Methodology – Page 21 – artdiamondblog.com

Rather than Debate Global Warming Skeptics, Some Label them “Denialists” to “Link Them to Holocaust Denial”

(p. D2) The contrarian scientists like to present these upbeat scenarios as the only plausible outcomes from runaway emissions growth. Mainstream scientists see them as being the low end of a range of possible outcomes that includes an alarming high end, and they say the only way to reduce the risks is to reduce emissions.
The dissenting scientists have been called “lukewarmers” by some, for their view that Earth will warm only a little. That is a term Dr. Michaels embraces. “I think it’s wonderful!” he said. He is working on a book, “The Lukewarmers’ Manifesto.”
When they publish in scientific journals, presenting data and arguments to support their views, these contrarians are practicing science, and perhaps the “skeptic” label is applicable. But not all of them are eager to embrace it.
“As far as I can tell, skepticism involves doubts about a plausible proposition,” another of these scientists, Richard S. Lindzen, told an audience a few years ago. “I think current global warming alarm does not represent a plausible proposition.”
. . .
It is perhaps no surprise that many environmentalists have started to call them deniers.
The scientific dissenters object to that word, claiming it is a deliberate attempt to link them to Holocaust denial. Some academics sharply dispute having any such intention, but others have started using the slightly softer word “denialist” to make the same point without stirring complaints about evoking the Holocaust.

For the full commentary, see:
Justin Gillis. “BY DEGREES; Verbal Warming: Labels in the Climate Debate.” The New York Times (Tues., FEB. 17, 2015): D1-D2.
(Note: ellipsis added.)
(Note: the online version of the commentary has the date FEB. 12 (sic), 2015.)

“Big Data” Does Not Tell Us What to Measure, and Ignores What Cannot Be Measured

(p. 6) BIG data will save the world. How often have we heard that over the past couple of years? We’re pretty sure both of us have said something similar dozens of times in the past few months.
If you’re trying to build a self-driving car or detect whether a picture has a cat in it, big data is amazing. But here’s a secret: If you’re trying to make important decisions about your health, wealth or happiness, big data is not enough.
The problem is this: The things we can measure are never exactly what we care about. Just trying to get a single, easy-to-measure number higher and higher (or lower and lower) doesn’t actually help us make the right choice. For this reason, the key question isn’t “What did I measure?” but “What did I miss?”
. . .
So what can big data do to help us make big decisions? One of us, Alex, is a data scientist at Facebook. The other, Seth, is a former data scientist at Google. There is a special sauce necessary to making big data work: surveys and the judgment of humans — two seemingly old-fashioned approaches that we will call small data.
Facebook has tons of data on how people use its site. It’s easy to see whether a particular news feed story was liked, clicked, commented on or shared. But not one of these is a perfect proxy for more important questions: What was the experience like? Did the story connect you with your friends? Did it inform you about the world? Did it make you laugh?
(p. 7) To get to these measures, Facebook has to take an old-fashioned approach: asking. Every day, hundreds of individuals load their news feed and answer questions about the stories they see there. Big data (likes, clicks, comments) is supplemented by small data (“Do you want to see this post in your News Feed?”) and contextualized (“Why?”).
Big data in the form of behaviors and small data in the form of surveys complement each other and produce insights rather than simple metrics.
. . .
Because of this need for small data, Facebook’s data teams look different than you would guess. Facebook employs social psychologists, anthropologists and sociologists precisely to find what simple measures miss.
And it’s not just Silicon Valley firms that employ the power of small data. Baseball is often used as the quintessential story of data geeks, crunching huge data sets, replacing fallible human experts, like scouts. This story was made famous in both the book and the movie “Moneyball.”
But the true story is not that simple. For one thing, many teams ended up going overboard on data. It was easy to measure offense and pitching, so some organizations ended up underestimating the importance of defense, which is harder to measure. In fact, in his book “The Signal and the Noise,” Nate Silver of fivethirtyeight.com estimates that the Oakland A’s were giving up 8 to 10 wins per year in the mid-1990s because of their lousy defense.
. . .
Human experts can also help data analysts figure out what to look for. For decades, scouts have judged catchers based on their ability to frame pitches — to make the pitch appear more like a strike to a watching umpire. Thanks to improved data on pitch location, analysts have recently checked this hypothesis and confirmed that catchers differ significantly in this skill.

For the full commentary, see:
ALEX PEYSAKHOVICH and SETH STEPHENS-DAVIDOWITZ. “How Not to Drown in Numbers.” The New York Times, SundayReview Section (Sun., MAY 3, 2015): 6-7.
(Note: ellipses added.)
(Note: the online version of the commentary has the date MAY 2, 2015.)

