain. PLoS Biol 13(4): e1002137. http://dx.doi.org/10.1371/journal.pbio.1002137 ;


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When Money Learns to Fly: Towards Sensing as a Service Applications Using Bitcoin

Sensing-as-a-Service (S2aaS) is an emerging Internet of Things (IOT) business model pattern. To be technically feasible and to effectively allow for broad adoption, S2aaS implementations have to overcome manifold systemic hurdles, specifically regarding payment and sensor identification. In an effort to overcome these hurdles, we propose Bitcoin as protocol for S2aaS networks. To lay the groundwork and start the conversation about disruptive changes that Bitcoin technology could bring to S2aaS concepts and IOT in general, we identify and discuss the core characteristics that could drive those changes. We present a conceptual example and describe the basic process of exchanging data for cash using Bitcoin.

 

When Money Learns to Fly: Towards Sensing as a Service Applications Using Bitcoin
Kay Noyen, Dirk Volland, Dominic Wörner, Elgar Fleisch

http://arxiv.org/abs/1409.5841


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Ants Swarm Like Brains Think

Ants Swarm Like Brains Think | Edgar Analytics & Complex Systems | Scoop.it
“As I watched films of these ant colonies, it looked like what was happening at the synapse of neurons. Both of these systems accumulate evidence about their inputs—returning ants or incoming voltage pulses—to make their decisions about whether to generate an output—an outgoing forager or a packet of neurotransmitter,” Goldman said. On his next trip to Stanford, he extended his stay. An unusual research collaboration had begun to coalesce: Ants would be used to study the brain, and the brain, to study ants.

 

http://nautil.us/issue/23/dominoes/ants-swarm-like-brains-think-rp


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Thermodynamics of firms' growth

The distribution of firms' growth and firms' sizes is a topic under intense scrutiny. In this paper we show that a thermodynamic model based on the Maximum Entropy Principle, with dynamical prior information, can be constructed that adequately describes the dynamics and distribution of firms' growth. Our theoretical framework is tested against a comprehensive data-base of Spanish firms, which covers to a very large extent Spain's economic activity with a total of 1,155,142 firms evolving along a full decade. We show that the empirical exponent of Pareto's law, a rule often observed in the rank distribution of large-size firms, is explained by the capacity of the economic system for creating/destroying firms, and can be used to measure the health of a capitalist-based economy. Indeed, our model predicts that when the exponent is larger that 1, creation of firms is favored; when it is smaller that 1, destruction of firms is favored instead; and when it equals 1 (matching Zipf's law), the system is in a full macroeconomic equilibrium, entailing "free" creation and/or destruction of firms. For medium and smaller firm-sizes, the dynamical regime changes; the whole distribution can no longer be fitted to a single simple analytic form and numerical prediction is required. Our model constitutes the basis of a full predictive framework for the economic evolution of an ensemble of firms that can be potentially used to develop simulations and test hypothetical scenarios, as economic crisis or the response to specific policy measures.

 

Thermodynamics of firms' growth
Eduardo Zambrano, Alberto Hernando, Aurelio Fernandez-Bariviera, Ricardo Hernando, Angelo Plastino

http://arxiv.org/abs/1504.07666


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Ants Swarm Like Brains Think

Ants Swarm Like Brains Think | Edgar Analytics & Complex Systems | Scoop.it
“As I watched films of these ant colonies, it looked like what was happening at the synapse of neurons. Both of these systems accumulate evidence about their inputs—returning ants or incoming voltage pulses—to make their decisions about whether to generate an output—an outgoing forager or a packet of neurotransmitter,” Goldman said. On his next trip to Stanford, he extended his stay. An unusual research collaboration had begun to coalesce: Ants would be used to study the brain, and the brain, to study ants.

 

http://nautil.us/issue/23/dominoes/ants-swarm-like-brains-think-rp


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Ingenious: David Krakauer

Ingenious: David Krakauer | Edgar Analytics & Complex Systems | Scoop.it

So one way of thinking about complexity is adaptive, many body systems. The sun is not an adaptive system; the sun doesn’t really learn. These do; these are learning systems. And we’ve never really successfully had a theory for many body learning systems. So just to make that a little clearer, the brain would be an example. There are many neurons interacting adaptively to form a representation, for example, of a visual scene; in economy, there are many individual agents deciding on the price of a good, and so forth; a political system voting for the next president. All of these systems have individual entities that are heterogeneous and acquire information according to a unique history about the world in which they live. That is not a world that Newton could deal with. There’s a very famous quote where he says something like, I have been able to understand the motion of the planets, but I will never understand the madness of men. What Newton was saying is, I don’t understand complexity.

