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iPop: How Big Data Will Transform Coaching in the NBA - Bleacher Report

iPop: How Big Data Will Transform Coaching in the NBA - Bleacher Report | Edgar Analytics & Complex Systems | Scoop.it
Bleacher Report iPop: How Big Data Will Transform Coaching in the NBA Bleacher Report And so as the game becomes more data-centric, a strange possibility is creeping into view: Like mortgage appraising, high-frequency trading and assessing credit...
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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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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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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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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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