Insights More Likely When Mood Is Positive and Distractions Few

If insights are more likely in the absence of distractions, then why are business executives so universally gung-ho on imposing on their workers the open office space layouts that are guaranteed to maximize distractions?

(p. C7) We can’t put a mathematician inside an fMRI machine and demand that she have a breakthrough over the course of 20 minutes or even an hour. These kinds of breakthroughs are too mercurial and rare to be subjected to experimentation.

We are, however, able to study the phenomenon more generally. Enter John Kounios and Mark Beeman, two cognitive neuroscientists and the authors of the “The Eureka Factor.” Messrs. Kounios and Beeman focus their book on the science behind insights and how to cultivate them.
As Mr. Irvine recognizes, studying insights in the lab is difficult. But it’s not impossible. Scientists have devised experiments that can provoke in subjects these kinds of insights, ones that feel genuine but occur on a much smaller scale.
. . .
The book includes some practical takeaways of how to improve our odds of getting insights as well. Blocking out distractions can create an environment conducive to insights. So can having a positive mood. While many of the suggestions contain caveats, as befits the delicate nature of creativity, ultimately it seems that there are ways to be more open to these moments of insight.

For the full review, see:
SAMUEL ARBESMAN. “Every Man an Archimedes; Insights can seem to appear spontaneously, but fully formed. No wonder the ancients spoke of muses.” The Wall Street Journal (Sat., May 23, 2015): C7.
(Note: ellipsis added.)
(Note: the online version of the review has the date May 22, 2015.)

The book under review, is:
Kounios, John, and Mark Beeman. The Eureka Factor: Aha Moments, Creative Insight, and the Brain. New York: Random House, 2015.

Physicists Accepting Theories Based on Elegance Rather than Evidence

(p. 5) Do physicists need empirical evidence to confirm their theories?
. . .
A few months ago in the journal Nature, two leading researchers, George Ellis and Joseph Silk, published a controversial piece called “Scientific Method: Defend the Integrity of Physics.” They criticized a newfound willingness among some scientists to explicitly set aside the need for experimental confirmation of today’s most ambitious cosmic theories — so long as those theories are “sufficiently elegant and explanatory.” Despite working at the cutting edge of knowledge, such scientists are, for Professors Ellis and Silk, “breaking with centuries of philosophical tradition of defining scientific knowledge as empirical.”
Whether or not you agree with them, the professors have identified a mounting concern in fundamental physics: Today, our most ambitious science can seem at odds with the empirical methodology that has historically given the field its credibility.

For the full commentary, see:
ADAM FRANK and MARCELO GLEISER. “Gray Matter; A Crisis at the Edge of Physics.” The New York Times, SundayReview Section (Sun., JUNE 7, 2015): 5.
(Note: ellipsis added.)
(Note: the date of the online version of the commentary is JUNE 5, 2015, and has the title “A Crisis at the Edge of Physics.”)

The controversial Nature article, mentioned above, is:
Ellis, George, and Joe Silk. “Scientific Method: Defend the Integrity of Physics.” Nature 516, no. 7531 (Dec. 18, 2014): 321-23.

Mathematician Says Mathematical Models Failed

The author of the commentary quoted below is a professor of mathematics at the Baltimore County campus of the University of Maryland.

(p. 4) . . . , in a fishery, the maximum proportion of a population earmarked each year for harvest must be set so that the population remains sustainable.

The math behind these formulas may be elegant, but applying them is more complicated. This is especially true for the Chesapeake blue crabs, which have mostly been in the doldrums for the past two decades. Harvest restrictions, even when scientifically calculated, are often vociferously opposed by fishermen. Fecundity and survival rates — so innocuous as algebraic symbols — can be difficult to estimate. For instance, it was long believed that a blue crab’s maximum life expectancy was eight years. This estimate was used, indirectly, to calculate crab mortality from fishing. Derided by watermen, the life expectancy turned out to be much too high; this had resulted in too many crab deaths being attributed to harvesting, thereby supporting charges of overfishing.
In fact, no aspect of the model is sacrosanct — tweaking its parameters is an essential part of the process. Dr. Thomas Miller, director of the Chesapeake Biological Laboratory at the University of Maryland Center for Environmental Science, did just that. He found that the most important factor for raising sustainability was the survival rate of pre-reproductive-age females. This was one reason, in 2008, after years of failed measures to increase the crab population, regulatory agencies switched to imposing restrictions primarily on the harvest of females. . . .
The results were encouraging: The estimated population rose to 396 million in 2009, from 293 million in 2008. By 2012, the population had jumped to 765 million, and the figure was announced at a popular crab house by Maryland’s former governor, Martin O’Malley, himself.
Unfortunately, the triumph was short-lived — the numbers plunged to 300 million the next year and then hit 297 million in 2014. Some blamed a fish called red drum for eating young crabs; others ascribed the crash to unusual weather patterns, or the loss of eel grass habitat. Although a definitive cause has yet to be identified, one thing is clear: Mathematical models failed to predict it.

