La fuerza de voluntad tiene poco que ver con una buena alimentación 

Me parece interesante lo que IdeasTED nos muestra sobre estudios realizados en hábitos de buena alimentación, arrojan que no necesariamente están ligados a la fuerza de voluntad, sino que dependen de factores que no habíamos contemplado.
Una oportunidad para reflexionar sobre la economía del comportamiento.

We know surprisingly little about what we want to eat, and why. Researcher Brian Wansink has been leading thousands of studies to understand more.

Two decades ago, Brian Wansink accidentally hit on a revelation that would change the course of his career. He and his graduate students (he was a professor at Wharton at the time) were running a study on sustainable food packaging. In the middle of giving free bags of snacks to Philadelphia moviegoers to see if they noticed they ate more from big packages, they ran out of big bags and had to revert to teeny ones that held just 110 calories. And here’s what they found: People with four tiny bags ate half as much as those with one big 440-calorie bag — and said they’d pay 20 percent more for snacks if companies sold them in smaller packages. “So here’s the punch line,” he says: “You can make more money by selling less food.”

Since then, Wansink — now a professor of applied economics and management at Cornell, director of theCornell Food and Brand Lab, and author of books including Slim by Design — has run 1,200 studies, by his estimate, on eating behaviors. It turns out we are utterly at the mercy of our environment.

We know far less about our own eating habits than we think. Wansink has found that we make many more decisions about food than we’re aware of — typically more than 200 per day. You don’t just choose whether to eat cereal or eggs, or even which cereal to eat. You also choose how much cereal to pour, how much milk to add, whether to finish the bowl, whether to have seconds. “There’s all these decisions that we don’t even code,” he says.

All of these decisions are subject to environmental cues. “Most people think they’re master and commander of their own diet,” Wansink says. But contrary to popular belief (and a whole lot of New Year’s resolutions), he’s found that healthy eating has very little to do with willpower. In one study, Wansink and his colleagues set up a table in which two out of four bowls of soup continually refilled from an apparatus hidden underneath the table. Participants given the self-refilling bowls ate 73 percent more, but didn’t feel any more sated. “We tend to eat with our eyes and not our stomach, because our stomach is a crude measure of how much we’ve eaten,” Wansink explains.

Size matters. Ever heard of the “small plate movement”? That’s all Wansink. In study after study, he has found that the bigger the plate, the more we eat. This stems from an optical illusion — the same amount of food on a bigger plate looks smaller — as well as changes in consumption norms and our decreased ability to estimate calories in larger portions. Eveneducating people about the effects of using a bigger plate has no effect on how much they eat. This goes for beverages, too: even professional bartenders pour moreinto a short, wide glass than a tall, skinny one of equal volume.

Color matters. Just as we can be tricked by the size of a plate, we can be tricked by its color. In one study, people who ate off plates the same color as their food (pasta with Alfredo sauce on a white plate, or with marinara sauce on a red plate) served themselves 22 percent more than those randomly assigned to color-contrast dining. “When a person serves themselves, they’re not even really looking at what they’re serving. It’s usually in the peripheral part of their vision,” Wansink says. “One thing about the periphery is if something isn’t high-contrast, it blurs even more into the background.”

Visibility matters. The more you see, the more you eat. We eat twice as many candies from a clear container than from an opaque one. But this phenomenon can work for you, too — if you put healthy foods in sight. Wansink and co. photographedover 200 American women’s kitchens and found that those who had cereal on the counter weighed 20 pounds more than those who didn’t and those who had soft drinks out weighed 24 to 26 pounds more — but women with even a single piece of fruit out weighed an average of 13 pounds less. “It’s a powerful testimony about our ‘see-food’ diet,” Wansink says — har, har.

Placement matters. Not only do we eat more candy out of a clear container, but we eat more when it’s close by: just moving the container from the desk to six feet away noticeably reduces the amount people eat. But there are subtler effects of placement, too. A study of 213 patrons of an all-you-can-eat buffet found that those with lower BMIs were likelier to sit facing away from the food. And it turns out that where you sit in a restaurant can really impact what you order: Wansink’sresearch indicates that people who sit farthest from the front door eat fewer salads and are much more likely to order dessert, while those sitting near a window or at a high-top table order more salads and fewer desserts — likely, Wansink believes, because they feel more on display.

