In response to last week’s post, Mike DeKay sent in this paper, which PNAS is good enough to let you down load for free.

CITATION
Attari, S. Z., DeKay, M. L., Davidson, C. I., & Bruine de Bruin, W. (in press). Public perceptions of energy consumption and savings. Proceedings of the National Academy of Sciences of the United States of America.

ABSTRACT
In a national online survey, 505 participants reported their perceptions of energy consumption and savings for a variety of household, transportation, and recycling activities. When asked for the most effective strategy they could implement to conserve energy, most participants mentioned curtailment (e.g., turning off lights, driving less) rather than effciency improvements (e.g., installing more effcient light bulbs and appliances), in contrast to experts’ recommendations. For a sample of 15 activities, participants underestimated energy use and savings by a factor of 2.8 on average, with small overestimates for low-energy activities and large underestimates for high-energy activities. Additional estimation and ranking tasks also yielded relatively flat functions for perceived energy use and savings. Across several tasks, participants with higher numeracy scores and stronger proenvironmental attitudes hadmore accurate perceptions. The serious defciencies highlighted by these results suggest that well-designed efforts to improve the public’s understanding of energy use and savings could pay large dividends.

For press coverage, see The New York Times, USA Today, Newsweek, The Economist, National Geographic, and Pocket Science on YouTube, among others.

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Peter McGraw, who is a big (in the sense of “notable” and in the sense of “six foot five inches tall” ) Decision Making researcher has launched a new

• Blog
• Website

There’s a nice profile of the man here: What makes us laugh? Professor Peter McGraw thinks he’s found the answer to one of humanity’s greatest questions

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Here is a cool paper documenting an amusing sort of less-is-more effect in which professionals do worse than laypeople in a crime-solving task. In addition, learning valid information decreases people’s accuracy. That said, logisitic regression beats ‘em all, which doesn’t fit the less-is-more theme, but then again, logistic regression is less than human.

CITATION
Bennell, C; Bloomfield, S; Snook, B; Taylor, P; Barnes, C. (2010). Linkage analysis in cases of serial burglary: comparing the performance of university students, police professionals, and a logistic regression model. Psychology, Crime and Law 16 (6), 507-524.

ABSTRACT
University students, police professionals, and a logistic regression model were provided with information on 38 pairs of burglaries, 20% of which were committed by the same offender, in order to examine their ability to accurately identify linked serial burglaries. For each offense pair, the information included: (1) the offense locations as points on a map, (2) the distance (in km) between the two offenses, (3) entry methods, (4) target characteristics, and (5) property stolen. Half of the participants received training informing them that the likelihood of two offenses being committed by the same offender increases as the distance between the offenses decreases. Results showed that students outperformed police professionals, that training increased decision accuracy, and that the logistic regression model achieved the highest rate of success. Potential explanations for these results are presented, focusing primarily on the participants’ use of offense information, and their implications are discussed.

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Finally, Isaac Dinner and I are working on a thought piece that applies our research on defaults to the question of energy conservation. It’s called:

• Goldstein, D. G. & Dinner, I. M. A fairly mechanical method for policy innovation. Working paper.

We may add something about “reducing carbon emissions” to the title. We welcome feedback in the next week.

In science, you are not supposed to believe something simply because other people believe it, even if those other people are really smart. Like the Hollywood narrator, we can think of examples where “one man (1), in a world of doubters, stands up for what he knows to be true”. Galileo was sent before the Roman Inquisition for his views, and mainstream physicists rejected Einstein’s theory of relativity; one Nobel Laureate referred to it as “a Jewish fraud” (2). Thank goodness they didn’t let the prevailing views keep them from publishing what they found.

However, despite what makes a good Hollywood story, the inconvenient truth is that if you think one thing and a lot of smarter and more knowledgeable people think you are wrong, you probably are wrong.

Sure, there’s Galileo, Einstein, the Asch experiments and Tetlock’s book, but where would we be if we didn’t take the word of those with intelligence and experience?

Really stupid, that’s where.

