Edgar Analytics & Complex Systems
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Democracy-Growth Dynamics for Richer and Poorer Countries

We study the empirical relationship between democracy and growth using grid-based panel regression and regime-transition frameworks. Our set-up nests several existing approaches, such as Barro (1996) and Gerring et al. (2005), and reconciles their conflicting messages in a more general model, and we identify the best-fitting discounts and memories. Our main finding is that democracy --best-modelled as a stock variable-- does cause growth, especially beyond the immediate short-run, by enabling the accumulation of physical, human, social and political capitals. Beyond threshold levels of democratic and economic development, however, there are incentives for de-democratization in order to boost short-run growth at the cost of higher sustained long-run growth.

 

Democracy-Growth Dynamics for Richer and Poorer Countries

Heinrich H. Nax, Anke B Schorr

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2698287


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Diversity of immune strategies explained by adaptation to pathogen statistics

Biological organisms have evolved a wide range of immune mechanisms to defend themselves against pathogens. Beyond molecular details, these mechanisms differ in how protection is acquired, processed and passed on to subsequent generations -- differences that may be essential to long-term survival. Here, we introduce a mathematical framework to compare the long-term adaptation of populations as a function of the pathogen dynamics that they experience and of the immune strategy that they adopt. We find that the two key determinants of an optimal immune strategy are the frequency and the characteristic timescale of the pathogens. Depending on these two parameters, our framework identifies distinct modes of immunity, including adaptive, innate, bet-hedging and CRISPR-like immunities, which recapitulate the diversity of natural immune systems.

 

Diversity of immune strategies explained by adaptation to pathogen statistics
Andreas Mayer, Thierry Mora, Olivier Rivoire, Aleksandra M. Walczak

http://arxiv.org/abs/1511.08836 


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Prediction in complex systems: the case of the international trade network

Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.

 

Prediction in complex systems: the case of the international trade network
Alexandre Vidmer, An Zeng, Matúš Medo, Yi-Cheng Zhang

http://arxiv.org/abs/1511.05404 ;


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Unbiased metrics of friends’ influence in multi-level networks

The spreading of information is of crucial importance for the modern information society. While we still receive information from mass media and other non-personalized sources, online social networks and influence of friends have become important personalized sources of information. This calls for metrics to measure the influence of users on the behavior of their friends. We demonstrate that the currently existing metrics of friends’ influence are biased by the presence of highly popular items in the data, and as a result can lead to an illusion of friends influence where there is none. We correct for this bias and develop three metrics that allow to distinguish the influence of friends from the effects of item popularity, and apply the metrics on real datasets. We use a simple network model based on the influence of friends and preferential attachment to illustrate the performance of our metrics at different levels of friends’ influence.

 

Unbiased metrics of friends’ influence in multi-level networks
Alexandre Vidmer, Matúš Medo and Yi-Cheng Zhang

EPJ Data Science 2015, 4:20  http://dx.doi.org/10.1140/epjds/s13688-015-0057-x ;


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The Hidden Power Laws of Ecosystems - Issue 29: Scaling - Nautilus

The Hidden Power Laws of Ecosystems - Issue 29: Scaling - Nautilus | Edgar Analytics & Complex Systems | Scoop.it
Here’s how to cause a ruckus: Ask a bunch of naturalists to simplify the world. We usually think in terms of a web of complicated…

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Gary Bamford's curator insight, November 1, 2015 3:52 PM

The complexity of complexity!

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The Observatory of Economic Complexity

The Observatory of Economic Complexity | Edgar Analytics & Complex Systems | Scoop.it

We are the world's leading visualization engine for international trade data.

