New York has, in the last years, joined the list of cities that require calorie counts to be posted on the menus of chain restaurants. Early research suggests that the labeling is not terribly effective, though Decision Science News is skeptical of the skepticism and thinks that long-run effects will emerge.

In any case, if posted calorie counts were really the sledgehammer manipulation they were hoped to be, they’d show an immediate effect. So what are behavioral economists to do? Try related policies.

Sara Bleich, an assistant professor of health policy at the Johns Hopkins Bloomberg School of Public Health, tried an innovative intervention and got some promising results. Her team compare calorie counts to exercise equivalents, so they tried signs saying that a soda:

• Contained 250 calories
• Contained 11 percent of daily calories
• Would take 50 minutes of exercise to burn off*

They found that the exercise message reduced soda purchases by 40 percent while the other framings had no effect.

* (According to fitday.com, which Decision Science News trusts and uses, 30 minutes jogging burns 250 calories, so perhaps the study’s results were obtained by exaggerating the time needed)

REFERENCES

• Exercise Info, Not Calorie Counts, Helps Teens Drop Sodas
• Calorie counts don’t change most people’s dining-out habits, experts say
• Loewenstein, G. (2011). Confronting Reality: Pitfalls of Calorie Posting.  American Journal of Clinical Nutrition, 93(4), 689-94.

Photo credit: http://www.flickr.com/photos/lipsss/3196111994/

At Decision Science News, we have all kinds of numbers memorized: IP addresses, passport numbers, phone numbers, bank account numbers, logarithms, etc. Once you have stuff like this memorized, you’ll  realize how much of a hassle it was to look all this stuff up all the time. Besides, it’s fun.

But how is it done?

It is done with the digit-sound method, which we learned from Professor Jaap Murre’s neuroMod site at the University of Amsterdam. According to Murre, this amazing method traces back to 1648!

You associate a digit with a sound or sounds like this

0 z, s, soft-c ‘z’ is the first letter of zero
1 t, th, d ‘t’ has one downstroke
2 n ‘n’ has two legs (downstrokes)
3 m ‘m’ has three legs (downstrokes)
4 r ‘r’ is the last sound in fouR
5 l ‘l’ or L is the Roman numeral 50
6 soft-g, j, sh, ch ‘g’ when turned around gives ’6′
7 k, q, hard-c, hard-g ‘k’ is made of two 7′s back-to-back
8 f, v ‘f’ when written in script looks like ’8′
9 p, b ‘p’ when turned around gives ’9′

Then, when you want to remember a number, you simply replace the digits with sounds and try to make a vivid phrase. Any vowels or sounds not included in the chart are ignored when you are reading back the phrase. For example, suppose you want to memorize Jenny’s phone number: 867-5309.

Here’s how we might do it:

• Change 867 to sounds, such as “f”, “sh” and “k”
• Change 5309 to sounds, such as “l”, “m”, “s” and “b”
• Now insert vowels and letters not on the chart in order to generate a vivid phrase from those sounds.

We came up with the following dialog involving a parent and child at the dinner table:

–”Fish, ok?”
–”Lame! *sob*”

In this mini play, we have “f”, “sh”, “k” “l”, “m”, “s” and “b” in order, and can easily reconstruct 867-5309. Remember, when reading it back, you ignore all vowels and letters that aren’t in the chart.

The only start-up cost is memorizing the chart, but that’s not hard. There’s even a training tool so you can practice until you have the associations down pat.

Of course, since there can be a few possible sounds for each letter, you need to play around a bit to find a phrase that works for you. To generate possibilities for long numbers, we’ve written the following python script.

