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Related:  Complex SystemsThéorie & réflexions autour de l'IC

The human microbiome: Me, myself, us WHAT’S a man? Or, indeed, a woman? Biologically, the answer might seem obvious. A healthy adult human harbours some 100 trillion bacteria in his gut alone. And it really is a system, for evolution has aligned the interests of host and bugs. That bacteria can cause disease is no revelation. A bug’s life One way to think of the microbiome is as an additional human organ, albeit a rather peculiar one. The microbiome, too, is organised. Specialised; but not monotonous. That detail is significant. This early nutritional role, moreover, is magnified throughout life. The fat of the land This role in nutrition points to one way in which an off-kilter microbiome can affect its host: what feeds a body can also overfeed or underfeed it. Experiments on mice suggest this is not just a question of the bacteria responding to altered circumstances. Having shown that gut bacteria are involved in obesity, Dr Gordon wondered if the converse was true. The diabetes in question is known as type-2.

Pour une critique de l’intelligence collective Texte initialement publié en avril 1996 L'intelligibilité va avec l'immatérialité. Thomas d'Aquin Il faut se rendre à l'évidence: nous vivons un véritable Cyber-Bang, aux conséquences imprévisibles. L'économie du virtuel commence à façonner en profondeur une nouvelle société, en accélérant la dématérialisation des flux, en augmentant les court-circuits informationnels, en restucturant les marchés du traitement de l'information, en généralisant la "désintermédiation", mais aussi en provoquant de nouvelles inégalités culturelles entre "info-riches" et "info-pauvres". A. 1. "Un mot est encore l'homme, deux mots sont déjà l'abîme" - dit Roberto Juarroz. Il y a cependant une différence radicale entre le langage naturel et la formalisation mathématique: les mathématiques favorisent l'induction et la généralisation. Il y a la raison qui se porte sur les essences en tant qu'elles sont connues, clarifiées, et la raison qui se porte sur les essences en tant qu'elles sont cachées, obscures. 2. 3. 4.

Programming Collective Intelligence  About me and why I read this book I've been programming professionally for ~7.5 years, mainly business applications and reporting, so I already have quite some love for data. While I haven't used math much in my day jobs, I liked (and was good at) it in high school, including taking extra classes - so I have learned basic statistics. About the book The book introduces lots of algorithms that can be used to gain new insight into any kind of data one might come across. Each of the algorithms is illustrated with real world application examples, and examples where applying them doesn't make sense are brought too. In addition to the well written, gradual introduction, the book has a concise algorithm reference at the end, so when one needs a quick refresher, there is no need to wade through the lengthy tutorials. Nonetheless, it is great to have actual code to play with, just the initial reading and reviewing of it requires some extra effort. The book was written in 2007, but is not dated.

Think Complexity by Allen B. Downey Buy this book from Download this book in PDF. Read this book online. Description This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science: Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. Complexity science is an interdisciplinary field---at the intersection of mathematics, computer science and physics---that focuses on these kinds of models. Free books! This book is under the Creative Commons Attribution-NonCommercial 3.0 Unported License, which means that you are free to copy, distribute, and modify it, as long as you attribute the work and don't use it for commercial purposes. Download the LaTeX source code (with figures and a Makefile) in a zip file.

Conduite du changement et utilisation de la courbe du deuil : PRUDENCE ! « EOMA Dans l’apprentissage des méthodes en conduite du changement, l’un des premiers points abordés est la "résistance" au changement. Pour illustrer ce processus de "résistance", puis celui de son acceptation, on utilise un schéma, une courbe nommée la "vallée de la peur" ou la" vallée des larmes". Il faut être extrêmement prudent quant à l’usage de cet "outil". En effet, cette courbe représente avant tout les phases du processus de deuil décrites par la psychiatre et psychologue américaine Elisabeth Kübler-Ross, qui a consacré son travail à l’accompagnement des personnes en soins palliatifs et à l’accompagnement de leurs proches. L’annonce de la mort proche provoque les réactions suivantes : Mais au sein de l’entreprise, avant de plaquer ce schéma sur les réactions présumées des collaborateurs à l’annonce d’un nouveau projet et des changements qu’il induit, il faut rappeler que le processus de deuil est un processus individuel et personnel. Clotilde Nicol Like this: J'aime chargement…