 

http://atlas.media.mit.edu/


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Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus

Inspired by Adam Smith and Friedrich Hayek, many economists have postulated the existence of invisible forces that drive economic markets. These market forces interact in complex ways making it difficult to visualize or understand the interactions in every detail. Here I show how these forces can transcend a zero-sum game and become a win-win business interaction, thanks to emergent social synergies triggered by division of labor. Computer simulations with the model Sociodynamica show here the detailed dynamics underlying this phenomenon in a simple virtual economy. In these simulations, independent agents act in an economy exploiting and trading two different goods in a heterogeneous environment. All and each of the various forces and individuals were tracked continuously, allowing to unveil a synergistic effect on economic output produced by the division of labor between agents. Running simulations in a homogeneous environment, for example, eliminated all benefits of division of labor. The simulations showed that the synergies unleashed by division of labor arise if: Economies work in a heterogeneous environment; agents engage in complementary activities whose optimization processes diverge; agents have means to synchronize their activities. This insight, although trivial if viewed a posteriori, improve our understanding of the source and nature of synergies in real economic markets and might render economic and natural sciences more consilient.

 

Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus
Klaus Jaffe

http://arxiv.org/abs/1509.04264 ;


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Identifying the structural discontinuities of human interactions

The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. In the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls and uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylize the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models, thus increasing our ability to predict social activities and to plan the development of infrastructures across multiple scales.


Identifying the structural discontinuities of human interactions
Sebastian Grauwin, Michael Szell, Stanislav Sobolevsky, Philipp Hövel, Filippo Simini, Maarten Vanhoof, Zbigniew Smoreda, Albert-Laszlo Barabasi, Carlo Ratti

http://arxiv.org/abs/1509.03149



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Complex networks: theory, methods and applications | Lake Como School of Advanced Studies

Complex networks: theory, methods and applications | Lake Como School of Advanced Studies | Edgar Analytics & Complex Systems | Scoop.it

Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline —including sociology, transportation, economics and finance, biology, and myriad others — and the study of “network science” has thus become a crucial component of modern scientific education.
The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.

 

Complex networks: theory, methods and applications
Lake Como School of Advanced Studies

Villa del Grumello, Como, Italy, 16-20 May 2016

http://ntmb.lakecomoschool.org


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The Globe of Economic Complexity

The Globe of Economic Complexity | Edgar Analytics & Complex Systems | Scoop.it

Visualize $15 Trillion of World Exports

One dot equals $100M of exports

 

http://globe.cid.harvard.edu


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Luciano Lampi's curator insight, August 30, 2015 12:06 PM

Fantastic tool. Explore it.

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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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Introduction to the Modeling and Analysis of Complex Systems

Introduction to the Modeling and Analysis of Complex Systems introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves. This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example.

 

http://textbooks.opensuny.org/introduction-to-the-modeling-and-analysis-of-complex-systems/


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Evolution of Self-Organized Task Specialization in Robot Swarms

Many biological systems execute tasks by dividing them into finer sub-tasks first. This is seen for example in the advanced division of labor of social insects like ants, bees or termites. One of the unsolved mysteries in biology is how a blind process of Darwinian selection could have led to such highly complex forms of sociality. To answer this question, we used simulated teams of robots and artificially evolved them to achieve maximum performance in a foraging task. We find that, as in social insects, this favored controllers that caused the robots to display a self-organized division of labor in which the different robots automatically specialized into carrying out different subtasks in the group. Remarkably, such a division of labor could be achieved even if the robots were not told beforehand how the global task of retrieving items back to their base could best be divided into smaller subtasks. This is the first time that a self-organized division of labor mechanism could be evolved entirely de-novo. In addition, these findings shed significant new light on the question of how natural systems managed to evolve complex sociality and division of labor.