NOTE 1: In this code, we’ve simplified the digit-sound method to include fewer letters since we always get confused with hard vs soft letters. We find the simplified method works just fine for us.
NOTE 2: This code is written in a hackish, randomized way that generates wasteful dupes. However, we don’t care since it doesn’t output the dupes, it runs super fast, and if you have a huge number of possibilities, you don’t want to see them all anyway. That said, if people have better implementations, we’re open for suggestions. Thanks to Dan Reeves for tightening up the code a bit!
NOTE 3: You can take the output of this program and hunt for words that fit the patterns with a linux call like this.
grep -E ‘^l[aieouy]*m[aieouy]*n[aeiouy]*$’ wordlist.txt
Where you are looking for words that match “l”, “m”, “n” and wordlist.txt is a list of words, one per line. You can find many wordlists here.

#Usage. Save this file as generate.py
#Then to use, do generate.py 867 (if 867 is the number for
#which you want to generate letters)
from cStringIO import StringIO
import random
from sys import argv
from time import strftime
mstr=str(argv[1])
def genString():
        numsounds = [['s','z'], ['t','th','d'],['n'],['m'],['r'],\
['l'],['j','ch','sh',],['k','q'],['f','v'],['p','b']]
        return [random.choice(numsounds[int(i)]) for i in mstr]
i=0
myDict={}
while i<10000:
        astr=' '.join(genString())
        if not myDict.has_key(astr):
                myDict[astr]=1
                print '%-20s ==> %2d : %20s' % \
(astr,i,strftime("%Y-%m-%d %H:%M:%S"))
        i=i+1

Photo credit: http://memory.uva.nl/memimprovement/eng/phon_peg_1.htm

We’ve written before about avoiding relative risk formats in communicating risks to the general public.

One issue is that subtle changes in terminology lead to vastly different figures. For example, imagine there are two coaches, A and B. When Coach A  is working, the team catches the ball 16 times per 100.  When Coach B is working, the team catches the ball 8 times per 100. Those are the facts. Now lets look at the range of numbers we can generate with relative risks from those facts.

• Going from Coach B to Coach A, the relative chance of catching the ball increases 100%. Let’s publish it in Science!
• Going from Coach A to Coach B, the relative chance of catching the ball drops 50%. Somebody call the newspaper!
• Going from Coach A to Coach B, the relative chance of dropping the ball increases 9.5%. Yawn
• Going from Coach B to Coach A, the relative chance of dropping the ball decreases 8.7%. Zzz….

After reading, many people will forget details such as whether the statistic was going from A to B or B to A,  whether it concerned catching or dropping, or what the sign was, leaving different people with different vague ideas that something in the set of -100%, -50%, -9.5%, -8.7%, 8.7%, 9.5%, 50%, 100% had something to do with coach quality.

We see here that relative risk formats can make a catch rate change from 16% to 8% look like a very different animal than a drop rate change from 84% to 92%. But they are not different animals, they are two ways of referring to the exact same event.

Don’t believe me? Without looking up, try to remember the relative change in drop rate going from Coach A to Coach B.

So. Is the best practice just to give two numbers, that is, tell the readers the catch rate went from 16% to 8%? Or to tell them that the drop rate went from 84% to 92%?

The problem is, even when avoiding relative risk formats, changes between small numbers just look bigger than changes between big numbers, even when the small numbers and the big numbers express the exact same event. That is, 8 vs 16 looks like a bigger deal than 92 vs 84.

Here’s an experimental demonstration. Stanislas Dehaene and colleagues surveyed Mundurucu participants (in the Amazon) and Western participants (in the USA) on where they felt numbers lie on a scale from from 10-100. Specifically, participants had an interface like that pictured above, with 10 dots on the left and 100 dots on the right. They were then shown between 10 and 100 dots and asked to click on the line where they would fall.

Data on the left are from the Mundurucu participants, those on the right from American participants.

Both the Amazonians and the US participants had negatively accelerated response curves. For instance, when shown 30 dots, the Mundurucu clicked the middle of the scale, and the Americans placed 40 dots in the middle of the scale. With such curves, differences between small numbers take up more space on the scale than equal differences between large numbers, which is why a rate change of 16 to 8 sounds like a bigger deal than a  rate change of 84 to 92. This is another instance in which people seem attuned to relative differences over and above absolute differences: the Weber-Fechner idea.

