L'opinione predominante, condivisa dalla maggioranza delle persone, emerge rapidamente su Twitter, qualunque sia l'argomento, e una volta stabilizzata difficilmente può cambiare. Lo ha scoperto una nuova analisi automatizzata, che potrebbe essere utilizzata per prevedere - ma forse anche per influenzare - come si orienterà l'opinione pubblica
Marinella De Simone's insight:
i dati mostrano che mentre all'inizio le opinioni su un argomento fluttuano notevolmente, questa variabilità si attenua molto in fretta, stabilizzandosi su un'opinione di maggioranza, largamente condivisa, che prevale nettamente sull'altra.
Nafeez Ahmed: Natural and social scientists develop new model of how 'perfect storm' of crises could unravel global system
Marinella De Simone's insight:
A new study sponsored by Nasa's Goddard Space Flight Center has highlighted the prospect that global industrial civilisation could collapse in coming decades due to unsustainable resource exploitation and increasingly unequal wealth distribution.
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
Opinion exchange models aim to describe the process of public opinion formation, seeking to uncover the intrinsic mechanism in social systems; however, the model results are seldom empirically justified using large-scale actual data. Online social media provide an abundance of data on opinion interaction, but the question of whether opinion models are suitable for characterizing opinion formation on social media still requires exploration. We collect a large amount of user interaction information from an actual social network, i.e., Twitter, and analyze the dynamic sentiments of users about different topics to investigate realistic opinion evolution. We find two nontrivial results from these data. First, public opinion often evolves to an ordered state in which one opinion predominates, but not to complete consensus. Second, agents are reluctant to change their opinions, and the distribution of the number of individual opinion changes follows a power law. Then, we suggest a model in which agents take external actions to express their internal opinions according to their activity. Conversely, individual actions can influence the activity and opinions of neighbors. The probability that an agent changes its opinion depends nonlinearly on the fraction of opponents who have taken an action. Simulation results show user action patterns and the evolution of public opinion in the model coincide with the empirical data. For different nonlinear parameters, the system may approach different regimes. A large decay in individual activity slows down the dynamics, but causes more ordering in the system.
Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.
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
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