 

Ferrante E, Turgut AE, Duéñez-Guzmán E, Dorigo M, Wenseleers T (2015) Evolution of Self-Organized Task Specialization in Robot Swarms. PLoS Comput Biol 11(8): e1004273. http://dx.doi.org/10.1371/journal.pcbi.1004273 ;


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The Sensorimotor Loop as a Dynamical System: How Regular Motion Primitives May Emerge from Self-Organized Limit Cycles

We investigate the sensorimotor loop of simple robots simulated within the LPZRobots environment from the point of view of dynamical systems theory. For a robot with a cylindrical shaped body and an actuator controlled by a single proprioceptual neuron, we find various types of periodic motions in terms of stable limit cycles. These are self-organized in the sense that the dynamics of the actuator kicks in only, for a certain range of parameters, when the barrel is already rolling, stopping otherwise. The stability of the resulting rolling motions terminates generally, as a function of the control parameters, at points where fold bifurcations of limit cycles occur. We find that several branches of motion types exist for the same parameters, in terms of the relative frequencies of the barrel and of the actuator, having each their respective basins of attractions in terms of initial conditions. For low drivings stable limit cycles describing periodic and drifting back-and-forth motions are found additionally. These modes allow to generate symmetry breaking explorative behavior purely by the timing of an otherwise neutral signal with respect to the cyclic back-and-forth motion of the robot.

 

The Sensorimotor Loop as a Dynamical System: How Regular Motion Primitives May Emerge from Self-Organized Limit Cycles
Bulcsú Sándor, Tim Jahn, Laura Martin and Claudius Gros

Front. Robot. AI, 02 December 2015 | http://dx.doi.org/10.3389/frobt.2015.00031


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Ultrasociety: How 10, 000 Years of War Made Humans the Greatest Cooperators on Earth: Peter Turchin

Cooperation is powerful.
There aren’t many highly cooperative species–but they nearly cover the planet. Ants alone account for a quarter of all animal matter. Yet the human capacity to work together leaves every other species standing.
We organize ourselves into communities of hundreds of millions of individuals, inhabit every continent, and send people into space. Human beings are nature’s greatest team players. And the truly astounding thing is, we only started our steep climb to the top of the rankings–overtaking wasps, bees, termites and ants–in the last 10,000 years. Genetic evolution can’t explain this anomaly. Something else is going on. How did we become the ultrasocial animal?
In his latest book, the evolutionary scientist Peter Turchin (War and Peace and War) solves the puzzle using some astonishing results in the new science of Cultural Evolution. The story of humanity, from the first scattered bands of Homo sapiens right through to the greatest empires in history, turns out to be driven by a remorseless logic. Our apparently miraculous powers of cooperation were forged in the fires of war. Only conflict, escalating in scale and severity, can explain the extraordinary shifts in human society–and society is the greatest military technology of all.
Seen through the eyes of Cultural Evolution, human history reveals a strange, paradoxical pattern. Early humans were much more egalitarian than other primates, ruthlessly eliminating any upstart who wanted to become alpha male. But if human nature favors equality, how did the blood-soaked god kings of antiquity ever manage to claim their thrones? And how, over the course of thousands of years, did they vanish from the earth, swept away by a reborn spirit of human equality? Why is the story of human justice a chronicle of millennia-long reversals? Once again, the science points to just one explanation: war created the terrible majesty of kingship, and war obliterated it.
Is endless war, then, our fate? Or might society one day evolve beyond it? There’s only one way to answer that question. Follow Turchin on an epic journey through time, and discover something that generations of historians thought impossible: the hidden laws of history itself.


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Self-propelled Chimeras

We report the appearance of chimera states in a minimal extension of the classical Vicsek model for collective motion of self-propelled particle systems. Inspired by earlier works on chimera states in the Kuramoto model, we introduce a phase lag parameter in the particle alignment dynamics. Compared to the oscillatory networks with fixed site positions, the self-propelled particle systems can give rise to distinct forms of chimeras resembling moving flocks through an incoherent surrounding, for which we characterize their parameter domains. More specifically, we detect localized directional one-headed and multi-headed chimera states, as well as scattered directional chimeras without space localization. We discuss canonical generalizations of the elementary Vicsek model and show chimera states for them indicating the universality of this novel behavior. A continuum limit of the particle system is derived that preserves the chimeric behavior.