For the full commentary, see:
Manil Suri. “Mathematicians and Blue Crabs.” The New York Times, SundayReview Section (Sun., MAY 3, 2015): 4.
(Note: ellipses added.)
(Note: the date of the online version of the commentary is MAY 2, 2015.)

A Highly Mathematical Model Endorses Friedman’s View that Feds Directed Economics toward Highly Mathematical Models

(p. 1138) . . . , in many areas, the existing organization of research is characterized by large research institutions staffed with hundreds of
researchers and national funding agencies who set the research agenda for the field. Given the size of such institutions, if they decide to launch a new research program, then the critical mass of scholars can be reached with certainty, and individual researchers need not fear the coordination risk. Researchers should thus choose to work on that research topic, provided that they perceive an expected reward that is larger than s. (p. 1139) Unfortunately, if the large institution selects a poor idea (with a small or even negative θ), it would then be responsible for the emergence of a strand of research with modest scientific value. As an example, Diamond (1996) recalls Milton Friedman’s criticism of the U.S. National Science Foundation, which, in his opinion, has directed the economics profession toward a highly mathematical model.12
. . .
12. Ironically, his opinion is endorsed in this paper by a “highly mathematical model.”

Source:
Besancenot, Damien, and Radu Vranceanu. “Fear of Novelty: A Model of Scientific Discovery with Strategic Uncertainty.” Economic Inquiry 53, no. 2 (April 2015): 1132-39.
(Note: ellipses added; italics in original.)

The 1996 Diamond article mentioned above, is:
Diamond, Arthur M., Jr. “The Economics of Science.” Knowledge and Policy 9, nos. 2/3 (Summer/Fall 1996): 6-49.

Sears CEO Ed Telling Had an Introverted Fury

Writing of Ed Telling, the eventual entrepreneurial CEO of Sears:

(p. 488) Slowly, the introverted Field soldier from Danville moved up through the organization. He eventually managed the same Midwestern zone he was once made to ride. He found himself in the decadent city-state called the New York group, and it was there, in the strangely methodical fury with which he fell upon the corruption of the group and the profligacy of powerful store jockeys, that certain individuals around him began to feel inspired by his quiet power, as if he’d touched some inverted desire in each of them to do justice at his beckoning and to even numerous scores. He was possessed of a determination to promulgate change such as none of them had ever seen before, and certain hard-bitten bitten veterans like Bill Bass found themselves strangely moved.

Source:
Katz, Donald R. The Big Store: Inside the Crisis and Revolution at Sears. New York: Viking Adult, 1987.

Successful Billionaire Mathematician Would Have Lost Math Contests, But Was Good at Slow Pondering

(p. D1) James H. Simons likes to play against type. He is a billionaire star of mathematics and private investment who often wins praise for his financial gifts to scientific research and programs to get children hooked on math.
But in his Manhattan office, high atop a Fifth Avenue building in the Flatiron district, he’s quick to tell of his career failings.
He was forgetful. He was demoted. He found out the hard way that he was terrible at programming computers. “I’d keep forgetting the notation,” Dr. Simons said. “I couldn’t write programs to save my life.”
After that, he was fired.
His message is clearly aimed at young people: If I can do it, so can you.
. . .
(p. D2) “I wasn’t the fastest guy in the world,” Dr. Simons said of his youthful math enthusiasms. “I wouldn’t have done well in an Olympiad or a math contest. But I like to ponder. And pondering things, just sort of thinking about it and thinking about it, turns out to be a pretty good approach.”

For the full story, see:
WILLIAM J. BROAD. “Seeker, Doer, Giver, Ponderer; A Billionaire Mathematician’s Life of Ferocious Curiosity.” The New York Times (Tues., JULY 8, 2014): D3.
(Note: ellipsis added.)
(Note: the online version of the story has the date JULY 7, 2014.)

Perceptual Diversity Puzzle: Is It White-and-Gold or Blue-and-Black?

“The dress in a photo from Caitlin McNeill’s Tumblr site.” Source of caption and photo: online version of the NYT article quoted and cited below.

(p. B1) The mother of the bride wore white and gold. Or was it blue and black?

