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"People want to see patterns in the world. It is how we evolved. We descended from those primates who were best at spotting the telltale pattern of a predator in the forest, or of food in the savannah. So important is this skill that we apply it everywhere, warranted or not."Benoît MandelbrotFractal Inventor, IBM Fellow Emeritus

ibmblr:

"People want to see patterns in the world. It is how we evolved. We descended from those primates who were best at spotting the telltale pattern of a predator in the forest, or of food in the savannah. So important is this skill that we apply it everywhere, warranted or not."

Benoît Mandelbrot
Fractal Inventor, IBM Fellow Emeritus

Scientific Data Has Become So Complex, We Have to Invent New Math to Deal With It - Wired Science
Simon DeDeo, a research fellow in applied mathematics and complex systems at the Santa Fe Institute, had a problem. He was collaborating on a new project analyzing 300 years’ worth of data from the archives of London’s Old Bailey, the central criminal court of England and Wales. Granted, there was clean data in the usual straightforward Excel spreadsheet format, including such variables as indictment, verdict, and sentence for each case. But there were also full court transcripts, containing some 10 million words recorded during just under 200,000 trials.
How the hell do you analyze that data?” DeDeo wondered. It wasn’t the size of the data set that was daunting; by big data standards, the size was quite manageable. It was the sheer complexity and lack of formal structure that posed a problem. This “big data” looked nothing like the kinds of traditional data sets the former physicist would have encountered earlier in his career, when the research paradigm involved forming a hypothesis, deciding precisely what one wished to measure, then building an apparatus to make that measurement as accurately as possible.

Scientific Data Has Become So Complex, We Have to Invent New Math to Deal With It - Wired Science

Simon DeDeo, a research fellow in applied mathematics and complex systems at the Santa Fe Institute, had a problem. He was collaborating on a new project analyzing 300 years’ worth of data from the archives of London’s Old Bailey, the central criminal court of England and Wales. Granted, there was clean data in the usual straightforward Excel spreadsheet format, including such variables as indictment, verdict, and sentence for each case. But there were also full court transcripts, containing some 10 million words recorded during just under 200,000 trials.

How the hell do you analyze that data?” DeDeo wondered. It wasn’t the size of the data set that was daunting; by big data standards, the size was quite manageable. It was the sheer complexity and lack of formal structure that posed a problem. This “big data” looked nothing like the kinds of traditional data sets the former physicist would have encountered earlier in his career, when the research paradigm involved forming a hypothesis, deciding precisely what one wished to measure, then building an apparatus to make that measurement as accurately as possible.

We especially need imagination in science. It is not all mathematics, nor all logic, but is somewhat beauty and poetry.

Astronomer Maria Mitchell (1818-1889), the first woman elected to the American Academy of Arts and Sciences.

Also see Robert Sapolsky on science and wonder and Richard Feynman on the cultural role of science.

( It’s Okay To Be Smart)

downtowncreator:

via upload.wikimedia.org
Clouds are not spheres, mountains are not cones, coastlines are not  circles, and bark is not smooth, nor does lightning travel in a straight  line.   —Benoît Mandelbrot, (1924-2010) in his introduction to The Fractal Geometry of Nature.

downtowncreator:

via upload.wikimedia.org

Clouds are not spheres, mountains are not cones, coastlines are not circles, and bark is not smooth, nor does lightning travel in a straight line.
  —Benoît Mandelbrot, (1924-2010) in his introduction to The Fractal Geometry of Nature.

First Habitable Exoplanet Could Be Discovered by May |  Wired.com
A new mathematical analysis predicts the first truly habitable exoplanet will show itself by early May 2011.
Well, more or less. “There is some wiggle room,” said Samuel Arbesman of the Harvard Institute for Quantitative Social Science, lead author of a new paper posted online and to be published in PLoS ONE October 4. His calculations predict a 50 percent probability that the  first habitable exoplanet will be discovered in May 2011, a 66 percent  chance by the end of 2013 and 75 percent chance by 2020.

First Habitable Exoplanet Could Be Discovered by May |  Wired.com

A new mathematical analysis predicts the first truly habitable exoplanet will show itself by early May 2011.

Well, more or less. “There is some wiggle room,” said Samuel Arbesman of the Harvard Institute for Quantitative Social Science, lead author of a new paper posted online and to be published in PLoS ONE October 4. His calculations predict a 50 percent probability that the first habitable exoplanet will be discovered in May 2011, a 66 percent chance by the end of 2013 and 75 percent chance by 2020.