Programming Collective Intelligence: Building Smart Web 2.0 Applications - Toby Segaran - Google Books Intelligent Complex Adaptive Systems I don’t believe in the existence of a complex systems theory as such and, so far, I’m still referring to complex systems science (CSS) in order to describe my research endeavours. In my view, the latter is constituted, up until now, by a bundle of loosely connected methods and theories aiming to observe— from contrasted standpoints—these fascinating objects of research called complex adaptive systems. Nearly 40 years after Von Bertalanffy’s General System Theory (1968) and Jacques Monod’s Chance and Necessity (1971), it is fair to look back and to try to assess how much remains to be said about these complex adaptive systems. After all, Prigogine’s Order out of Chaos (1984) already demonstrated that future wasn’t entirely predictable in a history- contingent world. The universe is a massive system of systems -- for example, ecological systems, social systems, commodity and stock markets.

Gérer le changement à chaque phase traversée par les acteurs d’un projet Face au changement qu’entraîne le déploiement d’un nouvel outil informatique, l’individu est amené à traverser quatre étapes de transition. Selon l’individu, la durée de ces quatre phases sera plus ou moins importante et évoluera en fonction de facteurs internes et/ou externes au projet. Ainsi, afin d’adapter son message et sa stratégie de conduite du changement, le consultant en Système d’Information doit être capable de déterminer avec précision la phase par laquelle passe chacun des acteurs. Cela permet également de mettre en place un changement de manière continue et progressive tout en prenant en compte les différences de ressenti de chacun. L’étape de choc et de déni Elle entraîne un refus de voir et de prendre en compte l’arrivée du nouveau système d’information. Quelques craintes fréquentes : « Si l’informatique fait tout à ma place, je risque de perdre mon emploi » ou « Mes habitudes de travail vont être modifiées du jour au lendemain ! L’étape de révolte La phase d’exploration

Programming Collective Intelligence Book Description Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you’ve found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general — all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. “Bravo! Table of Contents Chapter 1. Appendix. Book Details

Complex systems made simple Albert-László Barabási and Yang-Yu Liu, together with their collaborator Jean-Jacques Slotine at M.I.T., have developed a method for observing large, complex systems. In the image above, red dots represent sensor nodes, which are required to reconstruct the entire internal state of one such system. Image by Mauro Martino. Just as the name implies, com­plex sys­tems are dif­fi­cult to tease apart. But that may not matter any­more. The approach takes advan­tage of the inter­de­pen­dent nature of com­plexity to devise a method for observing sys­tems that are oth­er­wise beyond quan­ti­ta­tive scrutiny. “Con­nect­ed­ness is the essence of com­plex sys­tems,” said Albert-​​László Barabási, one of the paper’s authors and a Dis­tin­guished Pro­fessor of Physics with joint appoint­ments in biology and the Col­lege of Com­puter and Infor­ma­tion Sci­ence. Using their novel approach, the researchers first iden­tify all the math­e­mat­ical equa­tions that describe the system’s dynamics.

Authentic Community Leadership Accepted Papers - Collective Intelligence 2012 Oral Presentations Visualizing collective discursive user interactions in online life science communities Dhiraj Murthy, Alexander Gross, and Stephanie Bond Group foraging in dynamic environments Michael E. Roberts, Sam Cheesman, and Patrick McMullen Markerless motion capture in the crowd Ian Spiro, Thomas Huston, and Christoph Bregler Tracking the 2011 student-led movement in chile through social media use M. Effects of social influence on the wisdom of crowds Pavlin Mavrodiev, Claudio J. Learning to predict the wisdom of crowds Seyda Ertekin, Haym Hirsh, and Cynthia Rudin What "crowdsourcing" obscures: exposing the dynamics of connected crowd work during disaster Kate Starbird Collaborative development in Wikipedia Gerald C. Crowdsourcing Gaze Data Collection Dmitry Rudoy, Dan B Goldman, Eli Shechtman, and Lihi Zelnik-Manor Analytic methods for optimizing realtime crowdsourcing Michael Bernstein, David R. Crowd & Prejudice Nicolas Della Penna and Mark D. Christopher Horsethief Benjamin Kuipers Lav R.

Observability of complex systems Author Affiliations Edited by Giorgio Parisi, University of Rome, Rome, Italy, and approved December 26, 2012 (received for review September 6, 2012) Abstract A quantitative description of a complex system is inherently limited by our ability to estimate the system’s internal state from experimentally accessible outputs. Footnotes Author contributions: Y.

L'exemple le plus abouti d'intelligence collective... by noosquest Sep 1