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Ontonix - Complex Systems Management, Business Risk Management

Ontonix - Complex Systems Management, Business Risk Management | Edgar Analytics & Complex Systems | Scoop.it
WHAT IS COMPLEXITY?

Complexity, just like for example energy, is a natural property of every system. It is defined as a function of structureand  uncertainty.  Humans instinctively try to stay away from highly complex scenarios because of one fundamental reason – high complexity implies a capacity to deliver surprising behavior. Since complexity is a function of entropy, it is measured in bits. Complexity is an unusual function. It combines two "antagonistic" components which in Nature tend to oppose each other. Structure attempts to persist in the face of the erosive action of entropy. Our complexity metric blends them together


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Extended inclusive fitness theory: synergy and assortment drives the evolutionary dynamics in biology and economics  

W.D. Hamilton’s Inclusive Fitness Theory explains the conditions that favor the emergence and maintenance of social cooperation. Today we know that these include direct and indirect benefits an agent obtains by its actions, and through interactions with kin and with genetically unrelated individuals. That is, in addition to kin-selection, assortation or homophily, and social synergies drive the evolution of cooperation. An Extended Inclusive Fitness Theory (EIFT) synthesizes the natural selection forces acting on biological evolution and on human economic interactions by assuming that natural selection driven by inclusive fitness produces agents with utility functions that exploit assortation and synergistic opportunities. This formulation allows to estimate sustainable cost/benefit threshold ratios of cooperation among organisms and/or economic agents, using existent analytical tools, illuminating our understanding of the dynamic nature of society, the evolution of cooperation among kin and non-kin, inter-specific cooperation, co-evolution, symbioses, division of labor and social synergies. EIFT helps to promote an interdisciplinary cross fertilization of the understanding of synergy by, for example, 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. Another example is a bio-economic understanding of the motivations of terrorists, which identifies different forms of terrorism.

 

Extended inclusive fitness theory: synergy and assortment drives the evolutionary dynamics in biology and economics
Klaus Jaffe

SpringerPlus 2016 5:1092
http://dx.doi.org/10.1186/s40064-016-2750-z ;


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Eco-evolutionary dynamics of social dilemmas

Social dilemmas are an integral part of social interactions. Cooperative actions, ranging from secreting extra-cellular products in microbial populations to donating blood in humans, are costly to the actor and hence create an incentive to shirk and avoid the costs. Nevertheless, cooperation is ubiquitous in nature. Both costs and benefits often depend non-linearly on the number and types of individuals involved -- as captured by idioms such as `too many cooks spoil the broth' where additional contributions are discounted, or `two heads are better than one' where cooperators synergistically enhance the group benefit. Interaction group sizes may depend on the size of the population and hence on ecological processes. This results in feedback mechanisms between ecological and evolutionary processes, which jointly affect and determine the evolutionary trajectory. Only recently combined eco-evolutionary processes became experimentally tractable in microbial social dilemmas. Here we analyse the evolutionary dynamics of non-linear social dilemmas in settings where the population fluctuates in size and the environment changes over time. In particular, cooperation is often supported and maintained at high densities through ecological fluctuations. Moreover, we find that the combination of the two processes routinely reveals highly complex dynamics, which suggests common occurrence in nature.

 

Eco-evolutionary dynamics of social dilemmas
Chaitanya S. Gokhale, Christoph Hauert

http://arxiv.org/abs/1605.07656


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From big data to important information

Advances in science are being sought in newly available opportunities to collect massive quantities of data about complex systems. While key advances are being made in detailed mapping of systems, how to relate these data to solving many of the challenges facing humanity is unclear. The questions we often wish to address require identifying the impact of interventions on the system and that impact is not apparent in the detailed data that is available. Here, we review key concepts and motivate a general framework for building larger scale views of complex systems and for characterizing the importance of information in physical, biological, and social systems. We provide examples of its application to evolutionary biology with relevance to ecology, biodiversity, pandemics, and human lifespan, and in the context of social systems with relevance to ethnic violence, global food prices, and stock market panic. Framing scientific inquiry as an effort to determine what is important and unimportant is a means for advancing our understanding and addressing many practical concerns, such as economic development or treating disease.

 

From big data to important information
Yaneer Bar-Yam

Complexity

http://dx.doi.org/10.1002/cplx.21785


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A "Social Bitcoin" could sustain a democratic digital world

A multidimensional financial system could provide benefits for individuals, companies, and states. Instead of top-down control, which is destined to eventually fail in a hyperconnected world, a bottom-up creation of value can unleash creative potential and drive innovations. Multiple currency dimensions can represent different externalities and thus enable the design of incentives and feedback mechanisms that foster the ability of complex dynamical systems to self-organize and lead to a more resilient society and sustainable economy. Modern information and communication technologies play a crucial role in this process, as Web 2.0 and online social networks promote cooperation and collaboration on unprecedented scales. Within this contribution, we discuss how one dimension of a multidimensional currency system could represent socio-digital capital (Social Bitcoins) that can be generated in a bottom-up way by individuals who perform search and navigation tasks in a future version of the digital world. The incentive to mine Social Bitcoins could sustain digital diversity, which mitigates the risk of totalitarian control by powerful monopolies of information and can create new business opportunities needed in times where a large fraction of current jobs is estimated to disappear due to computerisation.

