Venezuelan economist Ricardo Hausmann and Chilean physicist César Hidalgo, in a joint effort of Harvard University and the Massachutes Institute of Technology MIT, draw a new world map of economic adventure, and suggest the Earth may not be flat.
The new science of complex systems will be at the heart of the future of the Worldwide Knowledge Society. It is providing radical new ways of understanding the physical, biological, ecological, and techno-social universe. Complex Systems are open, value-laden, multi-level, multi-component, reconfigurable systems of systems, situated in turbulent, unstable, and changing environments. They evolve, adapt and transform through internal and external dynamic interactions. They are the source of very difficult scientific challenges for observing, understanding, reconstructing and predicting their multi-scale dynamics. The challenges posed by the multi-scale modelling of both natural and artificial adaptive complex systems can only be met with radically new collective strategies for research and teaching (...)
It is commonly believed that information spreads between individuals like a pathogen, with each exposure by an informed friend potentially resulting in a naive individual becoming infected. However, empirical studies of social media suggest that individual response to repeated exposure to information is significantly more complex than the prediction of the pathogen model. As a proxy for intervention experiments, we compare user responses to multiple exposures on two different social media sites, Twitter and Digg. We show that the position of the exposing messages on the user-interface strongly affects social contagion. Accounting for this visibility significantly simplifies the dynamics of social contagion. The likelihood an individual will spread information increases monotonically with exposure, while explicit feedback about how many friends have previously spread it increases the likelihood of a response. We apply our model to real-time forecasting of user behavior.
The Simple Rules of Social Contagion Nathan O. Hodas, Kristina Lerman
Controlling self-organizing systems is challenging because the system responds to the controller. Here, we develop a model that captures the essential self-organizing mechanisms of Bak-Tang-Wiesenfeld (BTW) sandpiles on networks, a self-organized critical (SOC) system. This model enables studying a simple control scheme that determines the frequency of cascades and that shapes systemic risk. We show that optimal strategies exist for generic cost functions and that controlling a subcritical system may drive it to criticality. This approach could enable controlling other self-organizing systems.
Controlling Self-Organizing Dynamics on Networks Using Models that Self-Organize
Pierre-André Noël, Charles D. Brummitt, and Raissa M. D’Souza
Have you ever wondered where you or your children may be living in 2050? Experts predict that by then three-quarters of the world's population will live in cities. This August and September the BBC is taking a look at how our lives will be changed by the technological innovations being developed for Tomorrow’s Cities.
Global environmental change is affecting species distribution and their interactions with other species. In particular, the main drivers of environmental change strongly affect the strength of interspecific interactions with considerable consequences to biodiversity. However, extrapolating the effects observed on pair-wise interactions to entire ecological networks is challenging. Here we propose a framework to estimate the tolerance to changes in the strength of mutualistic interaction that species in mutualistic networks can sustain before becoming extinct. We identify the scenarios where generalist species can be the least tolerant. We show that the least tolerant species across different scenarios do not appear to have uniquely common characteristics. Species tolerance is extremely sensitive to the direction of change in the strength of mutualistic interaction, as well as to the observed mutualistic trade-offs between the number of partners and the strength of the interactions.
Estimating the tolerance of species to the effects of global environmental change Serguei Saavedra, Rudolf P. Rohr, Vasilis Dakos, Jordi Bascompte
The focused organization theory of social ties proposes that the structure of human social networks can be arranged around extra-network foci, which can include shared physical spaces such as homes, workplaces, restaurants, and so on. Until now, this has been difficult to investigate on a large scale, but the huge volume of data available from online location-based social services now makes it possible to examine the friendships and mobility of many thousands of people, and to investigate the relationship between meetings at places and the structure of the social network. In this paper, we analyze a large dataset from Foursquare, the most popular online location-based social network. We examine the properties of city-based social networks, finding that they have common structural properties, and that the category of place where two people meet has very strong influence on the likelihood of their being friends. Inspired by these observations in combination with the focused organization theory, we then present a model to generate city-level social networks, and show that it produces networks with the structural properties seen in empirical data.
A place-focused model for social networks in cities Chloë Brown, Anastasios Noulas, Cecilia Mascolo, Vincent Blondel
The hypothesis of a Hierarchy of the Sciences, first formulated in the 19th century, predicts that, moving from simple and general phenomena (e.g. particle dynamics) to complex and particular (e.g. human behaviour), researchers lose ability to reach theoretical and methodological consensus. This hypothesis places each field of research along a continuum of complexity and “softness”, with profound implications for our understanding of scientific knowledge. Today, however, the idea is still unproven and philosophically overlooked, too often confused with simplistic dichotomies that contrast natural and social sciences, or science and the humanities. Empirical tests of the hypothesis have usually compared few fields and this, combined with other limitations, makes their results contradictory and inconclusive. We verified whether discipline characteristics reflect a hierarchy, a dichotomy or neither, by sampling nearly 29,000 papers published contemporaneously in 12 disciplines and measuring a set of parameters hypothesised to reflect theoretical and methodological consensus. The biological sciences had in most cases intermediate values between the physical and the social, with bio-molecular disciplines appearing harder than zoology, botany or ecology. In multivariable analyses, most of these parameters were independent predictors of the hierarchy, even when mathematics and the humanities were included. These results support a “gradualist” view of scientific knowledge, suggesting that the Hierarchy of the Sciences provides the best rational framework to understand disciplines' diversity. A deeper grasp of the relationship between subject matter's complexity and consensus could have profound implications for how we interpret, publish, popularize and administer scientific research.
