Suicides more Common than Homicides

An excerpt from Livescience.com:

Overall, the public greatly underestimates the incidence of suicide.

About 30,000 people die by suicide each year in America. It is the ninth leading cause of death in this country, and higher for men than women.

And it's not just an American problem. Suicide is the second-leading cause of death for Canadian youth and young adults. Worldwide, there are an estimated 10 million to 20 million attempted suicides each year.

In fact, the suicide rate is higher than the homicide rate: Nearly a third more people die at their own hands than die at other people's hands (the murder rate in America is about 6 per 100,000; for suicides it's 10.8).

So for every two murders you hear about, three other people killed themselves.

One reason that people believe homicide is much more common than suicide is because of the news media's selective coverage.

Most people hear about the vast majority of deaths (such as accidents, homicides, and suicides) not from personal experience but from the news. Yet while murders make daily news, suicides and suicide attempts are often not considered newsworthy unless the victim is famous (such as Wilson, musician Kurt Cobain, or comedian Richard Jeni) or is part of a group (such as the Heaven's Gate cult).

There may be another reason why journalists are reluctant to cover suicide stories: They can lead to copycats, or what is called "suicide contagion," where others who are considering suicide are prompted to kill themselves from seeing news reports. While not common, it does happen, and helps to keep the true cost of suicides out of the public's mind.

Super Crunchers

Here is an excerpt from Supercrunchers, in which the author (a lawyer) discussed what makes an article more likely to be cited by other authors:

As a law professor, my primary publishing job is to write law review articles. I don’t get paid for them, but a central measure of an article’s success is the number of times the articles have been cited by other professors. So with the help of a full-time number-crunching assistant named Fred Vars, I went out and analyzed what caused a law review article to be cited more or less. Fred and I collected citation information on all the articles published for fifteen years in the top three law reviews. Our central statistical formula had more than fifty variables. Like Epagogix [a group that created an algorithm intended to predict whether a movie will be successful based on characteristics of its script], Fred and I found that seemingly incongruous things mattered a lot. Articles with shorter titles and fewer footnotes were cited significantly more, whereas articles that included an equation or an appendix were cited a lot less. Longer articles were cited more, but the regression formula predicted that citations per page peak for articles that were a whopping fifty-three pages long….

Law review editors who want to maximize their citation rates should also avoid publishing criminal and labor law articles, and focus instead on constitutional law.

Ceiling Height affects Decision Making

Here is an abstract from the Journal of Consumer Research:

The Influence of Ceiling Height: The Effect of Priming on the Type of Processing That People Use

Authors: Joan Meyers-Levy & Rui (Juliet) Zhu

Abstract: This article demonstrates that variations in ceiling height can prime concepts that, in turn, affect how consumers process information. We theorized that when reasonably salient, a high versus low ceiling can prime the concepts of freedom versus confinement, respectively. These concepts, in turn, can prompt consumers’ use of predominately relational versus item-specific processing. Three studies found support for this theorizing. On a variety of measures, ceiling height–induced relational or item-specific processing was indicated by people’s reliance on integrated and abstract versus discrete and concrete ideation. Hence, this research sheds light on when and how ceiling height can affect consumers’ responses.

Siberia Weather Predicts US Winter

An excerpt from Livescience.com:

A new weather-forecasting model based on autumn snowfall in Siberia could help meteorologists predict winter temperatures and snowfall in the United States and Europe.

The model results, reported this week in the Journal of Climate, could help make climate prediction more accurate and reliable for fields such as agriculture, water management and general weather risks. At least $3 trillion of the U.S. economy is sensitive to weather conditions, estimates the National Science Foundation (NSF).

Scientists led by Judah Cohen of Atmospheric and Environmental Research, Inc. (AER Inc.) verified the sCast (short for "seasonal forecast") model with seven real-time winter forecasts and 33 simulations of winters going back to 1972.

