Social behaviors are often contagious, spreading through a population as individuals imitate the decisions and choices of others. A variety of global phenomena, from innovation adoption to the emergence of social norms and political movements, arise as a result of people following a simple local rule, such as copy what others are doing. However, individuals often lack global knowledge of the behaviors of others and must estimate them from the observations of their friends' behaviors. In some cases, the structure of the underlying social network can dramatically skew an individual's local observations, making a behavior appear far more common locally than it is globally. We trace the origins of this phenomenon, which we call "the majority illusion," to the friendship paradox in social networks. As a result of this paradox, a behavior that is globally rare may be systematically overrepresented in the local neighborhoods of many people, i.e., among their friends. Thus, the "majority illusion" may facilitate the spread of social contagions in networks and also explain why systematic biases in social perceptions, for example, of risky behavior, arise. Using synthetic and real-world networks, we explore how the "majority illusion" depends on network structure and develop a statistical model to calculate its magnitude in a network.
The Majority Illusion in Social Networks Kristina Lerman, Xiaoran Yan, Xin-Zeng Wu
Filed Under: Capitalism, Competition, Free Markets, Human Action, Innovation, Market Process “Every device employed to bolster individual freedom must have as its chief purpose the impairment of the absoluteness of power.” —…
Margaret Wheatley – Paradigm Shifter, Author and Co-Founder of the Berkana Institute There is a misperception that people are motivated by competition. People are actually motivated by generosity and love.
Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave and Python. We present the principles behind the code design, and provide several examples to guide users
"JIDT: An information-theoretic toolkit for studying the dynamics of complex systems" Joseph T. Lizier, arXiv:1408.3270, 2014 http://arxiv.org/abs/1408.3270
From his blog, Otto Scharmer, Senior Lecturer at MIT, and founding chair of the Presencing Institute writes about the value of Deep Data over Big Data:
"The one thing that I have learned from al...
Christine Capra's insight:
“The one thing that I have learned from all these projects is that the key to transformative change is to make the system see itself. That’s why deep data matters. It matters to the future of our institutions, our societies, and our planet.” Scharmer
And on how many levels do we humans still prefer our blind spots!
Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.
The three keystones necessary for the creation of a 21st Century Civic Infrastructure are: 1) Engaging all sectors; 2) Enlisting all voices; and 3) Building vertical and horizontal thoroughfares for information and practice exchange.
Sir Tim Berners-Lee invented the World Wide Web 25 years ago. So it’s worth a listen when he warns us: There’s a battle ahead. Eroding net neutrality, filter bubbles and centralizing corporate control all threaten the web’s wide-open spaces. It’s up to users to fight for the right to access and openness. The question is, What kind of Internet do we want?
Harold Jarche features Chee Chin Liew’s presentation on moving from hierarchies to teams at BASF. It shows how IT Services used their technology platforms to enhance networking, knowledge-sharing, and collaboration.
It features an approach to “building flows of information into pertinent, useful and just-in-time knowledge” so that... knowledge can flow in order to foster trust and credibility.
In complex environments, weak hierarchies and strong networks are the best organizing principle. ...It means giving up control.
Creating this two-way flow of dialogue, practice, expertise, and interest, can be the foundation of a wirearchy.
In complex environments, weak hierarchies and strong networks are the best organizing principle.
....many companies today have strong networks...coupled with strong central control. Becoming a wirearchy requires new organizational structures that incorporate communities, networks, and cooperative behaviours. It means giving up control. The job of those in leaderships roles is to help the network make better decisions.
Related tools & posts by Deb:
See the companion post about Holacracy, here.
Stay in touch with Best of the Best news, taken from Deb's NINE multi-gold award winning curation streams from @Deb Nystrom, REVELN delivered once a month via email, available for free here, via REVELN Tools. Beyond Resilience: Black Swans, Anti-Fragility and Change
Beyond Resilience: Givers, Takers, Matchers and Anti-Fragile Systems
Co-Creation in Theory U: Leading from the Future as it Emerges & the Road to Commitment
Brian Skyrms presents eighteen essays which apply adaptive dynamics (of cultural evolution and individual learning) to social theory. Altruism, spite, fairness, trust, division of labor, and signaling are treated from this perspective. Correlation is seen to be of fundamental importance. Interactions with neighbors in space, on static networks, and on co-evolving dynamics networks are investigated. Spontaneous emergence of social structure and of signaling systems are examined in the context of learning dynamics.
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.