Surroundings matter. Our susceptibility to cues other than hunger to determine how much we eat goes beyond food, serving implements and even our situation in the restaurant to the very room we eat in. Wansink and his colleague transformed half of a Hardees into a fine-dining restaurant by dimming the lights and playing softer, slower music, and found that diners ate less, more slowly, and with more satisfaction.

But most of us simply do not believe the environment holds such power over our eating habits. “We all believe we’re smarter than a bowl. We all believe we’re smarter than the plate in front of us,” Wansink says. But in truth, chalking it up to our environment can release a lot of guilt, and be a huge relief. “It’s easier to be slim by design than to be slim by willpower,” he says. If you tweak your environment, it “ends up being done once, and typically follows you for the rest of your life.”

Photo: Stocksy.

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Porqué los algoritmos serán las futuras estrellas del diseño

RATHER THAN USING SOFTWARE TO DRAW AND ANALYZE A DESIGNER’S VISION, AUTODESK WANTS TO USE COMPUTING POWER TO GENERATE THE IDEA ITSELF. WILL THIS RENDER DESIGNERS OBSOLETE?

The industrial designer of the future might just be a computer. The traditional design process can be a laborious one, full of iterations and tweaks to make a product just so. For years, design software makerAutodesk has been working on an alternative: a program that sorts through all the ways to make a product of specific measurements and then spits out the best option.

“In the past couple of years, we have experienced such an explosion of computing power that we can completely change the design equation,” says Autodesk chief technology officer Jeff Kowalski. Rather than using software to draw and analyze a designer’s vision, Autodesk wants to use that computing power to generate the idea itself—by running through a nearly infinite number of ways to build the same product.

Though still in the experimental phase, Autodesk has been working on computational design for the last seven years through research projects such as Project Dreamcatcher, a system that generates CAD geometry based on a list of functional requirements. This approach could prove especially effective in sectors where being light and strong are of the utmost importance—as with cars and airplanes, where extra pounds have a direct impact on fuel efficiency.

Here’s how it works. You type in a set of constraints for the product. Then an algorithm “will go through a nearly infinite set of results and head towards ones that are going to satisfy the designer,” Kowalksi explains. Say you want a chair that needs to support 200 pounds of weight and be two feet off the ground while using the least amount of material possible. The program might start off with 1,000 ways to make the same chair, and then intermix what it sees as the most effective components of those 1,000 chairs to make 1,000 new options. It can go through a million iterations of the same chair “in the time that it would take the designer to describe just one.” It can also suggest what type of material to use.

A computer isn’t constrained by preconceived notions of what a chair should look like. The example above, chosen from several designs the algorithm generated, has a curvy, slightly portly, webbed aesthetic, quite unlike our traditional conception of how a chair should look. Other options for making the same chair might include straighter legs, or just a single pedestal leg, but the algorithm is capable of generating a variety of different designs that all meet the required criteria.

It’s not a process that’s limited to making chairs. “This applies to buildings, bridges, automotive parts—everything we do where we have to draw things,” Kowalski explains. It could be used to design a lightweight underlying structure of, say, a bicycle, which could then be covered by a skin to give it a different look—so you’re not limited to the lattice aesthetic, even if that’s the most efficient material for that product.

Computational design isn’t aimed at replacing human insight. People are still better at making more subjective selections over issues such as aesthetics. But the program certainly does serve up a panoply of options and can help sort through some of the more tedious aspects of the design process.

[Images: Courtesy of Autodesk]

 

http://www.fastcodesign.com/3029756/why-algorithms-are-the-next-star-designers

8 actividades de emprendimiento que podemos enseñar a nuestros niños

8 entrepreneurial skills you should teach your kids [infographic]

Parents and guardians do many things to ensure the success of their charges, but after reading, ‘rithmetic, and not picking one’s nose in public, entrepreneurial skills can be tackled starting at a young age.

Here are a few lessons to get them off to a great start.

8-entrepreneurial-skills-you-should-teach-your-kids

Via Pumpic.