APA position on Torture

The American Psychological Association released a statement on torture recently. Here is an excerpt:

... mock executions, water-boarding or any other form of simulated drowning or suffocation, sexual humiliation, rape, cultural or religious humiliation, exploitation of phobias or psychopathology, induced hypothermia, the use of psychotropic drugs or mind-altering substances ... hooding, forced nakedness, stress positions, the use of dogs to threaten or intimidate, physical assault including slapping or shaking, exposure to extreme heat or cold, threats of harm or death; and isolation, sensory deprivation and over-stimulation and/or sleep deprivation used in a manner that represents significant pain or suffering ... or the threatened use of any of the above techniques to the individual or to members of the individual’s family.

Are Smart Scientists Less Likely to Cheat

An excerpt from the Statistical Modeling, Causal Inference, and Social Science Blog:

In this discussion of Allegra Goodman's book novel Intuition, Barry wrote, "brilliant people are at least as capable of being dishonest as ordinary people." The novel is loosely based on some scientific fraud scandals from the 1980s, the one of its central characters, a lab director, is portrayed as brilliant and a master of details, but who makes a mistake by brushing aside evidence of fraud by a postdoc in her lab. One might describe the lab director's behavior as "soft cheating" since, given the context of the novel, she had to have been deluding herself by ignoring the clear evidence of a problem.

Anyway, the question here is: are brilliant scientists at least as likely to cheat? I have no systematic data on this and am not sure how how to get this information. One approach would be to randomly sample scientists, index them by some objective measure of "brilliance" (even something like asking their colleagues to rate their brilliance on a 1-10 scale and then taking averages would probably work), then do a through audit of their work to look for fraud, and then regress Pr(fraud) on brilliance. This would work if the prevalence of cheating were high enough. Another approach would be to do a case-control study of cheaters and non-cheaters, but the selection issues would seem to be huge here, since you'd be only counting the cheaters who got caught. Data might also be available within colleges on the GPA's and SAT scores of college students who were punished for cheating; we could compare these to the scores of the general population of students. And there might be useful survey data of students, asking questions like "do you cheat" and "what's your SAT" or whatever. I guess there might even be a survey of scientists, but it seems harder to imagine they'd admit to cheating.

Arguments that brilliant scientists are more likely to cheat

Goodman makes the argument (through fictional example) in her book that brilliant scientists are more likely to be successful lab directors, thus under more pressure to keep getting grants (many mouths to feed), thus susceptible to soft cheating, at least. Similarly, the cheating postdoc is described as so smart he never had to work hard in college, again under high expectations and cheating partly to maintain his reputation as the golden boy. On the other side, a more ordinary "worker bee" type will not be expected to come up with a brilliant insight, and so won't be under that pressure to cheat.

Another argument that brilliant scientists are more likely to cheat comes from some of the standard "overcoming bias" ideas, that a brilliant person is more likely to have made daring correct conjectures in the past, then when the person comes up with a new conjecture, he or she is more likely to believe in it and then fake the data. (I'm assuming that scientific cheating of the sort that's interesting is in the lines of twisting the data to support a conclusion that you think is true. If you don't even think the hypothesis is true, there's not much point to faking the evidence, since later scientists will overturn you anyway. The motivation for cheating is that you're sure you're right, and so you overconfidently discard the cases that don't support your case.)

Arguments that brilliant scientists are less likely to cheat

I'm half-convinced by the overconfidence argument above, but overall I suspect that brilliant scientists are more likely to be honest than less-brilliant scientists, at least in their own field of research. I say this partly because science is, to some extent, about communication, and transparency is helpful here. Also, as illustrated (fictionally) in Goodman's book, fraud is often done to cover up unsuccessful research. If you're brilliant, it's likely that your research will be successful: even if you don't achieve your big goals--even brilliant people will, perhaps should, bite off more than you can chew--you should get some productive spinoffs, and the simple cost-benefit analysis suggests that cheating would stand to lose you more than you'd gain.

