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 Rescooped by Juan I. Perotti from Papers

## Information Flows? A Critique of Transfer Entropies

A central task in analyzing complex dynamics is to determine the loci of information storage and the communication topology of information flows within a system. Over the last decade and a half, diagnostics for the latter have come to be dominated by the transfer entropy. Via straightforward examples, we show that it and a derivative quantity, the causation entropy, do not, in fact, quantify the flow of information. At one and the same time they can overestimate flow or underestimate influence. We isolate why this is the case and propose alternate measures for information flow. An auxiliary consequence reveals that the proliferation of networks as a now-common theoretical model for large-scale systems in concert with the use of transfer-like entropies has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems, despite the occurrence of polyadic dependencies. The net result is that much of the sophisticated organization of complex systems goes undetected.

Information Flows? A Critique of Transfer Entropies
Ryan G. James, Nix Barnett, James P. Crutchfield

http://arxiv.org/abs/1512.06479

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## Composing Music With Recurrent Neural Networks

It's hard not to be blown away by the surprising power of neural networks these days. With enough training, so called "deep neural networks", with many nodes and hidden layers, can do impressively ...

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## The multi-layer network nature of systemic risk and its implications for the costs of financial crises

•We present a multi-layer network approach to quantify systemic-risk.
•Systemic-risk is drastically underestimated when computed on single layers only, as is current practice.
•We introduce a nation-wide systemic-risk index that reflects the public costs for crises.
•The index unveils drastically higher risk than estimated by current risk indicators.
•We demonstrate the validity of the method on a complete dataset of the Mexican financial system.

The multi-layer network nature of systemic risk and its implications for the costs of financial crises
Sebastian Poledna, José Luis Molina-Borboa, Serafín Martínez-Jaramillo, , Marco van der Leij, Stefan Thurner

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## Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity

The capacity to integrate information is a prominent feature of biological brains and has been related to cognitive flexibility as well as consciousness. To investigate how environment complexity affects the capacity for information integration, we simulated the evolution of artificial organisms (“animats”) controlled by small, adaptive neuron-like networks (“brains”). Task environments varied in difficulty due primarily to the requirements for internal memory. By applying measures of information integration, we show that, under constraints on the number of available internal elements, the animats evolved brains that were the more integrated the more internal memory was required to solve a given task. Thus, in complex environments with a premium on context-sensitivity and memory, integrated brain architectures have an evolutionary advantage over modular ones.

Albantakis L, Hintze A, Koch C, Adami C, Tononi G (2014) Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity. PLoS Comput Biol 10(12): e1003966. http://dx.doi.org/10.1371/journal.pcbi.1003966 ;

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## Systemic risk analysis in reconstructed economic and financial networks

The assessment of fundamental properties for economic and financial systems, such as systemic risk, is systematically hindered by privacy issues − that put severe limitations on the available information. Here we introduce a novel method to reconstruct partially-accessible networked systems of this kind. The method is based on the knowledge of the fitnesses, i.e., intrinsic node-specific properties, and of the number of connections of only a limited subset of nodes. Such information is used to calibrate a directed configuration model which can generate ensembles of networks intended to represent the real system, so that the real network properties can be estimated within the generated ensemble in terms of mean values of the observables. Here we focus on estimating those properties that are commonly used to measure the network resilience to shock and crashes. Tests on both artificial and empirical networks shows that the method is remarkably robust with respect to the limitedness of the information available, thus representing a valuable tool for gaining insights on privacy-protected economic and financial systems.

Systemic risk analysis in reconstructed economic and financial networks
Giulio Cimini, Tiziano Squartini, Andrea Gabrielli, Diego Garlaschelli

http://arxiv.org/abs/1411.7613

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## Systemic risk analysis in reconstructed economic and financial networks

The assessment of fundamental properties for economic and financial systems, such as systemic risk, is systematically hindered by privacy issues − that put severe limitations on the available information. Here we introduce a novel method to reconstruct partially-accessible networked systems of this kind. The method is based on the knowledge of the fitnesses, i.e., intrinsic node-specific properties, and of the number of connections of only a limited subset of nodes. Such information is used to calibrate a directed configuration model which can generate ensembles of networks intended to represent the real system, so that the real network properties can be estimated within the generated ensemble in terms of mean values of the observables. Here we focus on estimating those properties that are commonly used to measure the network resilience to shock and crashes. Tests on both artificial and empirical networks shows that the method is remarkably robust with respect to the limitedness of the information available, thus representing a valuable tool for gaining insights on privacy-protected economic and financial systems.

Systemic risk analysis in reconstructed economic and financial networks
Giulio Cimini, Tiziano Squartini, Andrea Gabrielli, Diego Garlaschelli

http://arxiv.org/abs/1411.7613

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## Complexity and Dynamical Depth

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.
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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,

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!

