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George Whitesides: Toward a science of simplicity

George Whitesides: Toward a science of simplicity
Related:  Complex Systems

The human microbiome: Me, myself, us WHAT’S a man? Or, indeed, a woman? Biologically, the answer might seem obvious. A human being is an individual who has grown from a fertilised egg which contained genes from both father and mother. A growing band of biologists, however, think this definition incomplete. They see people not just as individuals, but also as ecosystems. 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.

The Simplex Process - Problem Solving Training from MindTools A Robust Creative Problem-Solving Process Work through the cycle. © iStockphoto/centyr When you're solving business problems, it's all-too-easy easy to skip over important steps in the problem-solving process, meaning that you can miss good solutions, or, worse still, fail to identify the problem correctly in the first place. One way to prevent this happening is by using the Simplex Process. This powerful step-by-step tool helps you identify and solve problems creatively and effectively. In this article, we'll look at each step of the Simplex Process. About the Tool The Simplex Process was created by Min Basadur, and was popularized in his book, "The Power of Innovation." It is suitable for problems and projects of any scale. Figure 1: The Simplex Process Rather than seeing problem-solving as a single straight-line process, Simplex is represented as a continuous cycle. This means that problem-solving should not stop once a solution has been implemented. 1. Problems may be obvious. 2. 3. Tip:

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.

Embrasser la complexité Par Hubert Guillaud le 16/07/10 | 4 commentaires | 6,069 lectures | Impression La complexité n’est-elle pas devenue une caractéristique de nos sociétés, plutôt qu’un bug ? Comment pourrions-nous regagner le contrôle de nos flots d’information, de notre temps ? Visualiser la complexité Sommes-nous en train de découvrir une nouvelle vision du monde, aussi différente de la vision mécanique newtonienne du réel, que celle-ci le fut de la vision aristotélicienne qui domina tout au long du Moyen-Age ? Image : Manuel Lima sur la scène du théâtre de la Criée à Marseille, photographié par Fabien Girardin. C’est dans le but de mieux comprendre cette révolution de la complexité que le designer Manuel Lima a créé le site Visual Complexity. Les 17e, 18e et 19e siècles, époque du triomphe de la mécanique newtonienne furent essentiellement consacrés à l’analyse de la simplicité. On y trouve des centaines de modèles. Comprendre la complexité des usages La connexion solution à la complexité ?

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.

Complicated VS Complex What’s the difference between sending a rocket to the moon and getting children to succeed in school? What’s the difference between a surgeon extracting a brain tumor and judge and jury deciding guilt or innocent for a person accused of murder? Answers: sending a rocket to the moon and surgeons extracting brain tumors are complicated tasks while getting children to succeed in school (or, for that matter, raising a child) and the criminal justice system are complex. According to York University (Ontario, Canada) business professor Brenda Zimmerman, complicated procedures like brain surgery and rocket launchings require engineer-designed blueprints, step-by-step algorithms, well-trained staff, and exquisite combinations of computer software running carefully calibrated equipment. Think rocket landing on the moon in 1969, doctor-controlled robotic arms doing brain surgery, and the U.S. “shock and awe” invasion of Iraq in 2003. Like this: Like Loading...

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.

Complexity Complexity is generally used to characterize something with many parts where those parts interact with each other in multiple ways. The study of these complex linkages is the main goal of complex systems theory. In science,[1] there are at this time a number of approaches to characterizing complexity, many of which are reflected in this article. Overview[edit] Definitions of complexity often depend on the concept of a "system"—a set of parts or elements that have relationships among them differentiated from relationships with other elements outside the relational regime. The approaches that embody concepts of systems, multiple elements, multiple relational regimes, and state spaces might be summarized as implying that complexity arises from the number of distinguishable relational regimes (and their associated state spaces) in a defined system. Disorganized complexity vs. organized complexity[edit] Sources and factors of complexity[edit] Varied meanings of complexity[edit]

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. Simplexity Simplexity is an emerging theory that proposes a possible complementary relationship between complexity and simplicity. The term draws from General Systems Theory, Dialectics (philosophy) and Design. Jeffrey Kluger wrote a book about this phenomenon that describes how house plants can be more complicated than industrial plants, how a truck driver's job can be as difficult as a CEO's and why 90% of the money donated to help cure diseases are given only to the research of 10% of them (and vice versa). The term has been adopted in advertising, marketing and the manufacture of left-handed screwdrivers. Design aspects[edit] Complexity tends to rise as system elements specialize and diversify to solve specific challenges.Simple interfaces tend to improve the usability of complex systems. History of the term[edit] Like most terms, it has been shaped through dialogues and discussions, in much the same way that a camel is a horse designed by committee. Education[edit] In science[edit] References[edit]

12:38 simple things的三个特征 by ecoin Jun 14