At a certain level of acceptance, a reasonable person will accept something as true enough to believe in and get on with life. We can’t re-run every experiment in the history of science. The good news is that due to homo sapiens’ brilliant capacity to accept some counter-intuitive matters on faith, we gullibly accept fanciful notions like atoms, viruses, and Greenland to make good decisions about chemical engineering, disease prevention, and navigation.

Even rationality, which people in the decision sciences care so deeply about, originated in the Enlightenment as a description of what smart people (les hommes éclairés) (3) believe. Rationality theory at its birth was just a theory of the cognitive psychology of smart people. As the beliefs of smart people changed over time, rationality theory bent in subservience (4).

So, here’s the question of the day. If you are a scientist, what should you believe about your beliefs when they contradict the beliefs of a lot of smart people?

Story time. In graduate school, your Decision Science News editor was chatting with his statistics professor, Steven Stigler (5). The topic was the limited usefulness of p-values. Scientists seem to wish that p-values referred to the probability that a hypothesis is true (and some actually and wrongly believe this, see 6). However, they actually reflect the probability of the data given that the null hypothesis is true. A young Decision Science News remarked that this probability isn’t all that interesting.

“Well”, Stigler said, “When the p-value is very small, it’s either the case that the null hypothesis is false, or that something extraordinary has happened. Both of those seem pretty interesting.”

End of story. Time to link story to the “one man against the world” scenario.

One man believes “not X”, the scientific world believes “X”. We the bystanders want to know the probability that either is right. But we can’t know that. Furthermore, we are not experts in every scientific discipline, and do not have time to become experts.

What we bystanders probably do is run intuitive statistics on the distribution of expert opinions. We guesstimate the probability that we’d observe the data we do (all these smart and knowledgeable standing behind “X”) given that “not X” were true. We estimate this to be a small probability. After all, the smart and knowledgeable people who become scientists are a skeptical bunch. They’re doubters by default and they all want to be Galileos who get immortalized for standing apart from the pack and being proven right. Getting the vast majority of scientists to agree on anything is a feat. We consider this small probability of expert consensus and say “either ‘one man’ is wrong or something extraordinary has happened”. We typically decide that ‘one man’ is wrong, and lo and behold, we’re usually right (7).

Ach, but it gets tricky. Opinions are not i.i.d. Some view overwhelming agreement as less convincing than a bit of disagreement. (Apparently it is written in Maimonides Law of the Sanhedrin (8) “If a Sanhedrin (i.e., a bunch of judges) opens a capital case with a unanimous guilty verdict, he is exempt, until some merit is found to acquit him.” That is, if you’re facing the death penalty and all the judges vote against you, it actually prevents you from being executed. Perhaps the idea is such unanimity is unlikely if the defendant had received a proper defense.)

All of this leads up to this week’s article from Proceedings of the National Academy of Sciences:

Expert credibility in climate change [PDF]

Although preliminary estimates from published literature and expert surveys suggest striking agreement among climate scientists on the tenets of anthropogenic climate change (ACC), the American public expresses substantial doubt about both the anthropogenic cause and the level of scientific agreement underpinning ACC. A broad analysis of the climate scientist community itself, the distribution of credibility of dissenting researchers relative to agreeing researchers, and the level of agreement among top climate experts has not been conducted and would inform future ACC discussions. Here, we use an extensive dataset of 1,372 climate researchers and their publication and citation data to show that (i) 97–98% of the climate researchers most actively publishing in the field support the tenets of ACC outlined by the Intergovernmental Panel on Climate Change, and (ii) the relative climate expertise and scientific prominence of the researchers unconvinced of ACC are substantially below that of the convinced researchers.

The authors claim that not only do most (97-98%) expert climate scientists believe in climate change, but that the small minority who doubt it are of lesser prominence and lower expertise. Publication and citation data are provided to make the argument. The Yahoo Research lunch crowd, all of whom are incredibly smart and all of whom believe in climate change, found the paper to be “awesome” and “hilarious”, but “incredibly fishy”. Sounds like good criteria for inclusion in Decision Science News.