 

A "Social Bitcoin" could sustain a democratic digital world
Kaj-Kolja Kleineberg, Dirk Helbing

http://arxiv.org/abs/1604.08168


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An Evolutionary Game Theoretic Approach to Multi-Sector Coordination and Self-Organization

Coordination games provide ubiquitous interaction paradigms to frame human behavioral features, such as information transmission, conventions and languages as well as socio-economic processes and institutions. By using a dynamical approach, such as Evolutionary Game Theory (EGT), one is able to follow, in detail, the self-organization process by which a population of individuals coordinates into a given behavior. Real socio-economic scenarios, however, often involve the interaction between multiple co-evolving sectors, with specific options of their own, that call for generalized and more sophisticated mathematical frameworks. In this paper, we explore a general EGT approach to deal with coordination dynamics in which individuals from multiple sectors interact. Starting from a two-sector, consumer/producer scenario, we investigate the effects of including a third co-evolving sector that we call public. We explore the changes in the self-organization process of all sectors, given the feedback that this new sector imparts on the other two.

 

An Evolutionary Game Theoretic Approach to Multi-Sector Coordination and Self-Organization
Fernando P. Santos, Sara Encarnação, Francisco C. Santos, Juval Portugali and Jorge M. Pacheco

Entropy 2016, 18(4), 152; http://dx.doi.org/10.3390/e18040152


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Cooperation, competition and the emergence of criticality in communities of adaptive systems

The hypothesis that living systems can benefit from operating at the vicinity of critical points has gained momentum in recent years. Criticality may confer an optimal balance between too ordered and exceedingly noisy states. Here we present a model, based on information theory and statistical mechanics, illustrating how and why a community of agents aimed at understanding and communicating with each other converges to a globally coherent state in which all individuals are close to an internal critical state, i.e. at the borderline between order and disorder. We study—both analytically and computationally—the circumstances under which criticality is the best possible outcome of the dynamical process, confirming the convergence to critical points under very generic conditions. Finally, we analyze the effect of cooperation (agents trying to enhance not only their fitness, but also that of other individuals) and competition (agents trying to improve their own fitness and to diminish those of competitors) within our setting. The conclusion is that, while competition fosters criticality, cooperation hinders it and can lead to more ordered or more disordered consensual outcomes.

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Networks of plants: how to measure similarity in vegetable species

Despite the common misconception of nearly static organisms, plants do interact continuously with the environment and with each other. It is fair to assume that during their evolution they developed particular features to overcome problems and to exploit possibilities from environment. In this paper we introduce various quantitative measures based on recent advancements in complex network theory that allow to measure the effective similarities of various species. By using this approach on the similarity in fruit-typology ecological traits we obtain a clear plant classification in a way similar to traditional taxonomic classification. This result is not trivial, since a similar analysis done on the basis of diaspore morphological properties do not provide any clear parameter to classify plants species. Complex network theory can then be used in order to determine which feature amongst many can be used to distinguish scope and possibly evolution of plants. Future uses of this approach range from functional classification to quantitative determination of plant communities in nature.

 

Networks of plants: how to measure similarity in vegetable species
Gianna Vivaldo, Elisa Masi, Camilla Pandolfi, Stefano Mancuso, Guido Caldarelli

http://arxiv.org/abs/1602.05887


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Transition to Chaos in Random Neuronal Networks

Transition to Chaos in Random Neuronal Networks | Edgar Analytics & Complex Systems | Scoop.it

Cortical neural circuits have been hypothesized to operate in a regime termed the “edge of chaos.” A new theoretical study puts this regime in a more biologically plausible perspective.

