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Taxi Drivers Get Bigger Tips When Paid By Credit Card


I was in a cab in Houston some years ago. The driver said that he did not like it when people paid by credit card, claiming that if the card turned out not to be good, it was his loss. I never really understood this. Perhaps in Houston they used the old-fashioned schoonk-schoonk credit card readers that don't approve the card first? One would have thought the drivers would appreciate having less cash on hand, giving people less reason to rob them.

New York City cabs now have credit card readers and touch-screen computers in the back. They present the passenger with preconfigured tip options, which start at 15%. The passenger can enter his or her own tip, but this requires more typing. What does this do to the amount people tip? The data are in. It turns out that the cabbies get bigger tips when cards are used.

Why? Good question. Perhaps when people tip by cash they tend to round against the driver's favor. Or perhaps the menu of choice options causes people to see the merits of tipping at least 15%. Or perhaps when the least-effort option is to tip 15% or more, people will go down the path of least resistance. Similar, but not totally similar to default effects, a specialty of mine.

P.S. I like to use PayPass. One simply holds his or her keychain (or special credit card) up to a PayPass reader in the back of the cab et voila, payment magically occurs. It is the ultimate in payment decoupling, you literally do not have to touch anything in order to pay. We see a future PhD thesis topic in this somewhere. Would not having to touch anything make you more likely to pay? Would you pay more?

Let's find the world's best players of this game

Do you have brainy friends in your social network? We're trying to find the best players in the world at a game of skill and luck. 

About half a year ago, I posted here a game of skill and luck. Many people played, and it was interesting to see the variation in scores. Some people got it, and some just didn't. Then I had an idea: let's try to find some of the best players in the world at this.

But how? While the world's a big place, through social networks, it can get much smaller.

As my host (I was recently a visiting researcher at Yahoo! Research) Duncan Watts, and others have shown, a short number of email forwards can connect two people living on opposite sides of the world: the "six degrees of separation" idea. The landmark experiment Watts was reviving was carried about by Stanley Milgram and published right here in Psychology Today (Stanley Milgram, "The Small World Problem", Psychology Today, 1967, Vol. 2, 60-67)!

The same idea should work for finding smart people as well. If each person who plays the game then forwards it on to some people they know who are even better at the game, eventually we may come across a person who really gets it and achieves a fantastically long winning streak.

So, let's give it a try. Play the game for yourself. Then, following the instructions in the game, send it on to some friends who will score better. (Don't pass on a link to this blog page, it's important you pass on the link you see when you play the game, this way we can see how far it's spreading).

Good luck! Click here to play and then pass it on to some smart, persistent friends!

How many rich and poor people are there in the USA?


Think you know it all? A good deal of decision-making research centers around people's abilities to make accurate estimates and inferences.

Those who like to test their knowledge might be interested in this fun video game / Web experiment put together by Decision Science News and Lionel Page.

In it, you get to enter your beliefs about the inequality of income in the USA, and at the end, you can find out how accurate you were. Fun!

Give it your best shot at:

Contracts to fight procrastination


Psychologists and economists love to talk about the notion of two selves: present self and future self. It's a nice way to explain the tendency to have one preference about the future, but a very different preference when the future becomes the present. On Sunday, future self might want to go to bed early on Thursday, wake up early on Friday, and hit the gym where it will listen to one hour of "Listen-and-repeat Italian" lessons while mastering the StairMaster.

However, come Thursday evening's dinner with a client, this voice cannot be heard next to that of present self saying yes to dessert, coffee, after-dinner liquer, and a postprandial visit to the pub. Sunday's voice is also asleep Friday morning, when the present self resets the alarm from 5:30 to 8:00.

In a clever April fools joke, the website proposed a solution in the form of an alarm clock that donates money to your most-hated cause should you hit the snooze button. Imagine giving money to a despised politician every time you slept in. Might that get you out of bed?

While the SnuzNLuz alarm clock was a joke, it was a brilliant one. I believe that someone will run with this or a very similar idea. Many future selves find their present selves to be their own worst enemies, and might be willing to pay obedience school tuition. In fact, the much-buzzed about is built on this model: taxing yourself for failing to lose weight, quit smoking, etc.

I am reminded of the time I was a postdoc at Columbia University, on the job market, and deep in a publish-or-perish the phase of my career. I instituted a similar (though lower-tech) mechanism. My rule was that if I didn't write a certain number of pages each day, I would lose five dollars. I think I lost about $60 on the scheme, though it did land me a job I love.

I remember being seriously conflicted about whom to give the money to if I procrastinated. I felt that if I gave it to a good cause, I would be continually justifying my procrastination as charitable. I felt that if I gave it to a bad cause, that would be evil. I also feared that I would start justifying my procrastination by telling myself the bad cause isn't so bad. (Sound far-fetched? The idea that we might infer our preferences from our actions is a key, if not field-defining, idea from social psychology.)

