Methodology – Page 5 – artdiamondblog.com

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.)

Double-Blind Clinical Trials Are NOT the Only Source of Good Evidence

(p. 16) Back in her office, . . . [rheumatologist Jennifer Frankovich] found that the scientific literature had no studies on patients like this to guide her. So she did something unusual: She searched a database of all the lupus patients the hospital had seen over the previous five years, singling out those whose symptoms matched her patient’s, and ran an analysis to see whether they had developed blood clots. “I did some very simple statistics and brought the data to everybody that I had met with that morning,” she says. The change in attitude was striking. “It was very clear, based on the database, that she could be at an increased risk for a clot.”
The girl was given the drug, and she did not develop a clot. “At the end of the day, we don’t know whether it was the right decision,” says Chris Longhurst, a pediatrician and the chief medical information officer at Stanford Children’s Health, who is a colleague of Frankovich’s. But they felt that it was the best they could do with the limited information they had.
A large, costly and time-consuming clinical trial with proper controls might someday prove Frankovich’s hypothesis correct. But large, costly and time-consuming clinical trials are rarely carried out for uncommon complications of this sort. In the absence of such focused research, doctors and scientists are increasingly dipping into enormous troves of data that already exist — namely the aggregated medical records of thousands or even millions of patients to uncover patterns that might help steer care.
. . .
(p. 17) . . . , developing a “learning health system” — one that can incorporate lessons from its own activities in real time — remains tantalizing to researchers. Stefan Thurner, a professor of complexity studies at the Medical University of Vienna, and his researcher, Peter Klimek, are working with a database of millions of people’s health-insurance claims, building networks of relationships among diseases. As they fill in the network with known connections and new ones mined from the data, Thurner and Klimek hope to be able to predict the health of individuals or of a population over time. On the clinical side, Longhurst has been advocating for a button in electronic medical-record software that would allow doctors to run automated searches for patients like theirs when no other sources of information are available.
With time, and with some crucial refinements, this kind of medicine may eventually become mainstream. Frankovich recalls a conversation with an older colleague. “She told me, ‘Research this decade benefits the next decade,’ ” Frankovich says. “That was how it was. But I feel like it doesn’t have to be that way anymore.”

For the full story, see:
VERONIQUE GREENWOOD. “Eureka; Dr. DATA; Can Statistical Analysis Tell Us What Clinical Trials Cannot?” The New York Times Magazine (Sun., OCT. 5, 2014): 16-17.
(Note: ellipses, and bracketed name, added.)
(Note: the online version of the story has the date OCT. 3, 2014, and has the title “Eureka; Can Big Data Tell Us What Clinical Trials Don’t?”)

“You Don’t Reach Serendip by Plotting a Course for It”

(p. 320) As John Barth wrote in The Last Voyage of Somebody the Sailor, “You don’t reach Serendip by plotting a course for it. You have to set out in good faith for elsewhere and lose your bearings serendipitously.”28 The challenge for educational institutions, government policy, research centers, funding agencies, and, by extension, all modern medicine, will be how to encourage scientists to lose their bearings creatively. What they discover may just save our lives!

Source:
Meyers, Morton A. Happy Accidents: Serendipity in Modern Medical Breakthroughs. New York: Arcade Publishing, 2007.
(Note: italics in original.)