 

http://nautil.us/issue/23/dominoes/ingenious-david-krakauer


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Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society

Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society | Edgar Analytics & Complex Systems | Scoop.it

The rapidly progressing digital revolution is now touching the foundations of the governance of societal structures. Humans are on the verge of evolving from consumers to prosumers, and old, entrenched theories – in particular sociological and economic ones – are falling prey to these rapid developments. The original assumptions on which they are based are being questioned. Each year we produce as much data as in the entire human history - can we possibly create a global crystal ball to predict our future and to optimally govern our world? Do we need wide-scale surveillance to understand and manage the increasingly complex systems we are constructing, or would bottom-up approaches such as self-regulating systems be a better solution to creating a more innovative, more successful, more resilient, and ultimately happier society? Working at the interface of complexity theory, quantitative sociology and Big Data-driven risk and knowledge management, the author advocates the establishment of new participatory systems in our digital society to enhance coordination, reduce conflict and, above all, reduce the “tragedies of the commons,” resulting from the methods now used in political, economic and management decision-making.

 

Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society
Authors: Dirk Helbing
ISBN: 978-3-319-15077-2 (Print) 978-3-319-15078-9 (Online)

http://www.pks.mpg.de/mpi-doc/sodyn/physicist-language/ ;


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Responding to complexity in socio-economic systems: How to build a smart and resilient society?

The world is changing at an ever-increasing pace. And it has changed in a much more fundamental way than one would think, primarily because it has become more connected and interdependent than in our entire history. Every new product, every new invention can be combined with those that existed before, thereby creating an explosion of complexity: structural complexity, dynamic complexity, functional complexity, and algorithmic complexity. How to respond to this challenge? And what are the costs?

 

Responding to complexity in socio-economic systems: How to build a smart and resilient society?
Dirk Helbing

http://arxiv.org/abs/1504.03750


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From seconds to months: multi-scale dynamics of mobile telephone calls

Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and "zoom out" towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.

 

From seconds to months: multi-scale dynamics of mobile telephone calls
Jari Saramaki, Esteban Moro

http://arxiv.org/abs/1504.01479


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Personalized routing for multitudes in smart cities

Human mobility in a city represents a fascinating complex system that combines social interactions, daily constraints and random explorations. New collections of data that capture human mobility not only help us to understand their underlying patterns but also to design intelligent systems. Bringing us the opportunity to reduce traffic and to develop other applications that make cities more adaptable to human needs. In this paper, we propose an adaptive routing strategy which accounts for individual constraints to recommend personalized routes and, at the same time, for constraints imposed by the collectivity as a whole. Using big data sets recently released during the Telecom Italia Big Data Challenge, we show that our algorithm allows us to reduce the overall traffic in a smart city thanks to synergetic effects, with the participation of individuals in the system, playing a crucial role.

 

Personalized routing for multitudes in smart cities
De Domenico M, Lima A, González MC, Arenas A
EPJ Data Science 2015, 4 :1

http://dx.doi.org/10.1140/epjds/s13688-015-0038-0 ;


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Must Read Report: The Internet’s Latest Disruption – Knowledge.

Must Read Report: The Internet’s Latest Disruption – Knowledge. | Edgar Analytics & Complex Systems | Scoop.it
Know or die: risk and opportunity of Knowledge 2.0
“And the web stormed the enterprise and disrupted roles, tasks and jobs: it cast speed, openness, flexibility and efficiency throughout, sparing no business processes: manufacturing, logistic, accounting, customer relation management, lead generation…”
The digital mutation is also profoundly disrupting how knowledge is acquired, organized and shared. Knowledge is an intangible, yet strategic asset of any enterprise. With businesses becoming more virtual and dematerialized, its value is patently and rapidly growing. Continue reading →
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Saving Human Lives: What Complexity Science and Information Systems can Contribute

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.

 

Saving Human Lives: What Complexity Science and Information Systems can Contribute
Dirk Helbing, Dirk Brockmann, Thomas Chadefaux, Karsten Donnay, Ulf Blanke, Olivia Woolley-Meza, Mehdi Moussaid, Anders Johansson, Jens Krause, Sebastian Schutte, Matjaž Perc

Journal of Statistical Physics
June 2014,

http://link.springer.com/article/10.1007/s10955-014-1024-9


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