In the end, I chose to leave the money on a seat on the New York subway. Maybe a good person would find it, maybe a bad person would find it, all I was certain of was regretting my procrastination. Given that you're not evil, if you found $5 on the 1/9 train around 2005, I hope that it inched you closer to your goals.

Major Choice Strategies


Rat Cho Un world
My somewhat annotated copy of Hastie & Dawes

Reid Hastie and Robyn M. Dawes, in their classic Rational Choice in an Uncertain World (pp. 232-234), outline some "major choice strategies," stemming from several schools including the Heuristics and Biases, Adaptive Decision Maker, and Fast and Frugal research programs:

Mental Effort Compensatory vs. Noncompensatory? Whole vs. Part Exhausive?
"Search for an alternative that is at least as good as every other alternative on all important attributes and choose it or find an alternative that is worse than any other alternative on all attributes and throw it out of the choice set."
Strategy: ADDITIVE LINEAR (Multi-Attribute Utility Theory)
Mental Effort Compensatory vs. Noncompensatory? Whole vs. Part Exhausive?
"Weight all the attributes by their importance (with reference to the current goals of the decision maker). Then consider each alternative one at a time and calculate a global utility by valuing each attribute, weighting it by its importance, and adding up the weighted values."
Mental Effort Compensatory vs. Noncompensatory? Whole vs. Part Exhausive?
"Consider two alternatives at a time; compare attribute by attribute, estimating the difference between the two alternatives; and sum up the differences across the attributes to provide a single overall difference score across all attributes for that pair. Carry the winner of this comparison over to the next viable alternative and make the same comparison. At the end of this process, the best alternative is the one that has 'won' all the pairwise comparisons."
Mental Effort Compensatory vs. Noncompensatory? Whole vs. Part Exhausive?
"First set 'acceptability' cutoff points on all important attributes; then look for the first alternative that is at least as good as the cutoff values on all important attributes or use the strategy to select a set of good-enough alternatives (all above the cutoff points) for further consideration."
Mental Effort Compensatory vs. Noncompensatory? Whole vs. Part Exhausive?
"First, set 'acceptability' cutoff points on the important attributes; then look for the first alternative that is at least as good as the cutoff value on any attribute or use the strategy to select a set of alternatives that are very good on at least one dimension for further consideration."
Mental Effort Compensatory vs. Noncompensatory? Whole vs. Part Exhausive?
"First, review the attributes and pick the one most important attribute; then choose the beest alternative on that attribute. If there are several "winners" on the first attribute, go on to the next most important attribute and pick the best remaining alternative(s) on that attribute. Repeat until only one alternative is left ... [Similar to the] take-the-best fast-and-frugal heuristic (successful in choice and judgment environments that reflect the distributions of alternatives and attribute values in real, everyday environments). The only adjustment to our description would be to substitute the word 'validity' (predictive accuracy) for 'importance'; order the attributes considered by their past validity in discriminating between good and bad alternatives."
Mental Effort Compensatory vs. Noncompensatory? Whole vs. Part Exhausive?
"Pick the first attribute that is salient and set a cutoff 'acceptability' point on that attribute. Throw out all alternatives that are below the cutoff on that one attribute. Then pick the next most attention-getting attribute, set an 'acceptability' cutoff on that attribute, and again throw out all alternatives that are below the cutoff. Repeat until only one alternative is left."
Mental Effort Compensatory vs. Noncompensatory? Whole vs. Part Exhausive?
"In some choices, people are so poorly informed about the alternatives that they simply rely on 'name recognition.' They choose the first alternative that they recognize ... in many realistic choices and judgments the 'fast and frugal' recognition choice heuristic behaves surprisingly well."
Source: Hastie, Reid & Dawes, Robyn M. (2001). Rational choice in an uncertain world. Sage: Thousand Oaks, CA, pp. 232-234.

About the Authors:



Reid Hastie is a Professor of Behavioral Science on the faculty of the Graduate School of Business in the Center for Decision Research at the University of Chicago. His primary research interests are in the areas of judgment and decision making (legal, managerial, medical, engineering, and personal), memory and cognition, and social psychology. He is best known for his research on legal decision making (Social Psychology in Court [with Michael Saks]; Inside the Jury [with Steven Penrod and Nancy Pennington]; and Inside the Juror [edited]) and on social memory and judgment processes (Person Memory: The Cognitive Basis of Social Perception [several co-authors]). Currently he is studying: the role of explanations in category concept representations (including the effects on category classification, deductive, and inductive inferences); civil jury decision making; the role of frequency information in probability judgments; and the psychology of reading statistical graphs and maps.