LE NOSTRE AZIONI SONO COME TEMPESTE Le nostre azioni corrispondono alle modalità di propagazione delle tempeste, delle epidemie, delle sommosse: a periodi di quiete, persino di stasi, si alternano ...
Marinella De Simone's insight:
Il classico approccio di organizzazione del lavoro, ad esempio, prevede tradizionalmente lo schema: “First in, first out” o “First come, first served”: il primo della fila nei compiti da eseguire è il primo che viene svolto. Questo schema, agevole nei sistemi meccanizzati, non lo è affatto nei comportamenti umani. Noi prendiamo decisioni nelle modalità di svolgimento dei compiti assegnati – laddove abbiamo possibilità di decidere in autonomia – molto meno lineari: alcuni compiti da svolgere possono aspettare tempi lunghissimi, per poi essere svolti tutti insieme in un lasso di tempo estremamente breve e concentrato.
Questa nuova prospettiva, fondata sull’analisi della dinamica dei comportamenti umani (e non solo) sarebbe molto importante applicarla ad esempio nell’analisi del decision making che le persone applicano in ambito lavorativo sulla priorità dei compiti da svolgere, poiché un’organizzazione dei tempi di lavoro che non ne tenga conto si scontra necessariamente con le modalità a noi più naturali di agire nel vivere quotidiano.
In most social and information systems the activity of agents generates rapidly evolving time-varying networks. The temporal variation in networks' connectivity patterns and the ongoing dynamic processes are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset and find a simple statistical law that characterize the temporal evolution of users' egocentric networks. We encode this observation in a reinforcement process defining a time-varying network model that exhibits the emergence of strong and weak ties. We study the effect of time-varying and heterogeneous interactions on the classic rumour spreading model in both synthetic, and real-world networks. We observe that strong ties severely inhibit information diffusion by confining the spreading process among agents with recurrent communication patterns. This provides the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks.
In a sort of biological "spooky action at a distance," water in a cell slows down in the tightest confines between proteins and develops the ability to affect other proteins much farther away, University of Michigan researchers have discovered.
On a fundamental level, the findings show some of the complex and unexpected ways that water behaves inside cells. In a practical sense, they could provide insights into how and why proteins clump together in diseases such as Alzheimer's and Parkinson's. Understanding how proteins aggregate could help researchers figure out how to prevent them from doing so.
In engineering, uncertainty is usually as welcome as sand in a salad. The development of digital technologies, from the alphabet to the DVD, has been driven in large part by the desire to eliminate random fluctuations, or noise, inherent in analog systems like speech or VHS tapes. But randomness also has a special ability to make some systems work better. Here are five cases where a little chaos is a critical part of the plan (...)
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.
Self-organization of heterogeneous particle swarms is rich in its dynamics but hard to design in a traditional top-down manner, especially when many types of kinetically distinct particles are involved. In this chapter, we discuss how we have been addressing this problem by (1) utilizing and enhancing interactive evolutionary design methods and (2) realizing spontaneous evolution of self organizing swarms within an artificial ecosystem.
Guiding Designs of Self-Organizing Swarms: Interactive and Automated Approaches Hiroki Sayama
Understanding of the mechanisms driving our daily face-to-face encounters is still limited; the field lacks large-scale datasets describing both individual behaviors and their collective interactions. However, here, with the help of travel smart card data, we uncover such encounter mechanisms and structures by constructing a time-resolved in-vehicle social encounter network on public buses in a city (about 5 million residents). Using a population scale dataset, we find physical encounters display reproducible temporal patterns, indicating that repeated encounters are regular and identical. On an individual scale, we find that collective regularities dominate distinct encounters’ bounded nature. An individual’s encounter capability is rooted in his/her daily behavioral regularity, explaining the emergence of “familiar strangers” in daily life. Strikingly, we find individuals with repeated encounters are not grouped into small communities, but become strongly connected over time, resulting in a large, but imperceptible, small-world contact network or “structure of co-presence” across the whole metropolitan area. Revealing the encounter pattern and identifying this large-scale contact network are crucial to understanding the dynamics in patterns of social acquaintances, collective human behaviors, and—particularly—disclosing the impact of human behavior on various diffusion/spreading processes.
Understanding metropolitan patterns of daily encounters Lijun Sun, Kay W. Axhausen, Der-Horng Lee, and Xianfeng Huang
It is widely believed that theory is useful in physics because it describes simple systems and that strictly empirical phenomenological approaches are necessary for complex biological and social systems. Here we prove based upon an analysis of the information that can be obtained from experimental observations that theory is even more essential in the understanding of complex systems. Implications of this proof revise the general understanding of how we can understand complex systems including the behaviorist approach to human behavior, problems with testing engineered systems, and medical experimentation for evaluating treatments and the FDA approval of medications. Each of these approaches are inherently limited in their ability to characterize real world systems due to the large number of conditions that can affect their behavior. Models are necessary as they can help to characterize behavior without requiring observations for all possible conditions. The testing of models by empirical observations enhances the utility of those observations. For systems for which adequate models have not been developed, or are not practical, the limitations of empirical testing lead to uncertainty in our knowledge and risks in individual, organizational and social policy decisions. These risks should be recognized and inform our decisions.
The Limits of Phenomenology: From Behaviorism to Drug Testing and Engineering Design Yaneer Bar-Yam
Cooperation among species tends to result in mutualistic networks with a nested structure, which is thought to increase biodiversity and persistence but may be less stable than unstructured networks: here nested networks are shown to result from a mechanism that maximizes species abundances in mutualistic communities, and the abundance of nested species is found to be directly linked to the resilience of the community.
Emergence of structural and dynamical properties of ecological mutualistic networks Samir Suweis, Filippo Simini, Jayanth R. Banavar & Amos Maritan