From a photograph of the dress the bride posted online, there was broad disagreement. A few days after the wedding last weekend on the Scottish island of Colonsay, a member of the wedding band was so frustrated by the lack of consensus that she posted a picture of the dress on Tumblr, and asked her followers for feedback.
“I was just looking for an answer because it was messing with my head,” said Caitlin McNeill, a 21-year-old singer and guitarist.
. . .
Less than a half-hour after Ms. McNeil’s original Tumblr post, Buzzfeed posted a poll: “What Colors Are This Dress?” As of Friday afternoon, it had (p. B5) been viewed more than 28 million times. (White and gold was winning handily.)
. . .
Politicians were eager to stake out their positions. “I know three things,” wrote Senator Christopher Murphy, a Connecticut Democrat, on Twitter. “1) the ACA works; 2) climate change is real; 3) that dress is gold and white.”
Sorry, senator. The dress, as we all now know, is blue and black. It goes for 50 pounds at Roman Originals, a British retailer.
. . .
Various theories were floated about why the dress looks different to different people. (No, if you see the darker hues of blue and black it doesn’t mean that you are depressed.)
Duje Tadin, associate professor for brain and cognitive sciences at the University of Rochester, says it may be because of variations in the number of photoreceptors called cones in the retina that perceive the color blue. The human eye has about six million cones that are sensitive to green, red or blue. Signals from the cones go to the brain, which interprets them as color.
“It’s puzzling,” conceded Dr. Tadin. “When it comes to color, blue is always the weird one. We have the fewest number of blue cones.” He added, “If you don’t have very many blue cones, you may see it as white, or if you have plenty of blue cones, you may see more blue.”
. . .
The one thing scientists could agree on was that this is a very unusual illusion. People who see the dress one way do not eventually begin to see it the other way, as is common with many optical illusions. “This clearly has to do with individual differences in how we perceive the world,” said Dr. Tadin. “There’s something about this particular image that just captures those differences in a remarkable way.

For the full story, see:
JONATHAN MAHLER. “The Dress That Melted the Internet.” The New York Times (Sat., FEB. 28, 2015): B1 & B5.
(Note: ellipses added.)
(Note: the online version of the story has the date FEB. 27, 2015, and has the title “The White and Gold (No, Blue and Black!) Dress That Melted the Internet.”)

Heckman Thinks that Economists Who Are Only Economists May Be Dangerous

The Journal of Political Economy, edited by the University of Chicago economics department, is one of the three or four most prestigious journals in the economics profession. For the last 20 years or so (if memory serves) the back cover of each issue has had a funny quote or interesting or unusual anecdote, related to some aspect of economics.
I was surprised to see that the quote from the October 2014 issue as “suggested by James J. Heckman.” Heckman is a Nobel-Prize-winner who is known mainly for developing new econometric techniques in the area of labor economics. When I was a graduate student at Chicago, his graduate students tended to be among those who were most oriented to formalism and technique. So I was surprised to see that he had suggested the following quote from neo-Austrian economist and fellow Nobel-Prize-winner F.A. Hayek:

(p. 463) But nobody can be a great economist who is only an economist—and I am even tempted to add that the economist who is only an economist is likely to become a nuisance if not a positive danger.

Source:
Hayek, F. A. “The Dilemma of Specialization.” In The State of the Social Sciences, edited by Leonard D. White. Chicago: University of Chicago Press, 1956.
(Note: I do not have the book, and cannot find the page range of Hayek’s article in the book.)

“If You Want to Find Something New, Look for Something New!”

(p. D8) Yves Chauvin, who shared the 2005 Nobel Prize in Chemistry for deciphering a “green chemistry” reaction now used to make pharmaceuticals and plastics more efficiently while generating less hazardous waste, died on Tuesday [January 27, 2015] in Tours, France.
. . .
He confessed that he was not a brilliant student, even in chemistry. “I chose chemistry rather by chance,” he wrote, “because I firmly believed (and still do) that you can become passionately involved in your work, whatever it is.”
Mr. Chauvin graduated from the Lyon School of Industrial Chemistry in 1954. Military service and other circumstances prevented him from pursuing a doctoral degree, which he said he regretted. “I had no training in research as such and as a consequence I am in a sense self-taught,” he wrote in his Nobel Prize lecture.
He worked in industry for a few years before quitting, frustrated by an inability to pursue new ideas. “My motto is more, ‘If you want to find something new, look for something new!’ ” Mr. Chauvin wrote. “There is a certain amount of risk in this attitude, as even the slightest failure tends to be resounding, but you are so happy when you succeed that it is worth taking the risk.”
He found the freedom to choose his research when he joined the French Petroleum Institute in 1960, and it led to his breakthrough on metathesis.
“Like all sciences, chemistry is marked by magic moments,” Mr. Chauvin wrote. “For someone fortunate enough to live such a moment, it is an instant of intense emotion: an immense field of investigation suddenly opens up before you.”

For the full obituary, see:

KENNETH CHANG. “Yves Chauvin, Chemist Sharing Nobel Prize, Dies at 84.” The New York Times (Sat., JAN. 31, 2015): D8.

(Note: ellipsis, and bracketed date, added.)

(Note: the online version of the obituary has the date JAN. 30, 2015.)