What do you think? [PDF]

NOTES
1) Sorry to the women, but that’s what they say.
2) Einstein: Holton, Gerald (2008). Who was Einstein? Why is he still so alive? In Galison, Peter L., Gerald Holton & Silvan S. Schweber (Eds) “Einstein for the 21st Century: His Legacy in Science, Art, and Modern Culture”. Also, as a Jew I take offense at the Nazi presumption that the Jews couldn’t come up with a better fraud than the theory of relativity.
3) Pardonnez moi, les femmes, main ce qu’on dit.
4) Daston, Lorraine. (1988). Classical Probability in the Enlightenment. Princeton: Princeton University Press.
5) As a graduate student, your Editor become very fond of Statistics and took so many graduate courses, he fulfilled the requirements for a Master’s degree. However, the University of Chicago had a rule that grad student scholarships covered only one Master’s degree and your Editor had already received one in Psychology. Since the costs had already been incurred, your Editor asked if he could give back the Master’s in Psych. The University was not amused.
6) Oakes, M. (1986). Statistical inference: A commentary for the social and behavioral sciences. Chichester, UK: Wiley.
7) Then we die. Sometimes we’re proven wrong after death, but as long as we were correct while alive it’s no grave concern.
8) Chapter 9

Once upon a time there was this graph (graph 1).

Andrew Gelman went all graphics curmudgeon on it, calling it an “ugly, sloppy bit of data graphics“, so it became this graph (graph 2).

Now the question is, which is better: graph 2 or graph 3?

Please use the comments and logic. Thank you.

ADDENDUM

As a result of all the feedback here. The following chart was chosen for use in the publication (Proceedings in the National Academy of Sciences):

Photo credit: http://www.flickr.com/photos/emeryjl/2104152944/. Graphs 1 and 3 have four categories and graph 2 has five categories. Also, there is a missing label on graph 3′s horizontal axis. Assume you are deciding among graphs of these basic forms that have equivalent numbers of groups and identical axis labeling.

Not long ago, your Decision Science News editor was planning a trip to a book group meeting along with another member. The monthly book group takes place in Cove Neck Long Island, about an hour East of Manhattan. Given the starting point (see map), the two had an email exchange about the best route. Your editor preferred to take the Southern route (above), as suggested by multiple Web sites, which gave time estimates under average conditions as well as under heavy traffic. These sites suggested that under the worst possible traffic, the trip would take as long as 1 hour 30 minutes.

However, the driver, citing “30 years of New York driving experience”, expressed certainty that going up the West Side Highway and taking the Kennedy (nee Triborough) bridge would be fastest. Your editor did not bring up his three years of daily commuting from the West Village to Long Island and went along for the ride, for which he was, and is, very thankful. Even if the northern route is longer, he reasoned, there will that much more of the driver’s delightful company to enjoy.

As the reader might expect, the northern route took about 2 hours and 15 minutes, possibly the longest voyage from the Tribeca to the North Shore since the advent of the canoe.

But that is all just background.

During the trip, your editor thought, “wouldn’t it be interesting to have a GPS that would show you where you are on the path you have chosen, but also show you where you would be had you chosen another path. A counterfactual GPS!”

But how would this fanciful counterfactual GPS know how long it would take you on the other route? Assuming some kind of large-scale participatory program, all GPSes could send back anonymous information about where they are and how fast they are going. In essence, the counterfactual GPS could just pick a car that is taking the other route, follow it on the other path, and display its position on your GPS, complete with nagging message (as above). It is not unlike choosing a person in another line at the grocery store to see what would have happened if you did not choose the line you did.

And what if nobody else is going to the same destination? Not a problem. Once the ‘followed’ car turns off the route, the counterfactual GPS picks another car to follow.

And what if you feel that you can drive faster than some random car that is traveling on the other route? Not a problem, the counterfactual GPS can sample all the cars traveling a piece of the route and pick one whose speed relative to other cars on its route is the same as your observed speed relative to other cars on your route.

And what if hardly anybody is driving at all when you are traveling? Again, not a problem. As soon as you indicate the two routes, the counterfactual GPS will start collecting statistics on both of them, in order to form up-to-the-minute estimates of how fast traffic is moving on each stretch of the route.