Educational infographics.

Posted by Kate Rinsema

Sobre el concepto de bricolage en evolución

Notas sobre el artículo  de Racine, Valerie, “Evolution and Tinkering” (1977), by Francois Jacob”. Embryo Project Encyclopedia (2014-10-24). ISSN: 1940-5030 http://embryo.asu.edu/handle/10776/8227.

“Evolution and Tinkering” (1977), by Francois Jacob

by Valerie Racine Keywords: bricolage, tinkering

 

In his essay “Evolution and Tinkering,” published in Science in 1977, François Jacob argues that a common analogy between the process of evolution by natural selection and the methods of engineering is problematic. Instead, he proposes to describe the process of evolution with the concept of bricolage (tinkering). In this essay, Jacob does not deny the importance of the mechanism of natural selection in shaping complex adaptations. Instead, he maintains that the cumulative effects of history on the evolution of life, made evident by molecular data, provides an alternative account of the patterns depicting the history of life on earth. Jacob’s essay contributed to genetic research in the late twentieth century that emphasized certain types of topics in evolutionary and developmental biology, such as genetic regulation, gene duplication events, and the genetic program of embryonic development. It also proposed why, in future research, biologists should expect to discover an underlying similarity in the molecular structure of genomes, and that they should expect to find many imperfections in evolutionary history despite the influence of natural selection.

The author of the article, François Jacob, studied enzyme expression and regulation in bacteria and bacteriophages at the Institut Pasteur in Paris, France. In 1965, Jacob won the Nobel Prize in Physiology or Medicine with André M. Lwoff and Jacques L. Monod for their work on the genetic control of enzyme and virus synthesis. At the Institut Pasteur, Jacob and his colleagues constructed a model of gene regulation according to which regulatory proteins in cells interact to switch on or off genes, and thus control physiological processes. They named this regulatory mechanism an operon, and hypothesized that it existed in the cells of all living organisms. Subsequent research confirmed their hypothesis. The ubiquity of regulation processes found at the molecular level led Jacob to consider its implications for evolutionary biology.

Jacob was also influenced by the work of Claude Lévi-Strauss, who was affiliated to Collège de France in Paris, France, and who applied the theoretical framework of structural linguistics to anthropology. Lévi-Strauss argued that researchers could analyze human communication through the various relations between the signifier and the signified and the changes in the relations between these units. In a similar way, Jacob said that the study of evolution could benefit from the analysis of the main units in molecular biology, such as the structural genes and the regulatory genes, and from the analysis of the changes in the relations between the units involved in the regulation of cell physiology. In his 1977 essay, “Evolution and Tinkering,” Jacob assimilated his knowledge of molecular biology into his philosophical ideas about the nature of science and scientific method.

The essay has ten sections. Jacob begins with an outline of his conception of the scientific worldview and the relationship between the natural and social sciences. In the first two sections, Jacob states that science is a human product that consists of a series of cultural attempts to delimit the possible by framing explanatory systems and bestowing unity and coherence upon the world. Like mythology, science attempts to explain the actual by delineating the possible, including the unknown or the invisible. Science, Jacob claims, can be differentiated from other cultural myths by its commitment to experimentation, and its ongoing process of criticism and revision. As such, science aims to provide only partial and provisional answers to questions about the world. The history of science, according to Jacob, depicts a pattern in which scientific knowledge begins as isolated pieces of knowledge in particular scientific domains, and develops into a unified account of phenomena.

In the third section, “The Hierarchy of Object,” Jacob addresses the challenges of studying objects, such as living organisms, human language and behaviour, and social and economic structures. Jacob argues against what he calls methodological reductionism, stating that it would be absurd to try to explain something complex, like democracy, by appealing to the structure and properties of its elementary physical particles. Nonetheless—Jacob notes—the laws that govern elementary physical particles constrain every higher level object of study, including political structures. Lower levels of the hierarchy of objects limit the range of possibilities for objects in higher levels.