Conversely, for a more mediocre scientist, cheating may be a roll of the dice, which, if it succeeds, can bring you to a plateau, and if it fails, you won't be that much worse off than before--you don't have such a big potential reputation to lose. And if the stakes are low, the cheating might never be discovered: you get the paper, the job, tenure or whatever, your findings are never replicated, and you move on.

Thinking of honesty as a behavior rather than a character trait

The other thing is that it might make more sense to think of honesty as a behavior rather than a character trait. I'm pretty honest (I think), but that also makes me an unpracticed liar (and, unsuprisingly, a bad liar). So the smart move for me is not to lie--again, more to lose than to gain (in my estimated expected value). But if I worked in a profession where dishonesty--or, to put it more charitably, hiding the truth--was necessary, something involving negotiation or legal maneuvers or whatever, then I'd probably get better at lying and then maybe I'd start doing more of it in other aspects of life.

Science seems to me like an area where lying isn't generally very helpful, so I don't see that the best scientists would be good or practiced liars. The incentives, at least for the very best work, go the other way.

Mean vs. Median (sex partners)

An excerpt from Slate.com:

In Sunday's New York Times, science writer Gina Kolata took on studies suggesting that men tend to have more sexual partners than women do. This CDC study, for one, shows that American men between the ages of 20 and 59 report accumulating a median of seven female bedmates, while for women the corresponding figure is just four. The problem, Kolata writes, is that these numbers present a mathematical contradiction. "It is logically impossible for heterosexual men to have more partners on average than heterosexual women," she explains. ...

The problem is hiding in the distinction between the median (the number reported by the CDC study) and the mean. The mean is what people usually call the "average." To calculate the mean number of sexual partners among a group of men, you add together each man's sexual partners, then divide by the total number of men. The median, on the other hand, is the number you'd get if you line all the men up in order of their number of partners, then ask the man in the middle to state his count.

Consider a village with 200 people, evenly divided by sex. Ninety of the women are virgins, but none of the men is. Each man has slept with just one of the sexually active women; each woman who's had sex, then, has had 10 partners. In this case, the median woman has zero sexual partners, but the median man has one. So we see a big difference in medians between the male and female populations, just as in the CDC data.

The means are a different story. Each male villager has one partner, for a total of 100; dividing by the total male population of 100 gives a mean of one. Among the women, the 10 nonvirgins have 10 sexual partners each, totaling 100 again; divide by the female population, and you'll find that the mean number of sexual partners per woman is also one. This equality is no coincidence. In a closed group like our village, the total number of opposite-sex partnerships has to be the same, whether you count these partnerships from the male or female point of view. If the questionnaire gives a different result, the questioned are lying.

In practice, means and medians are often roughly comparable. The exceptions come from situations where a small slice of the population has a lot of sex—or whatever other item is being measured. An old joke is illustrative here: 10 statisticians in a bar. Ted Turner walks in. The statisticians start to whoop and holler. "What's going on?" asks Turner. One statistician explains, "On average, we just got a whole lot richer!"

The joke here is that average, to a statistician, means mean—but average, to, well, an average person, means something more like typical. Ted Turner's presence in the bar raises the mean income of the drinkers quite a lot, but the median hardly at all. And when we ask questions about sexual behavior, it's usually typical men and women we want to know about—not averages that can be dragged upward by a few hypothetical Ted Turners of sex.

Mean vs. Median (sex partners)

An excerpt from Slate.com:

In Sunday's New York Times, science writer Gina Kolata took on studies suggesting that men tend to have more sexual partners than women do. This CDC study, for one, shows that American men between the ages of 20 and 59 report accumulating a median of seven female bedmates, while for women the corresponding figure is just four. The problem, Kolata writes, is that these numbers present a mathematical contradiction. "It is logically impossible for heterosexual men to have more partners on average than heterosexual women," she explains. ...