A counterfactual GPS would be more fun than educational, but it could improve the decision making of those who use it. That is, it could teach you whether it is a good idea or a bad idea to ignore the advice of the GPS.

When this was brought up at one of the famous and daily Yahoo Research lunches, Sharad begged to differ, saying that such a device would cause people to persist in their false belief that they are better at route planning than GPSes. Sharad reasoned (and he may correct us if we are wrong) that if the GPS is correct 60% of the times you disagreed with it, then it may be a long time before you realize that it is right more often than you are, and that your coincidental lucky streaks of beating it on occasion would only serve to make you think that you’ve identified special instances in which you have privileged information (even though such instances may be purely due to chance). In short, the counterfactual GPS could induce one to overfit the situation and engage in “probability matching” (deciding to trust the GPS 60% of the time) instead of always trusting it (the quote rational unquote thing to do).

Your editor supposes that if the counterfactual GPS kept long-term statistics, and then used onboard copies of R and ggplot2 to render and email out reports, such reports could help these people who are not good at trial-by-trial learning.

Like Sharad, your editor feels that people would be much more often right than wrong by trusting GPSes or mapping software. However, still, in 2010, there is information that can be profitably exploited, and with enough feedback, people might be able to outperform the GPS. For instance, if one sees an oil tanker on its side on the suggested route, it is likely that the GPS doesn’t know about this, making it is a good idea to go another way. (Sharad says in such cases, everyone will seek a detour, so staying put may be wisest).

What do you think, dear Decision Science News readers?

Would a counterfactual GPS make people better decision makers because it can teach people when and when not to trust the GPS? Or would it not make people better decision makers because it would encourage folks to believe they can eventually outsmart it (just as many people believe they’ll eventually outsmart the craps table or the stock market)?

It wouldn’t be 2010 if people didn’t love going out, taking pictures with their iPhones and Blackberries and posting them online. It is not only a great way let your friends know what you are up to, it is a great way to unknowingly reveal your location and even home address to complete strangers.

Here’s how it goes down:

1. You take a picture or video on your iPhone, Blackberry, or smart phone
2. You phone adds your latitude and longitude to the photo by default (through its built in GPS)
3. You upload the photo to the Web
4. You add useful tags to the photo, saying it it is your home, etc
5. Anyone who sees the photo can extract the latitude and longitude information from the photo
6. You’ve got a stalker

Annoyingly, the addition of geographic information to your photos is usually tough to switch off without completely switching off the otherwise useful GPS on your phone. It’s a case of dumb defaults where smart defaults are in order.

ICanStalkU.com, which went live in May, is designed to raise awareness of the privacy risks of geo-tagged images. The software behind the site looks for location data in images shared on Twitter. It then runs that data through Geonames, an online service that finds place names associated with latitude and longitude coordinates. The result is a stream of messages that identify the current location of Twitter users.

By tracking images posted on Twitter by a single user it is also possible to plot that user’s movements on a map, say Ben Jackson and Larry Pesce, security consultants based in Boston and Providence, Rhode Island, respectively, and the creators of the site. Jackson says he will unveil this mapping tool next week at the Hackers on Planet Earth conference in New York.

That slightly paranoid feeling one gets when posting content to the Web is now justified. It’s a bit of victory for the intuitive decision maker in all of us that resisting sharing private information when social networks were new, but has since been ignored.

References

Geo-tags reveal celeb secrets

icanstalku.com

A better way to set defaults: Nudge Your Customers Toward Better Choices

Other Decision Science News posts on defaults.

ADDENDUM:

One bit of relief is that Facebook strips EXIF data from photos that get uploaded.

Tweet and location data faked. Maximum likelihood location of such a tweet is estimated to be 41.789841,-87.588823

Rick Larrick recently told Decision Science News that baseball players have been getting better over the years in a couple ways.