When expressing changes in probabilities, it makes us nervous that people can play games. They can choose between small numbers (here catch rates) and large numbers (drop rates) to play up or play down a point.  This post discusses how this can happen whether one uses relative risk calculations or simply provides before and after numbers. This is one reason we are fans of providing absolute changes in probability. Going from Coach A to Coach B, catches decreased 8 percentage points, which is to say drops increased by 8 percentage points. With this formulation, no matter how the thing is expressed, the only thing the forgetful can get wrong is the sign.

REFERENCES

Dehaene, Izard, Spelke, Pica. (2008). Log or Linear? Distinct Intuitions of the Number Scale in Western and Amazonian Indigene Cultures. Science, 320, pp 1217-1220.

Addition Is Useless, Multiplication Is King: Channeling Our Inner Logarithm

XKCD: Percentage Points

R CODE

options(digits=2)
CoachAcatch=16 # Coach 1 catch rate
CoachAdrop=100-CoachAcatch # Coach 1 drop rate
CoachBcatch=8 # Coach 2 catch rate
CoachBdrop=100-CoachBcatch # Coach 2 drop rate
#Going from Coach A to Coach B
sprintf("Relative risk of catching the ball: %.1f%%",(CoachBcatch-CoachAcatch)/CoachAcatch*100)
sprintf("Relative risk of dropping the ball: %.1f%%",(CoachBdrop-CoachAdrop)/CoachAdrop*100)
#Going from Coach B to Coach A
sprintf("Relative risk of catching the ball: %.1f%%",(CoachAcatch-CoachBcatch)/CoachBcatch*100)
sprintf("Relative risk of dropping the ball: %.1f%%",(CoachAdrop-CoachBdrop)/CoachBdrop*100)
#Going from Coach A to Coach B
sprintf("Absolute risk of catching the ball: %.1f%%",(CoachBcatch-CoachAcatch))
sprintf("Absolute risk of dropping the ball: %.1f%%",(CoachBdrop-CoachAdrop))
#Going from Coach B to Coach A
sprintf("Absolute risk of catching the ball: %.1f%%",(CoachAcatch-CoachBcatch))
sprintf("Absolute risk of dropping the ball: %.1f%%",(CoachAdrop-CoachBdrop))


This week, two fun Econ-Finance papers, both with a Chicago link.

First is Steven Levitt and Thomas Miles‘ analysis of whether poker is a game of skill:

Using newly available data, we analyze that question by examining the performance in the 2010 World Series of Poker of a group of poker players identified as being highly skilled prior to the start of the events. Those players identified a priori as being highly skilled achieved an average return on investment of over 30 percent, compared to a -15 percent for all other players. This large gap in returns is strong evidence in support of the idea that poker is a game of skill

It is a game of skill, they conclude. Read more here.

Next the famous Fama-French duo ask the same question of mutual fund management. They find [drumroll] there is a bit of evidence for skill (and its opposite) in the extreme tails of the distribution:

The aggregate portfolio of actively managed U.S. equity mutual funds is close to the market portfolio, but the high costs of active management show up intact as lower returns to investors. Bootstrap simulations suggest that few funds produce benchmark-adjusted expected returns sufficient to cover their costs. If we add back the costs in fund expense ratios, there is evidence of inferior and superior performance (nonzero true α) in the extreme tails of the cross-section of mutual fund α estimates.

However, they wouldn’t advise trying to find a top manager over passive indexing:

In other words, going forward we expect that a portfolio of low cost index funds will perform about as well as a portfolio of the top three percentiles of past active winners, and better than the rest of the active fund universe.