The problem is hiding in the distinction between the median (the number reported by the CDC study) and the mean. The mean is what people usually call the "average." To calculate the mean number of sexual partners among a group of men, you add together each man's sexual partners, then divide by the total number of men. The median, on the other hand, is the number you'd get if you line all the men up in order of their number of partners, then ask the man in the middle to state his count.

Consider a village with 200 people, evenly divided by sex. Ninety of the women are virgins, but none of the men is. Each man has slept with just one of the sexually active women; each woman who's had sex, then, has had 10 partners. In this case, the median woman has zero sexual partners, but the median man has one. So we see a big difference in medians between the male and female populations, just as in the CDC data.

The means are a different story. Each male villager has one partner, for a total of 100; dividing by the total male population of 100 gives a mean of one. Among the women, the 10 nonvirgins have 10 sexual partners each, totaling 100 again; divide by the female population, and you'll find that the mean number of sexual partners per woman is also one. This equality is no coincidence. In a closed group like our village, the total number of opposite-sex partnerships has to be the same, whether you count these partnerships from the male or female point of view. If the questionnaire gives a different result, the questioned are lying.

In practice, means and medians are often roughly comparable. The exceptions come from situations where a small slice of the population has a lot of sex—or whatever other item is being measured. An old joke is illustrative here: 10 statisticians in a bar. Ted Turner walks in. The statisticians start to whoop and holler. "What's going on?" asks Turner. One statistician explains, "On average, we just got a whole lot richer!"

The joke here is that average, to a statistician, means mean—but average, to, well, an average person, means something more like typical. Ted Turner's presence in the bar raises the mean income of the drinkers quite a lot, but the median hardly at all. And when we ask questions about sexual behavior, it's usually typical men and women we want to know about—not averages that can be dragged upward by a few hypothetical Ted Turners of sex.

Gut Feelings

There has been much said in the past few years about how emotions and reasoning are quite inseparable. Antonio Damasio and colleagues did a unique study published in 1994 using the Iowa Gambling Task. Here is Wikipedia's explanation of the task:

The Iowa gambling task is a psychological task thought to simulate real-life decision making. It was introduced by Bechara, Damasio, Tranel and Anderson (1994), then researchers at the University of Iowa. It has been brought to popular attention by Antonio Damasio, proponent of the Somatic markers hypothesis and author of Descartes' Error. The task is sometimes known as Bechara's Gambling Task, and is widely used in research of cognition and emotion.

Participants are presented with 4 virtual decks of cards on a computer screen. They are told that each time they choose a card they will win some game money. Every so often, however, choosing a card causes them to lose some money. The goal of the game is to win as much money as possible. Every card drawn will earn the participant a reward ($100 for Decks A and B; $50 for Decks C and D). Occasionally, a card will also have a penalty (A and B have a total penalty of $1250 for every ten cards; C and D have a total penalty of $250 for every ten cards). Thus, A and B are "bad decks", and C and D are "good decks", because Decks A or B will lead to losses over the long run, and Decks C or D will lead to gains. Deck A differs from B and Deck C differs from D in the number of trials over which the losses are distributed: A and C have five smaller loss cards for every ten cards; B and D have one larger loss card for every ten cards.

Most healthy participants sample cards from each deck, and after about 40 or 50 selections are fairly good at sticking to the good decks. Patients with orbitofrontal cortex (OFC) dysfunction, however, continue to perseverate with the bad decks, sometimes even though they know that they are losing money overall. Concurrent measurement of galvanic skin response shows that healthy participants show a "stress" reaction to hovering over the bad decks after only 10 trials, long before conscious sensation that the decks are bad. By contrast, patients with OFC dysfunction never develop this physiological reaction to impending punishment.

Damasio, in his book, Descartes' Error, discusses how we need this emotional feedback in order to make good decisions. People with OFC dysfunction do not register this feedback and thus have problems with the Iowa Gambling task.