First, home runs and strikeouts have increased. The careless or clueless reader might note that this is curious, for from the batter’s perspective home runs are a good thing and strikeouts are a bad thing. What’s going on? Batters may be swinging harder, increasing the chance of both. The purported improvement is a result of the benefit of a home run being greater than the cost of a strikeout. After all, a home run results in at least one run, often more, and runs are a big deal since the typical team earns only about 5 of them per game.

DSN wondered how the players learned to swing harder from one decade to the next. Was it based on feedback from coaches? Or from fans / media attention?

According to Larrick, the number of attempted stolen bases has decreased over the years. Apparently, it is only worth it to steal if one can pull a very high percentage of the time, higher than had been believed in previous years (anyone know the stat?). So while crowds (presumably) like the action of stolen bases, players do not respond by doing it more. Winning seems more important than pleasing the crowd, which is a strike against the fan-feedback hypothesis.

After our post on winning back-to-back baseball games, some folks like our friend Russ Smith made comparisons to the hot hand effect. There is something to it. However, in the baseball example one starts with a prior of .5 (since one doesn’t even know which two teams are playing), while in basketball the chance a pro will make a free throw is about .75 (since one can condition on the player being a pro). What is surprising is that in both cases, the past success tells you next to nothing.

This conversation lead your Editor to find this NY Times article which shows that, surprisingly, pro basketball players are not getting better at free throws over the years.

So, the question to the readers is: Why do some athletic abilities improve as history marches on (e.g., running speeds, batting, base-stealing) and others do not (e.g., free throws)?

P.S. For the record, Decision Science News is not becoming a sports blog. It is just a phase the Web site is going through. That said, there has been interest in seeing this kind of result in other sports, so that analysis will be coming in future posts, in glorious, glorious R and ggplot2. (Don’t know R yet? Learn by watching: R Video Tutorial 1, R Video Tutorial 2)

Photo credit: http://www.flickr.com/photos/cakecrumb/4398699952/. A cupcake was chosen because Jeff gave us empirical evidence that people like cupcakes much more than a control food.

Yes, we all know how to calculate 15% or 20% exactly, but it’s fun to use tipping heuristics and even more fun to make crowded graphs of how they compare to each other. (Sorry for the junky chart. Open for suggestions, in the words of Tom Waits.)

Here are a few tipping heuristics compared to a 15% baseline (which some claim to be 15-20% in NYC):

- Round to the nearest $10, then double the number on the left

- Round to the nearest $5 and throw in $1 for every $5

- Double the tax

There is also the notorious “double the number on the left”, which a friend’s father described as “sometimes they win, sometimes they lose.” DSN doesn’t like this one as it inflicts its damage on small checks, which often require as much waitstaff effort as large ones. If you’re a high roller, it looks pretty safe, however.

Whatever you do, please advocate smart heuristics instead of those undeservedly popular iPhone tipping apps.

What tipping rule of thumb do you use?

Note: Tax figure is New York City restaurant tax, which is something like 8.875%. I regret doing this in Excel instead of R, but it seemed like it would be faster and prettier.

PhD students in Marketing, Psychology, and Economics are now gearing up to get their “packets” ready to mail out by the fourth of July in the hopes of lining up interviews at the annual AMA Summer Educator’s Conference. Each year DSN reprints this sort of “what to expect while you’re applying” guide, first published here by Dan Goldstein in 2005.

WHY AM I WRITING THIS?
I’ve seen the Marketing job market turn happy grad students into quivering masses of fear. I want to share experiences that I and others have contributed, and provide a bit advice to make the whole process less mysterious.

WHY SHOULD ANYONE LISTEN TO ME?
I’ve been on the AMA job market twice (mid 2000s), the Psychology market once (late 90s). As a professor I’ve conducted 20 AMA interviews and been a part of dozens of hiring decisions. I’ve been on the candidate end of about 40 AMA interviews, and experienced numerous campus visits, face-to-face interviews, offers, and rejections. I’m an outsider to Marketing who went on the market older and with more experience than the average rookie (35 years of age, with 8 years of research scientist, postdoc, visiting scholar, and industry positions). I’ve hired many people for many academic posts, so I know both sides.