REFERENCES

Levitt Steven D. and Thomas J. Miles. (2011). The Role of Skill Versus Luck in Poker: Evidence from the World Series of Poker, NBER Working Paper No. 17023. [link]

Fama, Eugene, F. and Kenneth R. French (2010) Luck versus Skill in the Cross-Section of Mutual Fund Returns. The Journal of Finance, LXV(5). [link]

Photo credit: http://www.flickr.com/photos/vizzzual-dot-com/2655969483/

AWKWARD FUTURE SELF INTERACTIONS

Decision Science News readers know about Hal Hershfield and Dan Goldstein‘s experiments in which they exposed people to interactive virtual-reality movies of their future selves to see how it would impact their saving behavior (pictured above). The idea was sent up in three Saturday Night Live fake commercials for Lincoln Financial (hat tip: Jake for alerting us). The SNL interactions with the future self were a lot more awkward than ours, but maybe that’s a good thing for changing behavior.

Links (mildly disturbing):

• http://www.hulu.com/watch/287086/saturday-night-live-lincoln-financial-i
• http://www.hulu.com/watch/287087/saturday-night-live-lincoln-financial-ii
• http://www.hulu.com/watch/287094/saturday-night-live-lincoln-financial-iii

After seeing that, you may want to check out a selection of more wholesome media concerning our research.

Hershfield, H. E., Goldstein, D. G., Sharpe, W. F., Fox, J., Yeykelis, L., Carstensen, L. L., & Bailenson, J. N. (2011). Increasing saving behavior through age-progressed renderings of the future self. Journal of Marketing Research, 48, S23-S37.

Wall Street Journal Article Meet ‘Future You.’ Like What You See?

New York Times Article Some novel ideas for improving retirement income

Allianz report featuring the research Behavioral Finance and the Post-Retirement Crisis

DSN received the following announcement, which should be of interest to agent-based modelers out there. The first tournament led to a Science paper, not a bad outcome.

We would like to invite you, the members of your research group, and your colleagues to participate in The Second Social Learning Strategies Tournament, which we hope will interest you. The tournament, which has a total of 25,000 euro available as prize money, is now open for entries.

The tournament is a competition designed to establish the most effective means to learn in a complex, variable environment.

In recent years, there has been a lot of interest (spanning several research fields, but especially economics, anthropology, and biology) in the problem of how best to acquire valuable information from others. The first Social Learning Strategies Tournament, inspired by Robert Axelrod’s famous Prisoner’s Dilemma tournaments on the evolution of cooperation, attracted over 100 entries from all around the world, and a paper detailing the results was published in the journal Science in 2010*. The high level of interest convinced us that it would be worthwhile to organise a second tournament in which some of the restricting assumptions of the first could be relaxed, so as to explore a broader range of questions.We have received funding for this from the European Research Council, and a committee of world-leading scientists have helped us to design the tournament game, including Sam Bowles (Santa Fe Institute), Rob Boyd (UCLA), Marc Feldman (Stanford), Magnus Enquist (Stockholm), Kimmo Erikkson (Stockholm) and Richard McElreath (UC Davis).

Entrants will be required to submit behavioural strategies detailing how to respond to the problem of resource gain in a complex, variable environment through combinations of individual and social learning.

Three extensions to the first tournament game will (i) explore the effects of learners being able to select from whom to learn, (ii) allow agents to refine existing behavior cumulatively, and (iii) place the action in a spatially structured population with multiple demes. A total of 25,000 euro prize money is available, divided into three 5,000 euro prizes for the best strategy under any single extension, and a 10,000 euro prize for the best strategy under all three extensions.

The competition is now open for entries, with a closing date of

February 28 2012. More information can be found at:

http://lalandlab.st-andrews.ac.uk/tournament2/

We would like to encourage you, the members of your laboratories, and your colleagues and collaborators, to participate in this competition.Please do forward this message to anyone you think might be interested. We would also be grateful if you would print out and post the attached flier on your notice boards, and forward it to anyone you think might be interested.

We hope that this tournament will increase understanding of, and stimulate research on, the evolution of learning, as Axelrod’s tournament did for the evolution of cooperation.