Gerd Gigerenzer was interviewed in Salon.com to discuss his new book Gut Feelings: The Intelligence of the Unconscious and how it relates to Michael Chertoff's recent announcement that he had a gut feeling that terrorists would attack soon. Below is an excerpt:

The controversy hit at a propitious moment for Gerd Gigerenzer, a German behavioral scientist who has made human intuition his life's work. Gigerenzer's new book, "Gut Feelings: The Intelligence of the Unconscious" -- a more deeply scientific (if less tickling) look at a subject first popularized by Malcolm Gladwell in "Blink" -- seeks to undo the cultural dismissal of the gut.

Not just Chertoff's but everyone's: Intuition, Gigerenzer writes, "is more than impulse and caprice; it has its own rationale." A "gut feeling" is not a supernatural force -- it's not ESP. Rather it is the product of your brain quickly, often unconsciously, using a rule of thumb (what academics call a "heuristic") to arrive at a decision using little evidence.

Imagine that you're playing baseball and a fly ball comes headed your way. How do you know where it's going to land? As Gigerenzer points out, people do not -- as scientists have long assumed -- calculate the ball's trajectory, estimating its velocity, angle, spin, the air's resistance and wind speed. Indeed, in experiments, baseball players have proved very bad at guessing where a fly ball will hit the ground. Instead, everyone who has ever caught a ball has (unconsciously) used a rule of thumb to do so. The rule is known as the "gaze heuristic," and it governs your speed as you chase the ball: You fix your eyes on the ball, start running, and adjust your speed so that the angle between you and the ball remains constant. In other words, instead of computing the ball's trajectory, all you have to do is keep your eye on it -- "the heuristic leads the player to the landing point," Gigerenzer writes.

Gigerenzer says that these heuristics arise out of our "evolved capacities." We've evolved to be able to track objects through the air, for instance. Consequently, our gut feelings -- whether they're useful in catching a ball, or in predicting a terrorist attack -- aren't to be taken lightly. Intuition is not a deviation from the right way to make decisions; it's how we make decisions all the time.

Bruce Lee Anecedote

An earlier entry had some interesting quotes from Bruce Lee. There is another story from one of Bruce Lee's students. Bruce Lee and his student were out running and they were trying to go a little faster and further than usual. The student protested, citing that he was worried that he was going to have a heart attack and die. Bruce Lee told the student, "then just die." After the run Bruce Lee responded with this quote:

"Because you might as well be dead. Seriously, if you always put limits on what you can do, physically or anything else, it'll spread over into the rest of your life. It'll will spread into your work, into your morality, into your entire being. There are no limits. There are plateaus, but you must not stay there, you must go beyond them. If it kills you, it kills you. A man must constantly exceed his own level."

Facial Microexpressions

An excerpt from livescience.com:

Split-second facial expressions made by others—and the feelings they betray—might go unnoticed by your conscious mind, but apparently they do register subliminally.

Reading these subtle clues in faces can guide the brain, resulting in unconscious warnings, a new study suggests. Although these hints might normally help warn a person of hidden threats, when kicked into overdrive they might exacerbate anxiety disorders, the scientists said.

Student volunteers were shown 70 different faces on a computer screen, each face bearing a surprised expression. Immediately before each surprised face was displayed, another face was flashed just for 30 milliseconds, faster than the conscious mind can register. Half of these "micro-expressions" were happy, while the others were fearful.

During the experiment, Northwestern University cognitive neuroscientist Ken Paller and his colleagues recorded brain activity using electrodes placed on the scalps of the volunteers. Although the volunteers were not aware they viewed subliminal emotional expressions, the recordings showed their brain detected them.

When asked to rate each surprised face as either positive, such as upon the sudden arrival of a friend, or negative, such as after experiencing a sudden act of violence, the volunteers rated surprised faces preceded by fearful micro-expressions more negatively on average than ones preceded by happiness.