HOW TO GET INTO THE AMA JOB MARKET
First, at least a couple months before the conference, find where it will be. It’s called the American Marketing Association Summer Educator’s Conference. Strange name, I know. Insiders just call it “The AMA”. Get yourself a room in the conference hotel, preferably on the floor where the express elevator meets the local elevator for the upper floors. You’ll be hanging out on this floor waiting to change elevators anyway, so you might as well start there.

Next, get your advisor / sponsor to write a cover letter encouraging people to meet with you at AMA. It helps if this person is in Marketing. Get 1 or 2 other letters of recommendation, a CV, and some choice pubs. Put them in an envelope and mail them out to a friend of your sponsor at the desired school. It should look like the letter is coming from your sponsor, even though you are doing the actual assembly and mailing. Repeat this process a bunch of times. It’s a good idea to hit a school with 2 packets, 3 if you suspect they’re a little disorganized. Certainly send one to the recruiting coordinator (you might find their name on hiring announcements, which are often sent to your home department’s secretary) and one to your sponsor’s friend. Mail to schools regardless of whether they are advertising a position or not. This is academia: nobody knows anything. This means you may be sending 50 or more packets. You want to have them mailed by the 4^th of July at the absolute latest.

THEN WHAT?
Wait to get calls or emails from schools wishing to set up AMA interviews with you. These calls may come in as late as one week before the conference. Often they come when you are sitting outside having a drink with friends. Some schools will not invite you for totally unknown reasons. You may get interviews from the top 10 schools and rejected from the 30th-ranked one. Don’t sweat it. Again, this is the land of total and absolute unpredictability that you’re entering into. Also, know that just because you get an interview doesn’t mean they have a job. Sometimes schools don’t know until the last minute if they’ll have funding for a post. Still, you’ll want to meet with them anyway. Other times, schools are quite certain they have two positions, but then later university politics shift and they turn out to have none.

After the AMA, you’ll hopefully get “fly-outs,” that is, offers to come and visit the campus and give a talk. This means you’ve made the top five or so. Most offers go down in December. There’s a second market that happens after all the schools realize they’ve made offers to the same person. Of course, some schools get wise to this and don’t make offers to amazing people who would have come. We need some kind of market mechanism to work out this part of the system.

THE “IT’S ALL ABOUT FRIENDSHIP” RULE
Keep in mind that you will leave this process with 1 or 0 jobs. Therefore, when talking to a person, the most likely thing is that he or she will not be your colleague in the future. You should then think of each opportunity as a chance to make a friend. You’ll need friends to collaborate, to get tenure, get grants, and to go on the market again if you’re not happy with what you get.

HOW DO YOU FIND OUT IN WHICH ROOM TO INTERVIEW?
The schools will leave messages for you telling you in which rooms your interviews will be. You’ll get calls, emails, and notes held for you at the hotel reception. Some schools will fail to get in touch with you so you have to try to find them. Many profs ask the hotel to make their room number public, but for some reason many hotel operators will still not give you the room number. Naturally having a laptop and internet connection allows for emailing of room numbers. Try to take care of this early on the first day.

HOW TO TREAT YOURSELF WHILE THERE
My sponsor gave me the advice of not going out at night and getting room service for breakfast and dinner. This worked for me. Also, the ridiculously high price of a room-service breakfast made me feel like I was sparing no expense, which I found strangely motivating.

HOW DO THE ACTUAL AMA INTERVIEWS GO?
At the pre-arranged time you will knock on their hotel room door. You will be let into a suite (p=.4) or a normal hotel room (p=.5, but see below). In the latter case, there will be professors with long and illustrious titles—people you once imagined as dignified—sitting on beds in their socks. The other people in the room may not look at you when you walk in because they will be looking for a precious few seconds at your CV. For at least some people in the room, this may be the first time they have concentrated on your CV. Yikes is right. Put the important stuff early in your CV so nobody can miss it.

THE SEAT OF HONOR
There will be one armchair in the room. Someone will motion towards the armchair, smile, and say, “You get the seat of honor!” This will happen at every school, at every interview, for three days. I promise.