 

Self-propelled Chimeras
Nikita Kruk, Yuri Maistrenko, Nicolas Wenzel, Heinz Koeppl

http://arxiv.org/abs/1511.04738


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Understanding Human-Machine Networks: A Cross-Disciplinary Survey

In the current hyper-connected era, modern Information and Communication Technology systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such human-machine networks (HMNs) are embedded in the daily lives of people, both or personal and professional use. They can have a significant impact by producing synergy and innovations.
The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, nor following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.

 

Understanding Human-Machine Networks: A Cross-Disciplinary Survey
Milena Tsvetkova, Taha Yasseri, Eric T. Meyer, J. Brian Pickering, Vegard Engen, Paul Walland, Marika Lüders, Asbjørn Følstad, George Bravos

http://arxiv.org/abs/1511.05324


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Robotics: Countering singularity sensationalism

Robotics: Countering singularity sensationalism | Edgar Analytics & Complex Systems | Scoop.it

Surprising advances are being achieved, for example in 'deep learning' — a method for approximating complex functions using thousands of numerical parameters. And robots are evolving, with advances in 3D sensing and mapping. But progress is not nearly as steady as some claim. Three books explore the topic from different perspectives. All suggest that robot superiority faces a formidable obstacle: human psychology.

 

Robotics: Countering singularity sensationalism

Ken Goldberg
Nature 526, 320–321 (15 October 2015) http://dx.doi.org/10.1038/526320a ;


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Network Science Institute @Northeastern

Network Science Institute @Northeastern | Edgar Analytics & Complex Systems | Scoop.it

The Network Science Institute was born out of our commitment to explore universality and predictability of systems to discover their function and develop intervention strategies to improve the health and security of human populations. The Institute is a highly collaborative and interdisciplinary lab space driven by the need to integrate models, theories and problem solving approaches across disciplines in research and education.

 

http://networkscienceinstitute.org 


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Extended Inclusive Fitness Theory bridges Economics and Biology through a common understanding of Social Synergy

Inclusive Fitness Theory (IFT) was proposed half a century ago by W.D. Hamilton to explain the emergence and maintenance of cooperation between individuals that allows the existence of society. Contemporary evolutionary ecology identified several factors that increase inclusive fitness, in addition to kin-selection, such as assortation or homophily, and social synergies triggered by cooperation. Here we propose an Extend Inclusive Fitness Theory (EIFT) that includes in the fitness calculation all direct and indirect benefits an agent obtains by its own actions, and through interactions with kin and with genetically unrelated individuals. This formulation focuses on the sustainable cost/benefit threshold ratio of cooperation and on the probability of agents sharing mutually compatible memes or genes. This broader description of the nature of social dynamics allows to compare the evolution of cooperation among kin and non-kin, intra- and inter-specific cooperation, co-evolution, the emergence of symbioses, of social synergies, and the emergence of division of labor. EIFT promotes interdisciplinary cross fertilization of ideas by allowing to describe the role for division of labor in the emergence of social synergies, providing an integrated framework for the study of both, biological evolution of social behavior and economic market dynamics.

 

Extended Inclusive Fitness Theory bridges Economics and Biology through a common understanding of Social Synergy
Klaus Jaffe

http://arxiv.org/abs/1509.02745


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International Workshop on Nonlinearity, Nonequilibrium and Complexity: Questions and perspectives in Statistical Physics.

International Workshop on Nonlinearity, Nonequilibrium and Complexity: Questions and perspectives in Statistical Physics. | Edgar Analytics & Complex Systems | Scoop.it

The workshop is aimed at discussing a few chosen contemporary developments in statistical physics. Topics include problems in condensed matter and dynamical systems (pattern structures, granular matter, glass formation, turbulence, marginal chaos, etc.); and also current applications outside of traditional fields in physics (in biology, ecology, sociology, economy, seismology and other geophysical, astrophysical phenomena, complexity in urban developments, complexity in linguistics, literature and arts, etc.). There would be an examination of equilibrium and nonequilibrium theories, and of the current efforts in generalizing statistical mechanical structures and methods. We would like to emphasize that our aim is to make the meeting the occasion for a memorable scientific discussion that can be carried out comfortably in an intimate environment.