 Rescooped by Juan I. Perotti from Complex World

## How bird flocks are like liquid helium

Mathematical model shows how hundreds of starlings coordinate their movements in flight.

A flock of starlings flies as one, a spectacular display in which each bird flits about as if in a well-choreographed dance. Everyone seems to know exactly when and where to turn. Now, for the first time, researchers have measured how that knowledge moves through the flock—a behavior that mirrors certain quantum phenomena of liquid helium.

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## Punctuated Equilibrium in the Large Scale Evolution of Programming Languages

The analogies and differences between biological and cultural evolution have been explored by evolutionary biologists, historians, engineers and linguists alike. Two well known domains of cultural change are language and technology. Both share some traits relating the evolution of species, but technological change is very difficult to study. A major challenge in our way towards a scientific theory of technological evolution is how to properly define evolutionary trees or clades and how to weight the role played by horizontal transfer of information. Here we study the large scale historical development of programming languages, which have deeply marked social and technological advances in the last half century. We analyse their historical connections using network theory and reconstructed phylogenetic networks. Using both data analysis and network modelling, it is shown that their evolution is highly uneven, marked by innovation events where new languages are created out of improved combinations of different structural components belonging
to previous languages. These radiation events occur in a bursty pattern and are tied to novel technological and social niches. The method can be extrapolated to other systems and consistently captures the major classes of languages and the widespread horizontal design exchanges, revealing a punctuated evolutionary path.

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## Large-scale Fluctuations of Lyapunov Exponents in Diffusive Systems

We present a general formalism for computing Lyapunov exponents and their fluctuations in spatially extended systems described by diffusive fluctuating hydrodynamics, thus extending the concepts of dynamical system theory to a broad range of non-equilibrium systems. Our analytical results compare favorably with simulations of a lattice model of heat conduction. We further show how the computation of Lyapunov exponents for the Symmetric Simple Exclusion Process relates to damage spreading and to a two-species pair annihilation process, for which our formalism yields new finite size results.

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## Spatial correlation analysis of cascading failures: Congestions and Blackouts

Cascading failures have become major threats to network robustness due to their potential catastrophic consequences, where local perturbations can induce global propagation of failures. Unlike failures spreading via direct contacts due to structural interdependencies, overload failures usually propagate through collective interactions among system components. Despite the critical need in developing protection or mitigation strategies in networks such as power grids and transportation, the propagation behavior of cascading failures is essentially unknown. Here we find by analyzing our collected data that jams in city traffic and faults in power grid are spatially long-range correlated with correlations decaying slowly with distance. Moreover, we find in the daily traffic, that the correlation length increases dramatically and reaches maximum, when morning or evening rush hour is approaching. Our study can impact all efforts towards improving actively system resilience ranging from evaluation of design schemes, development of protection strategies to implementation of mitigation programs.

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tom cockburn's curator insight,

Could be far reaching in its significance

 Rescooped by Juan I. Perotti from Financial Markets and Trading

## Do Google Trend data contain more predictability than price returns?

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ukituki's curator insight,

Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culprit. We then argue that the real question is whether such data contain more predictability than price returns themselves: our backtest yields a performance of about 17bps per week which only weakly depends on the kind of data on which predictors are based, i.e. either past price returns or Google Trends data, or both.

 Rescooped by Juan I. Perotti from Data is big

## Tensor Methods in Machine Learning

Algorithms off the convex path.

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## Networkcontrology

An increasing number of complex systems are now modeled as networks of coupled dynamical entities. Nonlinearity and high-dimensionality are hallmarks of the dynamics of such networks but have generally been regarded as obstacles to control. Here, I discuss recent advances on mathematical and computational approaches to control high-dimensional nonlinear network dynamics under general constraints on the admissible interventions. I also discuss the potential of network control to address pressing scientific problems in various disciplines.

Networkcontrology,
Chaos 25, 097621 (2015)
http://dx.doi.org/10.1063/1.4931570 ;

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## Fundamental limitations of network reconstruction

Network reconstruction helps us understand, diagnose and control complex networked systems by inferring properties of their interaction matrices, which characterize how nodes in the systems directly interact with each other. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations on which properties of the interaction matrix can be inferred from accessing the dynamics of individual nodes remain unknown. Here we characterize these fundamental limitations by deriving the necessary and sufficient condition to reconstruct any property of the interaction matrix. Counterintuitively, we prove that inferring less information ---such as the sign/connectivity pattern or the degree sequence--- does not make the network reconstruction problem easier than recovering the interaction matrix itself (i.e. the traditional parameter identification problem). Our analysis also reveals that using prior information of the interaction matrix ---such as bound on the edge-weights--- is the only way to circumvent these fundamental limitations of network reconstruction. This sheds light on designing new algorithms with practical improvements over parameter identification methods.

