"In this book, I suggest that to understand cities we must view them not simply as places in space but as systems of networks and flows. To understand space, we must understand flows, and to understand flows, we must understand networks—the relations between objects that comprise the system of the city. Drawing on the complexity sciences, social physics, urban economics, transportation theory, regional science, and urban geography, , I introduce theories and methods that reveal the deep structure of how cities function. (...)" Michael Batty
Swarm intelligence is a relatively new discipline that deals with the study of self-organizing processes both in nature and in artificial systems. Researchers in ethology and animal behavior have proposed many models to explain interesting aspects of social insect behavior such as self-organization and shape-formation. Recently, algorithms inspired by these models have been proposed to solve difficult computational problems. An example of a particularly successful research direction in swarm intelligence is ant colony optimization, the main focus of which is on discrete optimization problems. Ant colony optimization has been applied successfully to a large number of difficult discrete optimization problems including the traveling salesman problem, the quadratic assignment problem, scheduling, vehicle routing, etc., as well as to routing in telecommunication networks. Another interesting approach is that of particle swarm optimization, that focuses on continuous optimization problems. Here too, a number of successful applications can be found in the recent literature. Swarm robotics is another relevant field. Here, the focus is on applying swarm intelligence techniques to the control of large groups of cooperating autonomous robots.
ANTS 2014 will give researchers in swarm intelligence the opportunity to meet, to present their latest research, and to discuss current developments and applications.
Time evolution equations for dynamical systems can often be derived from generating functionals. Examples are Newton's equations of motion in classical dynamics which can be generated within the Lagrange or the Hamiltonian formalism. We propose that generating functionals for self-organizing complex systems offer several advantages. Generating functionals allow to formulate complex dynamical systems systematically and the results obtained are typically valid for classes of complex systems, as defined by the type of their respective generating functionals. The generated dynamical systems tend, in addition, to be minimal, containing only few free and undetermined parameters. We point out that two or more generating functionals may be used to define a complex system and that multiple generating function may not, and should not, be combined into a single overall objective function. We provide and discuss examples in terms of adapting neural networks.
Nowadays, any organization should employ network scientists/analysts who are able to map and analyse complex systems that are of importance to the organization (e.g. the organization itself, its activities, a country’s economic activities, transportation networks, research networks).Interconnectivity is beneficial but also brings in vulnerability: if you and I are connected we can share resources; meanwhile your problems can become mine and vice versa.The concept of “crystallized imagination” refers to things that are first in our head and then become reality. This concept can be turned into network applied research on economic complexity of a country’s economic activities and development prospects.
SOCIAL networks stand accused of being enemies of productivity. According to one popular (if questionable) infographiccirculating online, the use of Facebook, Twitter and other such sites at work costs the American economy $650 billion each year. Our attention spans are atrophying, our test scores declining, all because of these “weapons of mass distraction.”
Yet such worries have arisen before. In England in the late 1600s, very similar concerns were expressed about another new media-sharing environment, the allure of which seemed to be undermining young people’s ability to concentrate on their studies or their work: the coffeehouse. It was the social-networking site of its day.
Like coffee itself, coffeehouses were an import from the Arab world. England’s first coffeehouse opened in Oxford in the early 1650s, and hundreds of similar establishments sprang up in London and other cities in the following years. People went to coffeehouses not just to drink coffee, but to read and discuss the latest pamphlets and news-sheets and to catch up on rumor and gossip.
Coffeehouses were also used as post offices. Patrons would visit their favorite coffeehouses several times a day to check for new mail, catch up on the news and talk to other coffee drinkers, both friends and strangers. Some coffeehouses specialized in discussion of particular topics, like science, politics, literature or shipping. As customers moved from one to the other, information circulated with them.
Telling the story of Fractal Geometry, till its usage in complexity theory, very nicely done.
For centuries, fractal-like irregular shapes were considered beyond the boundaries of mathematical understanding. Now, mathematicians have finally begun mapping this uncharted territory. Their remarkable findings are deepening our understanding of nature and stimulating a new wave of scientific, medical, and artistic innovation stretching from the ecology of the rain forest to fashion design. The documentary highlights a host of filmmakers, fashion designers, physicians, and researchers who are using fractal geometry to innovate and inspire.
It all starts with a single octahedron structure, then after four iterations there are already 625 of them. Each iteration creates a new octahedron at each vertex. The result is a fascinating 3D fractal construction on micro and ...