In the fourth section, “Constraints and History,” Jacob states that most objects of scientific study are complex organizations or systems influenced by a combination of constraints and history. For instance, he argues that emergent properties of a system can be explained by appealing to the components of the system, but they cannot be deduced from them. In other words, one can not predict the emergent properties of complex systems, like cells’ and organisms’ properties, from the properties of their components. The complex nature of the objects of study constrains predictions. Thus, such objects require examination at more than one level of analysis. Furthermore, Jacob argues that, because complex objects can result from evolutionary processes, they are also constrained by history. For example, scientists have shown that the structure of a cell relies on its molecular elements and composition. However, Jacob notes, any evidence of these molecular elements in prebiotic time is not sufficient to explain the origin of life on earth. Historical conditions, including highly contingent events, have played a role in the origin of life.

In the next two sections, Jacob introduces and develops the metaphor of tinkering to bring into focus the historical character of evolutionary theory. Jacob begins, in the fifth section, by describing the process of natural selection as an imposition of constraints on open systems, or organisms. Natural selection, according to Jacob, is both a negative and a positive force. It is negative in the sense that it works to eliminate less fit variants in a population, and it is positive in the sense that it works to integrate mutations that accumulate over time to produce adaptations. Jacob states that natural selection‘s creative force is evident in its ability to recombine old material into novelties; new structures, new organs, and even new species.

In section six, “Evolution and Tinkering,” Jacob dismisses a comparison between natural selection and engineering for three reasons. First, unlike natural selection, an engineer works according to a pre-conceived plan of the final product. Second, an engineer actively chooses her materials and has access to the best tools designed for accomplishing the task at hand. Natural selection, in contrast, affects the structurally and functionally imperfect parts of the biotic world and reconfigures existing systems into novel ones. Third, if the engineer is successful, the final product achieves a level of perfection. Evolution by natural selection, however, yields imperfect products. For these three reasons, Jacob rejects an analogy between natural selection and engineering, and instead he proposes the metaphor that natural selection is like a bricoleur (tinkerer). Like natural selection, a tinkerer works with no specific end in mind, collecting any materials at his disposal, and rearranges them into a workable object. Thus, contingency constitutes the main feature of evolutionary processes.

Jacob further elaborates this analogy in the seventh section, “Evolution as Tinkering.” Different tinkerers, Jacob argues, likely develop different solutions to similar problems. For example, evolution has resulted in different types of eyes—pinhole, lens, and multiple tubes—to address the issue of how organisms use light to perceive the world. In these cases, natural selection used materials at its disposal to form differently-structured adaptations to similar problems. Here, Jacob underscores the claim that evolution never produces new forms from scratch.

Jacob argues that this tinkering characteristic of evolutionary processes is most evident at the molecular level. In section eight, “Molecular Tinkering,” he explains that all living organisms, both unicellular and multicellular, exhibit an underlying unity in their chemical structures and functions. Jacob states that, because all life shares the same organic molecules and similar metabolic pathways, it is more probable that new functional proteins have arisen from a rearrangement of genetic elements than it is that those proteins appeared anew. As evidence, Jacob cites the discovery that similar DNA sequences from organisms as distantly related as fruit flies and pigs help cause structures as different as wings and legs to develop.

To support his analogy, Jacob appeals to a hypothesis of Susumu Ohno’s, who worked at City of Hope Medical Center in Duarte, California. Ohno presented the hypothesis in 1970, and it is about the logic of gene duplication events in evolutionary history. When a gene gets copied or duplicated in a genome, the new gene lacks the functional constraints of the old gene. In such cases, the duplicated copy can accumulate beneficial or neutral mutations with little deleterious effect on the overall fitness of the organism. This accumulation can lead to the re-arrangement of genetic elements, so that existing structures can acquire new functions. This hypothesis of genomic change, Jacob argues, illustrates the process of tinkering.

According to Jacob, molecular biologists have shown that most morphological change in vertebrates has not resulted from new structural genes, but rather it is the consequence of a change in the regulation of genetic components, including events like heterotopy, a change in the spatial location of developmental events, and heterochrony, a change in the timing of developmental events. Jacob argues that these events occur in embryonic development according to the precise schedule of a genetic program, suggesting that gene regulation is the key factor in the generation of animals’ forms and functions.