 

Transition to Chaos in Random Neuronal Networks
Jonathan Kadmon and Haim Sompolinsky
Phys. Rev. X 5, 041030 (2015)

http://dx.doi.org/10.1103/PhysRevX.5.041030


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Ants determine their next move at rest: motor planning and causality in complex systems

To find useful work to do for their colony, individual eusocial animals have to move, somehow staying attentive to relevant social information. Recent research on individual Temnothorax albipennis ants moving inside their colony’s nest found a power-law relationship between a movement’s duration and its average speed; and a universal speed profile for movements showing that they mostly fluctuate around a constant average speed. From this predictability it was inferred that movement durations are somehow determined before the movement itself. Here, we find similar results in lone T. albipennis ants exploring a large arena outside the nest, both when the arena is clean and when it contains chemical information left by previous nest-mates. This implies that these movement characteristics originate from the same individual neural and/or physiological mechanism(s), operating without immediate regard to social influences. However, the presence of pheromones and/or other cues was found to affect the inter-event speed correlations. Hence we suggest that ants’ motor planning results in intermittent response to the social environment: movement duration is adjusted in response to social information only between movements, not during them. This environmentally flexible, intermittently responsive movement behaviour points towards a spatially allocated division of labour in this species. It also prompts more general questions on collective animal movement and the role of intermittent causation from higher to lower organizational levels in the stability of complex systems.

 

Edmund R Hunt, Roland J Baddeley, Alan Worley, Ana B Sendova-Franks, Nigel R Franks. 2016 Ants determine their next move at rest: motor planning and causality in complex systems. Royal Society Open Science 3:150534.
http://rsos.royalsocietypublishing.org/content/3/1/150534


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Undecidability of the spectral gap

In quantum many-body physics, the spectral gap is the energy difference between the ground state of a system and its first excited state. Establishing whether it is possible to make a decision about the system being gapped or gapless, given a specific model Hamiltonian, is a long-standing problem in physics known as the spectral gap problem. Here, Toby Cubitt et al. prove that the spectral gap problem is undecidable. Although it had been known before that deciding about the existence of a spectral gap is difficult, this result proves the strongest possible form of algorithmic difficulty for a core problem of many-body physics.

 

Undecidability of the spectral gap
Toby S. Cubitt, David Perez-Garcia & Michael M. Wolf

Nature 528, 207–211 (10 December 2015) http://dx.doi.org/10.1038/nature16059 ;


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Complexity Digest's curator insight, December 11, 2015 4:01 PM

This result is similar to those of Gödel, Turing, and Chaitin, but for physics, proving that not every macroscopic property can be derived from microscopic properties, i.e. non-reductionism.

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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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Gamification Taxonomy

Gamification Taxonomy | Edgar Analytics & Complex Systems | Scoop.it
We have conducted extensive empirical research over the last 4 years as part of a university PhD program to develop the world’s first comprehensive enterprise gamification taxonomy. Our taxonomy has been peer reviewed and is built on our database of over 300 enterprise gamification projects. This has now become a globally recognised tool that helps designers and organisations to plan, develop and implement a gamification initiative.

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Gamification could be the bright spot in advancing Higher Education forward as well as a way to democratize Eduction of the highest degree
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Small groups and long memories promote cooperation

Complex social behaviors lie at the heart of many of the challenges facing evolutionary biology, sociology, economics, and beyond. For evolutionary biologists the question is often how group behaviors such as collective action, or decision making that accounts for memories of past experience, can emerge and persist in an evolving system. Evolutionary game theory provides a framework for formalizing these questions and admitting them to rigorous study. Here we develop such a framework to study the evolution of sustained collective action in multi-player public-goods games, in which players have arbitrarily long memories of prior rounds of play and can react to their experience in an arbitrary way. We construct a coordinate system for memory-m strategies in iterated n-player games that permits us to characterize all cooperative strategies that resist invasion by any mutant strategy, and stabilize cooperative behavior. We show that, especially when groups are small, longer-memory strategies make cooperation easier to evolve, by increasing the number of ways to stabilize cooperation. We also explore the co-evolution of behavior and memory. We find that even when memory has a cost, longer-memory strategies often evolve, which in turn drives the evolution of cooperation, even when the benefits for cooperation are low.

 

Small groups and long memories promote cooperation
Alexander J. Stewart & Joshua B. Plotkin
Scientific Reports 6, Article number: 26889 (2016)
http://dx.doi.org/10.1038/srep26889


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Cooperation, competition and the emergence of criticality in communities of adaptive systems

The hypothesis that living systems can benefit from operating at the vicinity of critical points has gained momentum in recent years. Criticality may confer an optimal balance between too ordered and exceedingly noisy states. Here we present a model, based on information theory and statistical mechanics, illustrating how and why a community of agents aimed at understanding and communicating with each other converges to a globally coherent state in which all individuals are close to an internal critical state, i.e. at the borderline between order and disorder. We study—both analytically and computationally—the circumstances under which criticality is the best possible outcome of the dynamical process, confirming the convergence to critical points under very generic conditions. Finally, we analyze the effect of cooperation (agents trying to enhance not only their fitness, but also that of other individuals) and competition (agents trying to improve their own fitness and to diminish those of competitors) within our setting. The conclusion is that, while competition fosters criticality, cooperation hinders it and can lead to more ordered or more disordered consensual outcomes.