Reid Hastie vita



Robyn Dawes is the Charles J. Queenan, Jr. University Professor Ph.D.: University of Michigan Department Member Since: 1985 at the Department of Social & Decision Sciences at Carnegie Mellon University. His research interests spans five areas: intuitive expertise, human cooperation, retrospective memory, methodology and United States AIDS policy. He states, "I write journal articles and books because I believe the information they contain could be valuable -- at least on a "perhaps, maybe" basis. I have never written anything with the expectation that it will sell, or become a "citation classic" (although one of my articles has). I believe that in American culture we are obsessed with outcomes rather than with behaving in ways that tend to bring about the best expected outcomes, while "time and chance" play a very important role. [...] Some of my clinical colleagues claim that feelings are not understood until they can be put into words. My own view is that every translation of a feeling, thought, idea or mathematical form into words involves at least a small element of automatic distortion, often a much larger element."

Prediction markets for the 2008 US election



Lionel Page (University of Westminster), in conjunction with Paul Antoine Chevalier (Paris School of Economics), Dan Goldstein (London Business School & Decision Science News), Leighton Vaughan Williams (Nottingham Trent University), and Peter Urwin (University of Westminster) are pleased to bring you a Web site that uses prediction markets to forecast election outcomes and more.
What candidate would have the highest probability to win the presidential election if nominated?

What candidate has the program which is more likely to foster growth, reduce unemployment and crime?

All these crucial questions for the voters in this 2008 US election cannot be answered using traditional polls. Using prediction markets in a innovative way, the website proposes answers to these questions. presents the estimation of the the conditional probability of success of each candidate if nominated/elected. The numbers are estimated in real time, directly from the prices on specific Intrade contracts.

At this stage of the primary campaign, politimetrics proposes the best answer available the question: "who are the candidates the most likely to win the presidential election if nominated?" Later on in the campaign, we plan to present an even more interesting answer: "which candidate would be the most successful president on a list of issues?" To do so, we have proposed to Intrade a series of specific contracts (listed under the section "Impact of Next President" on their website). Eventually, we hope to be able to answer to questions like:

Is Hilary Clinton more likely to be more effective in managing the economy than John McCain?

Is Mitt Romney more likely to decrease crime than Barack Obama?

R video tutorial number 1


Here a video tutorial on how to get started using the R Language for Statistical Computing.

The tutorial is best viewed in your browser’s full-screen mode, try pressing F11 in Windows.

R is free and open source, and constantly being improved upon by countless contributors worldwide. DSN highly recommends using R.

Topics covered include:

* Downloading and installing R in Windows * The R graphical user interface * Viewing the graphics demo * Vectors and basic stats * Simple plotting

Gut Feelings


Leading Judgment and Decision-Making scholar Gerd Gigerenzer was interviewed in this week about his new book Gut Feelings: The Intelligence of the Unconscious in a piece amusingly titled "Should National Security Depend on Michael Chertoff's Gut?"

The interview, which covers September 11th, fly balls, high school dropouts, illegal drugs, and the prostate, makes for attractive summer reading.

Should you test for statistical significance?



This week, the always-provocative J. Scott Armstrong submits this comment to Decision Science News:

"About two years ago, I was a reasonable person who argued that tests of statistical significance were useful in some limited situations. After completing research for "Significance tests harm progress in forecasting" in the International Journal of Forecasting, 23 (2007), 321-327, I have concluded that tests of statistical significance should never be used. Here is the abstract:

I briefly summarize prior research showing that tests of statistical significance are improperly used even in leading scholarly journals. Attempts to educate researchers to avoid pitfalls have had little success. Even when done properly, however, statistical significance tests are of no value. Other researchers have discussed reasons for these failures. I was unable to find empirical evidence to support the use of significance tests under any conditions. I then show that tests of statistical significance are harmful to the development of scientific knowledge because they distract the researcher from the use of proper methods. I illustrate the dangers of significance tests by examining a re-analysis of the M3-Competition. Although the authors of the re-analysis conducted a proper series of statistical tests, they suggested that the original M3-Competition was not justified in concluding that combined forecasts reduce errors, and that the selection of the best method is dependent on the selection of a proper error measure; however, I show that the original conclusions were correct. Authors should avoid tests of statistical significance; instead, they should report on effect sizes, confidence intervals, replications/extensions, and meta-analyses. Practitioners should ignore significance tests and journals should discourage them.
The paper is followed by commentaries by Keith Ord, Herman Stekler, and Paul Goodwin, and by my reply "Statistical significance tests are unnecessary even when properly done and properly interpreted: Reply to commentaries" , which can be found online at
This is happy news for practitioners, researchers, and students. On the other hand, it might create anguish among faculty who teach people about statistical significance."

The pandemic pandemic



Attention is turning towards the low-probability events that can change the world.

Institutions are undergoing epidemic preparations. Harvard Business Review has an feature on pandemics with two sections written by noted decision science researcher Baruch Fischhoff. A podcast is available here.

A book on high-impact black swans debuted at number five on the NY Times best seller list.

Popular television shows 24 depict again and again how a few individuals, without special titles or power, can topple governments and kill millions.

Decision researchers are investigating how people infer the likelihood of low-probability events from experience and description.

It is a pandemic pandemic?

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