Fundamental limitations of network reconstruction
Marco Tulio Angulo, Jaime A. Moreno, Albert-László Barabási, Yang-Yu Liu

http://arxiv.org/abs/1508.03559

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## Invasion of cooperation in scale-free networks: Accumulated vs. average payoffs

It is well known that cooperation cannot be an evolutionary stable strategy for a non-iterative game in a well-mixed population. In contrast, structured populations favor cooperation since cooperators can benefit each other by forming local clusters. Previous studies have shown that scale-free networks strongly promote cooperation. However, little is known about the invasion mechanism of cooperation in scale-free networks. To study microscopic and macroscopic behaviors of cooperators' invasion, we conducted computational experiments of the evolution of cooperation in scale-free networks where, starting from all defectors, cooperators can spontaneously emerge by mutation. Since the evolutionary dynamics are influenced by the definition of fitness, we tested two commonly adopted fitness functions: accumulated payoff and average payoff. Simulation results show that cooperation is strongly enhanced with the accumulated payoff fitness compared to the average payoff fitness. However, the difference between the two functions decreases as the average degree increases. Moreover, with the average payoff fitness, low-degree nodes play a more important role in spreading cooperative strategies compared to the case of the accumulated payoff fitness.

Invasion of cooperation in scale-free networks: Accumulated vs. average payoffs
Genki Ichinose, Hiroki Sayama

http://arxiv.org/abs/1412.2311

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## On the Complexity and Behaviour of Cryptocurrencies Compared to Other Markets

We show that the behaviour of Bitcoin has interesting similarities to stock and precious metal markets, such as gold and silver. We report that whilst Litecoin, the second largest cryptocurrency, closely follows Bitcoin's behaviour, it does not show all the reported properties of Bitcoin. Agreements between apparently disparate complexity measures have been found, and it is shown that statistical, information-theoretic, algorithmic and fractal measures have different but interesting capabilities of clustering families of markets by type. The report is particularly interesting because of the range and novel use of some measures of complexity to characterize price behaviour, because of the IRS designation of Bitcoin as an investment property and not a currency, and the announcement of the Canadian government's own electronic currency MintChip.

On the Complexity and Behaviour of Cryptocurrencies Compared to Other Markets
Daniel Wilson-Nunn, Hector Zenil

http://arxiv.org/abs/1411.1924

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## Top-Down Causation and the Rise of Information in the Emergence of Life

Biological systems represent a unique class of physical systems in how they process and manage information. This suggests that changes in the flow and distribution of information played a prominent role in the origin of life. Here I review and expand on an emerging conceptual framework suggesting that the origin of life may be identified as a transition in causal structure and information flow, and detail some of the implications for understanding the early stages chemical evolution.

Top-Down Causation and the Rise of Information in the Emergence of Life
Sara Imari Walker

Information 2014, 5(3), 424-439; http://dx.doi.org/10.3390/info5030424

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Eli Levine's curator insight,

If this is the case, then it confirms a lot of what I've been hypothesizing about government and its role in shaping the legal landscape of our social world (which then influences our ecological, social, environmental, and political world).  Government is always beholden to the natural laws of physics, biology, psychology/neurology, sociology, and economics.  However, government can play a significant role in determining the effects that we experience in our world, based on their obedience to natural laws and limits.

We can make a better, healthier, more sustainable, and more resilient world for ourselves within the context of our environment, social, ecological, and cosmological.  The question is, do we have the will, intelligence, wisdom, sense, and accuracy of perception to do anything with it?

We'll see, I guess.

Here's hoping for a permanent leap forward for humanity.  One that will not end until the universe itself comes to an end (it always does).

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## The Leverage Effect on Wealth Distribution in a Controllable Laboratory Stock Market

Wealth distribution has always been an important issue in our economic and social life, since it affects the harmony and stabilization of the society. Under the background of widely used financial tools to raise leverage these years, we studied the leverage effect on wealth distribution of a population in a controllable laboratory market in which we have conducted several human experiments, and drawn the conclusion that higher leverage leads to a higher Gini coefficient in the market. A higher Gini coefficient means the wealth distribution among a population becomes more unequal. This is a result of the ascending risk with growing leverage level in the market plus the diversified trading abilities and risk preference of the participants. This work sheds light on the effects of leverage and its related regulations, especially its impact on wealth distribution. It also shows the capability of the method of controllable laboratory markets which could be helpful in several fields of study such as economics, econophysics and sociology.