In all the books and research papers on systems thinking that I have read, I don't think I have yet found the word courage as part of the language used. There is a lot written about systems thinking in terms of it's relevance and importance, it's theories and methodologies, but nothing about what it takes--emotionally. And I'm convinced: systems thinking not only requires skill, it also takes courage.
Archaeological and ethnohistorical evidence suggests a link between a population's size and structure, and the diversity or sophistication of its toolkits or technologies. Addressing these patterns, several evolutionary models predict that both the size and social interconnectedness of populations can contribute to the complexity of its cultural repertoire. Some models also predict that a sudden loss of sociality or of population will result in subsequent losses of useful kills/technologies. Here, we test these predictions with two experiments that permit learners to access either one or five models (teachers). Experiment 1 demonstrates that naive participants who could observe five models, integrate this information and generate increasingly effective skills (using an image editing tool) over 10 laboratory generations, whereas those with access to only one model show no improvement. Experiment 2, which began with a generation of trained experts, shows how learners with access to only one model lose skills (in knot-tying) more rapidly than those with access to five models. In the final generation of both experiments, all participants with access to five models demonstrate superior skills to those with access to only one model. These results support theoretical predictions linking sociality to cumulative cultural evolution.
We know that the human brain is a powerful organ, but many of us aren't aware of how much the mind is truly capable of -- and much more powerful it can become through deliberate training. By exercising the brain (yes, you can use repetition and habit as you do when you exercise the body), we can achieve what may have previously seemed nearly impossible.
A multitude of studies have linked meditation with both physical and mental health benefits, from reduced depression and anxiety to improved immune system functioning. And thanks to a line of research that looks at the brain power of of Buddhist monks -- who have devoted their lives to the practice of meditation, compassion and non-attachment -- we now know that the brain changes that result from years of mindfulness practices can be staggering.
Cooperation is essential for successful human societies. Thus, understanding how cooperative and selfish behaviors spread from person to person is a topic of theoretical and practical importance. Previous laboratory experiments provide clear evidence of social contagion in the domain of cooperation, both in fixed networks and in randomly shuffled networks, but leave open the possibility of asymmetries in the spread of cooperative and selfish behaviors. Additionally, many real human interaction structures are dynamic: we often have control over whom we interact with. Dynamic networks may differ importantly in the goals and strategic considerations they promote, and thus the question of how cooperative and selfish behaviors spread in dynamic networks remains open. Here, we address these questions with data from a social dilemma laboratory experiment. We measure the contagion of both cooperative and selfish behavior over time across three different network structures that vary in the extent to which they afford individuals control over their network ties. We find that in relatively fixed networks, both cooperative and selfish behaviors are contagious. In contrast, in more dynamic networks, selfish behavior is contagious, but cooperative behavior is not: subjects are fairly likely to switch to cooperation regardless of the behavior of their neighbors. We hypothesize that this insensitivity to the behavior of neighbors in dynamic networks is the result of subjects’ desire to attract new cooperative partners: even if many of one’s current neighbors are defectors, it may still make sense to switch to cooperation. We further hypothesize that selfishness remains contagious in dynamic networks because of the well-documented willingness of cooperators to retaliate against selfishness, even when doing so is costly. These results shed light on the contagion of cooperative behavior in fixed and fluid networks, and have implications for influence-based interventions aiming at increasing cooperative behavior.
This two-and-a-half day introductory course focuses on the science of networks: a new field that studies common principles of complex networks across disciplines. Social and economic networks, food webs, the World Wide Web, and the power grid are examples of the kinds of systems that network science seeks to understand. In this course, taught by prominent Santa Fe Institute faculty and associates, you will learn the basic concepts and tools of this new science, and see several case studies of their application in diverse areas. You will also have the opportunity for discussion with the faculty and other participants about applications within your own areas of interest. You will come away with an understanding and appreciation of the importance of network science for biology, ecology, economics, business, human health, social life, and other pursuits.
This self-contained book systematically explores the statistical dynamics on and of complex networks with a special focus on time-varying networks. In the constantly changing modern world, there is an urgent need to understand ...
Scientists have derived a series of mathematical formulas that describe how cities' properties vary in relation to their population size, and then posits a novel unified, quantitative framework for understanding how cities function and grow.