THE TIME COURSE
There will be two minutes of pleasant chit-chat. They will propose that you talk first and they talk next. There will be a little table next to the chair on which you will put your flip book of slides. You will present for 30 minutes, taking their questions as they come. They will be very nice. When done, they will ask you if you have anything to ask them. You of course do not. You hate this question. You make something up. Don’t worry, they too have a spiel, and all you need to do is find a way to get them started on it. By the time they are done, it’s time for you to leave. The whole experience will feel like it went rather well.

PREDICTING IF YOU WILL GET A FLY-OUT
It’s impossible to tell from how it seems to have gone whether they will give you a fly-out or not. Again, this is the land of staggering and high-impact uncertainty. They might not invite you because you were too bad (and they don’t want you), or because you were too good (and they think they don’t stand a chance of getting you).

DO INTERVIEWS DEVIATE FROM THAT MODEL?
Yes.

Sometimes instead of a hotel room, they will have a private meeting room (p=.075). Sometimes they will have a private meeting room with fruit, coffee, and bottled water (p=.025). Sometimes, they will fall asleep while you are speaking (p=.05). Sometimes they will be rude to you (p=.025). Sometimes a key person will miss an early interview due to a hangover (p=.025). Sometimes, if it’s the end of the day, they will offer you alcohol (p=.18, conditional on it being the end of the day).

HOW YOU THINK THE PROCESS WORKS
The committee has read your CV and cover letter and looked at your pubs. They know your topic and can instantly appreciate that what you are doing is important. They know the value of each journal you have published in and each prize you’ve won. They know your advisor and the strengths she or he instills into each student. They ignore what they’re supposed to ignore and assume everything they’re supposed to assume. They’ll attach a very small weight to the interview and fly you out based on your record, which is the right thing to do according to a mountain of research on interviews.

HOW THE PROCESS REALLY WORKS
The interviewers will have looked at your CV for about one minute a couple months ago, and for a few seconds as you walked in the room. They will never have read your entire cover letter, and they will have forgotten most of what they did read. They could care less about your advisor and will get offended that you didn’t cite their advisor. They’ll pay attention to everything they’re supposed to ignore and assume nothing except what you repeat five times. Flouting 50 years of research in judgment and decision-making, they’ll attach a small weight to your CV and fly you out based on your interview.

IF ENGLISH IS NOT YOUR MOTHER TONGUE
Your ability to speak English well won’t get you a good job, but your inability to do so will eliminate you from consideration at every top school. Understand that business schools put a premium on teaching. If the interviewers don’t think you can communicate in the classroom, they’re probably not going to take a chance on you. If you are just starting out and your spoken English is shaky, my advice is to work on it as hard as you are working on anything else. Hire a dialect coach (expensive) or an english-speaking actor or improviser (cheaper) to work with you on your English pronunciation. In the Internet age, it’s quite easy to download samples of English conversational speech, for instance from podcasts, for free. It’s also very easy to get a cheap headset and a free audio recorder (like Audacity) with which to practice.

TWO WAYS TO GIVE YOUR SPIEL
1) The plow. You start and the first slide and go through them until the last slide. Stop when interrupted and get back on track.

2) The volley. Keep the slides closed and just talk with the people about your topic. Get them to converse with you, to ask you questions, to ask for clarifications. When you need to show them something, open up the presentation and show them just that slide.

I did the plow the first year and the volley the second year. I got four times more fly-outs the second year. Econometricians are working hard to determine if there was causality.

HOW TO ACT
Make no mistake, you are an actor auditioning for a part. There will be no energy in the room when you arrive. You have to be like Santa Claus bringing in a large sack of energy. The interviewers will be tired. They’ve been listening to people in a stuffy hotel room from dawn till dusk for days. If you do an average job, you lose: You have to be two standard deviations above the mean to get a fly-out. So audition for the part, and make yourself stand out. If you want to learn how actors audition, read Audition by Michael Shurtleff.