The Leverage Effect on Wealth Distribution in a Controllable Laboratory Stock Market

Zhu C, Yang G, An K, Huang J

PLoS ONE 9(6): e100681. (2014)

http://dx.doi.org/10.1371/journal.pone.0100681

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## Evaluating sentiment in financial news articles

Can the choice of words and tone used by the authors of financial news articles correlate to measurable stock price movements? If so, can the magnitude of price movement be predicted using these same variables? We investigate these questions using the Arizona Financial Text (AZFinText) system, a financial news article prediction system, and pair it with a sentiment analysis tool. Through our analysis, we found that subjective news articles were easier to predict in price direction (59.0% versus 50.0% of chance alone) and using a simple trading engine, subjective articles garnered a 3.30% return. Looking further into the role of author tone in financial news articles, we found that articles with a negative sentiment were easiest to predict in price direction (50.9% versus 50.0% of chance alone) and a 3.04% trading return. Investigating negative sentiment further, we found that our system was able to predict price decreases in articles of a positive sentiment 53.5% of the time, and price increases in articles of a negative sentiment 52.4% of the time. We believe that perhaps this result can be attributable to market traders behaving in a contrarian manner, e.g., see good news, sell; see bad news, buy.

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## Hidden scaling patterns and universality in written communication

The temporal statistics exhibited by written correspondence appear to be media dependent, with features which have so far proven difficult to characterize. We explain the origin of these difficulties by disentangling the role of spontaneous activity from decision-based prioritizing processes in human dynamics, clocking all waiting times through each agent's proper time'' measured by activity. This unveils the same fundamental patterns in written communication across all media (letters, email, sms), with response times displaying truncated power-law behavior and average exponents near -${}\frac{3}{2}$. When standard time is used, the response time probabilities are theoretically predicted to exhibit a bimodal character, which is empirically borne out by our newly collected years-long data on email. These perspectives on the temporal dynamics of human correspondence should aid in the analysis of interaction phenomena in general, including resource management, optimal pricing and routing, information sharing, and emergency handling.

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## The scaling of human interactions with city size

The size of cities is known to play a fundamental role in social and economic life. Yet, its relation to the structure of the underlying network of human interactions has not been investigated empirically in detail. In this paper, we map society-wide communication networks to the urban areas of two European countries. We show that both the total number of contacts and the total communication activity grow superlinearly with city population size, according to well-defined scaling relations and resulting from a multiplicative increase that affects most citizens. Perhaps surprisingly, however, the probability that an individual's contacts are also connected with each other remains largely unaffected. These empirical results predict a systematic and scale-invariant acceleration of interaction-based spreading phenomena as cities get bigger, which is numerically confirmed by applying epidemiological models to the studied networks. Our findings should provide a microscopic basis towards understanding the superlinear increase of different socioeconomic quantities with city size, that applies to almost all urban systems and includes, for instance, the creation of new inventions or the prevalence of certain contagious diseases.

Markus Schläpfer, Luís M. A. Bettencourt, Sébastian Grauwin, Mathias Raschke, Rob Claxton, Zbigniew Smoreda, Geoffrey B. West, and Carlo Ratti
The scaling of human interactions with city size
J. R. Soc. Interface. 2014 11 20130789; http://dx.doi.org/10.1098/rsif.2013.0789

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## Disorder induces explosive synchronization

We study explosive synchronization, a phenomenon characterized by first-order phase transitions between incoherent and synchronized states in networks of coupled oscillators. While explosive synchronization has been the subject of many recent studies, in each case strong conditions on the heterogeneity of the network, its link weights, or its initial construction are imposed to engineer a first-order phase transition. This raises the question of how robust explosive synchronization is in view of more realistic structural and dynamical properties. Here we show that explosive synchronization can be induced in mildly heterogeneous networks by the addition of quenched disorder to the oscillators' frequencies, demonstrating that it is not only robust to, but moreover promoted by, this natural mechanism. We support these findings with numerical and analytical results, presenting simulations of a real neural network as well as a self-consistency theory used to study synthetic networks.

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## The civilizing process in London’s Old Bailey

One of the characteristics of the modern era is the emergence of new bureaucratic and social mechanisms for the management and control of violence. Our analysis of 150 y of spoken word testimony in the English criminal justice system provides new insight into this critical process. We show how, beginning around the 1800s, trials for violent and nonviolent offenses become increasingly distinct. Driven by a shifting set of underlying signals, this long-term shift in the underlying norms of the system involves both changes in bureaucratic practice and in civil society as a whole.

The civilizing process in London’s Old Bailey
Sara Klingenstein, Tim Hitchcock, and Simon DeDeo

http://dx.doi.org/10.1073/pnas.1405984111
PNAS June 16, 2014

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