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Combining complex networks and data mining: why and how

The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have be used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex networks metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

 

Combining complex networks and data mining: why and how
M. Zanin, D. Papo, P. A. Sousa, E. Menasalvas, A. Nicchi, E. Kubik, S. Boccaletti

http://arxiv.org/abs/1604.08816


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Control of complex networks requires both structure and dynamics

The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.

 

Control of complex networks requires both structure and dynamics
Alexander J. Gates & Luis M. Rocha
Scientific Reports 6, Article number: 24456 (2016)
http://dx.doi.org/10.1038/srep24456


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Major transitions in evolution and in technology: What they have in common and where they differ

What have technological and biological evolution in common? One feature has been mentioned just above: Technologies and the professions related to them have finite lifetimes like biological species. Efficiency and other economic criteria are crucial for the survival of technologies and play the same role as fitness does in biological evolution. Technologies form complex networks of mutual dependences just as the different species do in the food webs of ecosystems. One less obvious feature is the tinkering principle. Innovation builds upon already existing technologies and only rarely—in exceptional cases—starts from scratch. One of these exceptions, perhaps, was the introduction of electricity into society. Pre-human nature is an obligatory tinkerer and the tinkering principle was indeed formulated first in the context of biological evolution: Nature does not design with the eyes of an engineer, she works like a tinkerer. Biological evolution can only make use of entities that are already present in the population. Biological evolution does never start from scratch but existing functions are used in different combinations and in a different context. Common to technology and biological evolution is an optimization principle that concerns economic efficiency in the former and fitness in the sense of the number of progeny in the latter case: In case a technology produces the same goods more expensively it will run out of business as a variant within a population does when it has less offspring.

 

Major transitions in evolution and in technology: What they have in common and where they differ
Peter Schuster

Complexity

http://dx.doi.org/10.1002/cplx.21773


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Scientists make first direct detection of gravitational waves

Scientists make first direct detection of gravitational waves | Edgar Analytics & Complex Systems | Scoop.it
Almost 100 years ago today, Albert Einstein predicted the existence of gravitational waves — ripples in the fabric of space-time that are set off by extremely violent, cosmic cataclysms in the early universe. With his knowledge of the universe and the technology available in 1916, Einstein assumed that such ripples would be “vanishingly small” and nearly impossible to detect. The astronomical discoveries and technological advances over the past century have changed those prospects.
Now for the first time, scientists in the LIGO Scientific Collaboration — with a prominent role played by researchers at MIT and Caltech — have directly observed the ripples of gravitational waves in an instrument on Earth. In so doing, they have again dramatically confirmed Einstein’s theory of general relativity and opened up a new way in which to view the universe.

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Marcelo Errera's curator insight, February 11, 12:08 PM

It's a good example of the leaps theorists take when they make claims that make sense in theory, but it is hard to be proven or accepted at first. Likewise there are theories that seem obvious, but only after someone properly stated it.

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Emergence of Consensus in a Multi-Robot Network: from Abstract Models to Empirical Validation

Consensus dynamics in decentralised multiagent systems are subject to intense studies, and several different models have been proposed and analysed. Among these, the naming game stands out for its simplicity and applicability to a wide range of phenomena and applications, from semiotics to engineering. Despite the wide range of studies available, the implementation of theoretical models in real distributed systems is not always straightforward, as the physical platform imposes several constraints that may have a bearing on the consensus dynamics. In this paper, we investigate the effects of an implementation of the naming game for the kilobot robotic platform, in which we consider concurrent execution of games and physical interferences. Consensus dynamics are analysed in the light of the continuously evolving communication network created by the robots, highlighting how the different regimes crucially depend on the robot density and on their ability to spread widely in the experimental arena. We find that physical interferences reduce the benefits resulting from robot mobility in terms of consensus time, but also result in lower cognitive load for individual agents.

 

Emergence of Consensus in a Multi-Robot Network: from Abstract Models to Empirical Validation
Vito Trianni, Daniele De Simone, Andreagiovanni Reina, Andrea Baronchelli

http://arxiv.org/abs/1601.04952


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The physics of life

The physics of life | Edgar Analytics & Complex Systems | Scoop.it
From flocking birds to swarming molecules, physicists are seeking to understand 'active matter' — and looking for a fundamental theory of the living world.

 

http://www.nature.com/news/the-physics-of-life-1.19105


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Marcelo Errera's curator insight, January 13, 1:09 PM

Organization emerges naturally. One more manifestation of the constructal law.

 

By the way, soon to appear:

The Physics of Life: The Evolution of Everything 
by Adrian Bejan 
Link: http://amzn.com/1250078822

Francisco Restivo's curator insight, January 14, 6:29 PM

Living world is the real laboratory.

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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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