In the ninth section, Jacob outlines two consequences of the metaphor of evolution as a process of tinkering. First, if his analogy holds, then biologists should expect to find similarities in the underlying molecular elements of different species. For example, Jacob argues, biochemists have discovered hormone peptides that trigger a variety of chemical reactions in cells from organisms in different species. Second, biologists should expect to see many imperfections or redundancies in the design of organisms. For instance, Jacob explains, the human reproductive system illustrates a less than perfect mechanism in which almost half of the total number of conceptions result in no viable fetuses.

Jacob ends his essay with a final example of tinkering, arguing that the human brain is a product of highly contingent, historical events. Jacob contends that, in humans, the addition of the neo-cortex to the rhinencephalon, a primitive part of the brain responsible for the sense of smell and theorized to control instinct, has set the conditions for the evolution of the human brain. The human brain is thus the result of an imperfect patchwork of a structure controlling visceral or emotional drives, the rhinencephalon, and a structure controlling more sophisticated cognitive abilities, the neo-cortex. This case can be extrapolated, Jacob argues, to a general rule for evolution: evolution is the net result of a particular sequence of historical opportunities.

Jacob’s essay had, at first, a mixed reception. Many biologists said that the description of evolution by natural selection as a process of tinkering was blatantly obvious. In 1983 Walter Gehring and his team at the University of Basel in Basel, Switzerland, discovered of a standard set of DNA sequences called the Homeobox in genes that controlled the embryonic development of body plans of animals. Scientists soon found the Homeobox in genes of diverse organisms from flies to humans. Given those results, scientists explicitly began referring to Jacob’s essay and to his concept of tinkering.

In 1982, Jacob published a series of lectures given at the University of Washington in Seattle, Washington, under the title The Possible and the Actual, which includes a slightly modified version of his original essay, “Evolution and Tinkering,” as well as some essays expounding his philosophy of science. In 2006, the Novartis Foundation in London, United Kingdom, held a symposium on the concept of tinkering in evolution and development. By the second decade of the twenty-first century, scientists had cited “Evolution and Tinkering” thousands of times.

Sources

  1. Artmann, Stefan. “Four Principles of Evolutionary Pragmatics in Jacob’s Philosophy of Modern Biology.” Axiomathes 14 (2004): 381–395.
  2. Carroll, Sean B. Endless Forms Most Beautiful. New York & London: W.W. Norton & Co., 2005.
  3. Jacob, François. “Evolution and Tinkering.” Science 196 (1977): 1161–1166.
  4. Jacob, François. The Possible and the Actual. Seattle: University of Washington Press, 1982.
  5. Laubichler, Manfred. “Tinkering: a Conceptual and Historical Evaluation.” In Tinkering: The Microevolution of Development, Novartis Foundation Symposium 284. New York: John Wiley, 2007: 20–34.
  6. Lieberman, Daniel E., and Brian K. Hall. “The evolutionary developmental biology of tinkering: an introduction to the challenge.” In Tinkering: The Microevolution of Development, Novartis Foundation Symposium 284. New York: John Wiley, 2007: 1–19.
  7. McGinnis, William, Michael, Levine, Ernst, Hafen, Atsushi, Kuroiwa, Walter, Gehring. “A conserved DNA sequence in homoeotic genes of the Drosophila Antennapedia and bithorax complexes.” Nature 308 (1984): 428–33.
  8. Morange, Michel. “French Tradition and the rise of Evo-devo.” Theory in Biosciences 126 (2007): 149–153.
  9. Nobel Prize Foundation. Nobel Prize in Medicine or Physiology 1965.http://www.nobelprize.org/nobel_prizes/medicine/laureates /1965/ (Accessed 16 March 2014).
  10. Ohno, Susumu. Evolution by Gene Duplication. New York: Springer-Verlag, 1970.