"Our results show that an unconsciously perceived signal of threat, such as a brief facial expression of fear, can still bubble up and unwittingly influence social judgments and how we act," said Paller's colleague Wen Li, also at Northwestern.

The researchers detailed their findings in an upcoming issue of the Journal of Cognitive Neuroscience.

"We can perceive a lot more than we are aware of perceiving, and doing so can influence behavior, although these influences can lead to just subtle differences and not make you do something out of the ordinary," Paller said.

Before the experiment, questionnaires given to the volunteers assessed how often they experienced anxiety, particularly in social affairs. Those with more anxious personalities had the strongest brain response to subliminal expressions of fear. Paller suggested their brains may be highly sensitive to subtle hints of threat and "may thus cause excessive anxiety."

...

It is unclear whether susceptibility to micro-expressions has a huge effect on social judgments and behavior, Paller said. "Most of the reason you like or don't like someone could be because of things you are aware of, not things you aren't," he said.

People do make facial micro-expressions in real life, and detecting them might normally help sense another's true intentions, he said. Still, Paller noted that "valuable as this ability may be, when someone is paying you a compliment it is sometimes better to take it at face value rather than read more into it."

Video of Intensive Exposure

Below is a pretty good video of what happens in intensive exposure:

Ichiro Quotes

On this blog there have been quotes from Rob Zombie and Donald Rumsfeld, Ben Franklin, Thomas Paine, and Bruce Lee. Now, we can add Ichiro Suzuki, of the Seattle Mariners. Slate.com has an interesting article describing some of the weird things he has said (below is an excerpt):

When Ichiro held a press conference earlier this month to discuss the deal, he divulged that a key advisory role in deciding to remain in Seattle was played by his dog Ikky. "He said, 'Woof, woof, woof,' which meant, 'Stay, stay, stay,' " Suzuki told reporters in Japanese. "Of course, I listened."

It was a curious revelation, and not just for the obvious reasons. When he first arrived in the United States in 2001, Ichiro would not even share his pet's name with a curious reporter. "I do not have the dog's permission," he explained.

...After starting this season with a run of multihit games, Suzuki was asked whether he found his performance surprising: "It's not surprising. At the same time, it's not that usual. It's somewhere between usual and surprising," he said.

... Before facing off against Red Sox pitcher Daisuke Matsuzaka in April, Ichiro said, "I hope he arouses the fire that's dormant in the innermost recesses of my soul. I plan to face him with the zeal of a challenger."

Asked recently about a road trip to Cleveland, he admitted: "To tell the truth, I'm not excited to go to Cleveland, but we have to. If I ever saw myself saying I'm excited going to Cleveland, I'd punch myself in the face, because I'm lying."

And about Tiger Woods, he said, "Tiger is a great golfer, but … when you say athlete, I think of Carl Lewis. When you talk about [golfers or race-car drivers], I don't want to see them run. It's the same if you were to meet a beautiful girl and go bowling. If she's an ugly bowler, you are going to be disappointed."

Evaluating Outcomes in Schools

Here is an excerpt from Charles Wheelan discussing the difficulty in assessing school outcomes:

We don't really even know which schools are good schools.
...

Think of it this way: If a golf pro gives Tiger Woods a lesson, and a different golf pro gives me a lesson, can we conclude that Tiger's teacher is better than mine because Tiger beats me by three shots in a match after our lessons?

That's usually how newspapers and real estate agents pick the "best schools." The true measure of quality -- with golf pros or elementary schools -- is how much value they add. In education, that turns out to be extremely difficult to calculate.

You may be thinking that we could probably fix this with a simple pre- and post-test -- let's measure what kids know when they walk in the school door, and then measure how much they know when they leave, one or four or eight years later. The best schools will be the ones with the students who show the most improvement.