SOCIAL SKILLS MATTER
From the candidate’s point of view, everything is about the CV and the correctness of the mathematical proofs in the job market paper. However, for better or for worse, extra-academic qualities matter. Here are two examples. 1) The Social Lubricant factor. Departments get visitors all the time: guest speakers, visiting professors, job candidates, etc. Some departments are a bunch of folks who stare at their shoes when introduced to a new person. These departments have a real problem: they have nobody on board who can make visitors feel at ease, and sooner or later word starts to spread about how socially awkward the people at University X are. To fix such problems, departments sometimes hire socially-skilled types who know how to make people comfortable in conversation, and who know how to ask good questions during talks. Also, interviewers assume that people who can talk a good game will be star teachers. 2) The Soft Sell factor. Many people succeed in academia not because they are often right, but also because they are masters of making other people feel like they aren’t wrong. Defensiveness or determination to embarrass when responding to critique is a sure way to blow an interview.

HAVE A QUIRK
One of the biggest risks facing you is that you will be forgotten. Make sure the interviewers know something unusual about you. My quirk is that I worked internationally as an actor and theater director for over 10 years; I even had a bit part in a Conan O’Brien sketch on TV. It has nothing to do my research, but people always bring up this odd little fact when I do campus visits. Some bits of trivia are just more memorable than others.

DON’T GIVE UP
Never think it’s hopeless. Just because you’re not two SDs above the mean at the school of your dreams, it does not mean you’re not the dream candidate of another perfectly good school.

Many candidates don’t realize the following: The students are competing for schools but the schools are also competing for students. If you strike out, you can just try again next year. I know a person in Psychology who got 70 rejections in one year. I know a person in Marketing who was told he didn’t place in the top 60 candidates at the 20th ranked school. The subsequent year, both people got hired by top 5 departments. One of them is ridiculously famous!

RUMORS
Gossip can mess with your chances. Gossip that you are doing well can hurt you because schools will be afraid to invite you if they think you won’t come. Gossip that you are doing poorly can hurt you because schools that like you will be afraid to invite you if they think no one else does. Sometimes people will ask a prof at your school if you would come to their school, and the prof will then ask you. To heck with that. Just say that if they want to talk to you, they should talk with you directly.

The danger of rumors can be summed up by the following story. At ACR in 2003, I was having a beer with someone who confessed, “you know, my friend X at school Y told me that they want to hire you, but they’re afraid your wife won’t move to Z”. I was single.

SHARE YOUR OWN AMA HORROR STORIES
I am more than happy to publish anonymous AMA horror stories as part of this post. You can reach me at dan at dangoldstein dot com.

There are those who consider the field of Judgment and Decision Making to be much like the field of Social Psychology, and others who find them as similar as vodka and water.

How can we, as the French say, préciser la différence?

Decision Science News has taken it upon itself to brew up a little textual analysis with the most recent conference programs of the Society for Judgment and Decision Making (SJDM) and the Society for Personality and Social Psychology (SPSP).

DSN counted how many times each word appeared in each program, then made a list of words that occurred in both programs, then deleted words that occurred three times or fewer in either program, then calculated for each word its rate per 10,000 words in each program, then looked at the words that had the greatest difference in the frequency of occurrence between programs, and then struck the uninteresting ones. Here are the results:

Words that are much more common in decision-making than in social psychology
(Rate per 10,000 words)

Words that are much more common in social psychology than in decision-making
(Rate per 10,000 words)

One can see not only differences in topic areas, but methodological differences as well (for example, social psych’s love of 2×2 ANOVA designs and median splits causes the words “high” and “low” to make it onto the list; perhaps this also explains why “positive” and “negative” appear).

Which field would you rather be in?

Decision Science News was having dinner with Shlomo Benartzi recently, not far from his beloved Four Seasons Hotel in New York. At the end of the meal, a chocolate souffle was ordered. Halfway through the souffle, Benartzi asked “would you like any more of this?” Decision Science News declined and watched as Benartzi took the peppermill in hand and peppered the souffle. The website was thinking that this might be interesting to taste, but then salt was added to the mix.

“There,” grinned Shlomo, “now we won’t eat too much. A little trick I learned.”

DSN appreciates learning of a new commitment device, but does find it strange the somewhere inside the present self, the future self can still obtain and operate condiment dispensers.