 

International Workshop on Nonlinearity, Nonequilibrium and Complexity: Questions and perspectives in Statistical Physics. This is an event in honor of Prof. Alberto Robledo's 70th birthday.

Mexico City, Mexico

2015-11-29:12-04

https://sites.google.com/site/robledo70b/ ;


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Civilization Far From Equilibrium - Energy, Complexity, and Human Survival

Human societies use complexity -- within their institutions and technologies -- to address their various problems, and they need high-quality energy to create and sustain this complexity. But now greater complexity is producing diminishing returns in wellbeing, while the energetic cost of key sources of energy is rising fast. Simultaneously, humankind's problems are becoming vastly harder, which requires societies to deliver yet more complexity and thus consume yet more energy. Resolving this paradox is the central challenge of the 21st century. Thomas Homer-Dixon holds the CIGI Chair of Global Systems at the Balsillie School of International Affairs in Waterloo, Canada, and is a Professor at the University of Waterloo.

 

https://www.youtube.com/watch?v=4Vf-y3mv57U


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Creativity and ALife

Three forms of creativity are exemplified in biology and studied in ALife. Combinational creativity exists as the first step in genetic algorithms. Exploratory creativity is seen in models using cellular automata or evolutionary programs. Transformational creativity can result from evolutionary programming. Even radically novel forms can do so, given input from outside the program itself. Transformational creativity appears also in reaction-diffusion models of morphogenesis. That there are limits to biological creativity is suggested by ALife work bearing on instances of biological impossibility.

 

Creativity and ALife
Margaret A. Boden

Artificial Life

Summer 2015, Vol. 21, No. 3, Pages 354-365
http://dx.doi.org/10.1162/ARTL_a_00176 


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The joy of transient chaos

The joy of transient chaos | Edgar Analytics & Complex Systems | Scoop.it

We intend to show that transient chaos is a very appealing, but still not widely appreciated, subfield of nonlinear dynamics. Besides flashing its basic properties and giving a brief overview of the many applications, a few recent transient-chaos-related subjects are introduced in some detail. These include the dynamics of decision making, dispersion, and sedimentation of volcanic ash, doubly transient chaos of undriven autonomous mechanical systems, and a dynamical systems approach to energy absorption or explosion.

 

The joy of transient chaos   

Tamás Tél

Chaos 25, 097619 (2015)

http://dx.doi.org/10.1063/1.4917287 


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Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks

Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks | Edgar Analytics & Complex Systems | Scoop.it
The financial crisis illustrated the need for a functional understanding of systemic risk in strongly interconnected financial structures. Dynamic processes on complex networks being intrinsically difficult to model analytically, most recent studies of this problem have relied on numerical simulations. Here we report analytical results in a network model of interbank lending based on directly relevant financial parameters, such as interest rates and leverage ratios. We obtain a closed-form formula for the “critical degree” (the number of creditors per bank below which an individual shock can propagate throughout the network), and relate failures distributions to network topologies, in particular scalefree ones. Our criterion for the onset of contagion turns out to be isomorphic to the condition for cooperation to evolve on graphs and social networks, as recently formulated in evolutionary game theory. This remarkable connection supports recent calls for a methodological rapprochement between finance and ecology.

 

Smerlak M, Stoll B, Gupta A, Magdanz JS (2015) Mapping Systemic Risk: Critical Degree and Failures Distribution in Financial Networks. PLoS ONE 10(7): e0130948. http://dx.doi.org/10.1371/journal.pone.0130948 ;


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malek's comment, August 13, 2015 10:23 AM
though provoking, wonder why we coin financial risk with moral hazards?
pdeppisch's comment, August 13, 2015 12:40 PM
Because cheating / pulling a fast one is what financial services is all about: http://www.motherjones.com/politics/2010/01/mortgage-sharks-foreclosing Loan Sharks!