Not exactly. Gifted students don't merely begin at a higher level, they also learn at a faster rate. So, to stick with our athletic example, suppose that neither Tiger Woods nor Michael Moore has ever played tennis before. If we give them each a tennis coach, can we evaluate the quality of those coaches based on the subsequent outcome of a tennis match between Tiger Woods and Michael Moore? (Pause for a moment and consider what a great pay-per-view event that would be.)

So schools with high test scores may or may not be doing a great job; perhaps their students are capable of much more. And conversely, some schools with middling or poor test scores may be doing a terrific job educating students who would otherwise be failing abjectly.

Priming

An excerpt from the NY Times:

In a recent experiment, psychologists at Yale altered people’s judgments of a stranger by handing them a cup of coffee.

The study participants, college students, had no idea that their social instincts were being deliberately manipulated. On the way to the laboratory, they had bumped into a laboratory assistant, who was holding textbooks, a clipboard, papers and a cup of hot or iced coffee — and asked for a hand with the cup.

That was all it took: The students who held a cup of iced coffee rated a hypothetical person they later read about as being much colder, less social and more selfish than did their fellow students, who had momentarily held a cup of hot java.

No Revisions Policy

An excerpt from the Freakonomics Blog:

getting published is a brutal process. You spend a year or two coming up with an idea, try to think over every aspect of the problem, collect and analyze data, and finally produce a 30-page paper summarizing all the work. You can only send it to one journal at a time. Then you wait six to nine months to get a decision from an editor.

At the top journals, 90 percent or more of the papers get rejected, which means you then head back to square one at another journal. Even if the referees and editors like the paper, the best you can hope for is a “revise and resubmit” invitation, which entails pages and pages of demands for changes before it will be reconsidered for publication. In a few cases, the comments I got back from referees and editors were longer than the paper I submitted in the first place!

In the off chance that your paper ever gets accepted, there is often a delay of a year before it appears in print. Thus, from start to finish, the whole process takes 3 to 5 years.

...

McAfee just took over as the editor of a journal called Economic Inquiry. It has been a long time since I submitted a paper to that journal, in part because the last time I did, the editorial process was especially onerous. Just a few weeks ago one of my co-authors suggested sending our paper there, and I vetoed the idea for that very reason. Luckily, I’ve been slow in getting around to submitting to another journal, because with a new sheriff in town, things are changing at Economic Inquiry. After taking over as editor, McAfee posted this on the journal’s web page (hat tip to Marginal Revolution, who blogged about it):

Editor’s Announcement: No Revisions Option

Journal time to publication lags have become embarrassing. Many authors have 5-year submission-to-print stories. More insidious, in my view, is the gradual morphing of the referees from evaluators to anonymous co-authors. Referees request increasingly extensive revisions. Usually these represent improvements, but the process takes a lot of time and effort, and the end result is often worse owing to its committee-design. Authors, knowing referees will make them rewrite the paper, are sometimes sloppy with the submission. This feedback loop - submitting a sloppy paper since referees will require rewriting combined with a need to fix all the sloppiness - has led to our current misery. Moreover, the expectation that referees will rewrite papers, combined with sloppy submissions, makes refereeing extraordinarily unpleasant. We - the efficiency-obsessed academic discipline - have the least efficient publication process.

The system is broken.

Consequently, Economic Inquiry is starting an experiment. In this experiment, an author can submit under a “no revisions” policy. This policy means exactly what it says: if you submit under no revisions, I (or the co-editor) will either accept or reject. What will not happen is a request for a revision.

I will ask referees: “is it better for Economic Inquiry to publish the paper as is, versus reject it, and why or why not?” This policy returns referees to their role of evaluator. There will still be anonymous reports.

Authors who receive an acceptance would have the option of publishing without changes. If a referee noticed a minor problem and put it in the report, self-respecting authors would fix the problem. But such fixes would not be a condition of publication.

During the course of the experiment, an author may opt for submission under the old system. The old system remains the default; to opt in to the new system, please add “I submit under the ‘no revisions’ policy.”