Subject:

Institut Pasteur (Paris, France);; Jacob, Francois, 1920-2013;; Operons;; Genetic regulation;; Nobel Prizes;; Gene Expression Regulation;; Evolution;; Natural selection;; Engineering;; Homeobox genes; Institut Pasteur (Paris, France);; Jacob, Francois, 1920-2013;; Operons;; Genetic regulation;; Nobel Prizes;; Gene Expression Regulation;; Evolution;; Natural selection;; Engineering;; Homeobox genes; Literature

How to Cite:

Racine, Valerie, “”Evolution and Tinkering” (1977), by Francois Jacob”. Embryo Project Encyclopedia (2014-10-24). ISSN: 1940-5030 http://embryo.asu.edu/handle/10776/8227.

Last Modified:

Friday, October 24, 2014 – 16:02

Publisher:

Arizona State University. School of Life Sciences. Center for Biology and Society. Embryo Project Encyclopedia.

Rights:

Copyright Arizona Board of Regents Licensed as Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported (CC BY-NC-SA 3.0) http://creativecommons.org/licenses/by-nc-sa/3.0/

– See more at: https://embryo.asu.edu/pages/evolution-and-tinkering-1977-francois-jacob#sthash.9wJjOc8l.dpuf

 

mas ligas

 

  • Molino, Jean (2000). “Toward an Evolutionary Theory of Music and Language”, The Origins of Music. Cambridge, Mass: A Bradford Book, The MIT Press. ISBN 0262232065.
  • Ciborra, C (1992). “From Thinking to Tinkering: The Grassroots of Strategic Information Systems”, The Information Society 8, 297-309
  • Curious George Brigade, Crimethinc, Co-Conspirators: DIY. Von Anarchie und Dinosauriern. Unrast, Münster 2006, ISBN 3-89771-444-2.
  • Kyle Bravo, Jenny LeBlanc: Making Stuff and Doing Things. Microcosm Publishing, Portland 2005, ISBN 0-9726967-9-2.

Definiciones de huerística

Buscando definiciones de heurística, me encontré esta Wiki “Epistemowikia”, un proyecto colaborativo interesante realizado por la Universidad de Extremadura.

les dejo las definiciones y la liga:

Huerística:

Ciencia que estudia los procesos de decisión respecto a un campo de conocimiento concreto, como son las estrategias cognitivas. Su contrapartida formal en computación es el algoritmo.

La palabra heurística proviene de la palabra griega heuriskein que significa descubrir, encontrar. Por heurística entendemos una estrategia, método, criterio o truco usado para hacer más sencilla la solución de problemas difíciles. El conocimiento heurístico es un tipo especial de conocimiento usado por los humanos para resolver problemas complejos. En este caso el adjetivo heurístico significa medio para descubrir.

Debido a la existencia de algunos problemas importantes con un gran interés práctico difíciles de resolver, comienzan a surgir algoritmos capaces de ofrecer posibles soluciones que aunque no consiguen el resultado óptimo, si que se acercan en un tiempo de cálculo razonable. Estos algoritmos están basados en el conocimiento heurístico y por lo tanto reciben el nombre de algoritmos heurísticos.

Por lo general, los algoritmos heurísticos encuentran buenas soluciones, aunque a veces no hay pruebas de que la solución pueda hallarse en un tiempo razonablemente corto o incluso de que no pueda ser errónea. Frecuentemente pueden encontrarse casos particulares del problema en los que la heurística obtendrá resultados muy malos o que tarde demasiado en encontrar una solución.

Un método heurístico es un conjunto de pasos que deben realizarse para identificar en el menor tiempo posible una solución de alta calidad para un determinado problema.

Al principio esta forma de resolver problemas no fue bien vista en los círculos académicos, debido fundamentalmente a su escaso rigor matemático. Sin embargo, gracias a su interés práctico para solucionar problemas reales fue abriendo poco a poco las puertas de los métodos heurísticos, sobre todo a partir de los años 60. Actualmente las versiones matemáticas de métodos heurísticos están creciendo en su rango de aplicaciones, así como en su variedad de enfoques.

Nuevas técnicas heurísticas son utilizadas a diario por científicos de computación, investigadores operativos y profesionales, para resolver problemas que antes eran demasiado complejos o grandes para las anteriores generaciones de este tipo de algoritmos.

http://web.archive.org/web/20100430143100/http://campusvirtual.unex.es/cala/epistemowikia/index.php?title=Heur%C3%ADstica