*Rendell et al. (2010) Why copy others? Insights from the Social Learning Strategies Tournament. Science 328: 208-213

Image credit: Goldstein, D. G. (2009). Heuristics. In P. Hedström & P. Bearman (Eds.), The Oxford Handbook of Analytical Sociology. (pp. 140-164). New York: Oxford University Press. [Download]

Health has a major impact on both individuals and nations. Health problems
can impact a person’s emotional, financial and social state; they can also
affect a nation’s financial and social standing. Indeed, countries across
the globe are currently battling the increasing costs of health care
delivery, while others are trying to modernize their systems. Furthermore,
most nations face similar health related challenges such as reducing
unhealthy behaviors (poor diet and smoking), increasing healthy behaviors
(exercising), assisting disadvantaged population gain better access to
health services, and improving adherence to medical treatment.

According to the Surgeon General’s Office the leading causes of mortality in
the U.S. have substantial behavioral components. It is no wonder, therefore,
that both psychologists and economists have been among the pioneers in
studying components associated with health behaviors and have provided a
range of successful behaviorally based prevention and treatment options.
Yet, the sheer extent of these problems calls for a more interdisciplinary
approach. In recent years a growing number of researchers have turned to
behavioral and experimental economics in the hopes of providing additional
insights to facilitate positive health behavior changes.

The aim of this special issue is to bring together the latest research in
behavioral and experimental economics on health related issues, stimulate
cross disciplinary exchange of ideas (theories, methods and practices)
between health economists and psychologists, and provide an opportunity to
simulate novel and creative ways to tackle some of the most important health
challenges we currently face. This special issue will be of interest not
only to a diverse range of researchers but to health professionals,
practitioners and policy makers alike.

With this call for papers, we hope to attract manuscripts that are
outstanding empirical and/or theoretical exemplars of research on any health
related topic from a behavioral and/or experimental economic perspective. We
anticipate studies will focus on a range of topics, including, but not
limited to: Smoking, Dietary choices, Adherence to treatment, Decision
making, Risk taking behavior, Choice architecture, Information asymmetry and
use of monetary incentives to alter behavior. We expect papers to reflect a
variety of methodologies but to highlight implications of the research for
practitioners and policy makers.

Authors should submit a short proposal (maximum of 400 words) that outlines
the plan for a full manuscript* to Yaniv Hanoch, PhD *and* Eric Andrew
Finkelstein*, PhD, guest editors for the special issue, by *March 1, 2012*.
The proposal should outline the study question, methods and findings of the
proposed submission and note how the paper will align with the theme of the
special issue. *Submissions are due August 1, 2012.* Papers should be
prepared in full accord with the *Health Psychology* Instructions to Authors
and submitted through the Manuscript Submission
Portal.
All manuscripts will be peer reviewed. Some papers not included in a
specific special section may be accepted for publication in *Health
Psychology* as regular papers. Please indicate in the cover letter
accompanying your manuscript that you would like to have the paper
considered for the Special Series on Health Psychology meets Behavioral
Economics.

Most inventions fail to be commercialized profitably. Wouldn’t it be nice to be able to predict which ones will? This paper, by Astebro and Elhedhli argues that a simple rule can do quite well making forecasts in a difficult real-world setting.

CITATION
Åstebro, T. and Elhedhli, S. (2006). The Effectiveness of Simple Decision Heuristics: Forecasting Commercial Success for Early-Stage Ventures. Management Science, 52(3), 395-409.

ABSTRACT
We investigate the decision heuristics used by experts to forecast that early-stage ventures are subsequently commercialized. Experts evaluate 37 project characteristics and subjectively combine data on all cues by examining both critical flaws and positive factors to arrive at a forecast. A conjunctive model is used to describe their process, which sums “good” and “bad” cue counts separately. This model achieves a 91.8% forecasting accuracy of the experts’ correct forecasts. The model correctly predicts 86.0% of outcomes in out-of-sample, out-of-time tests. Results indicate that reasonably simple decision heuristics can perform well in a natural and very difficult decision-making context.

The journal Judgment and Decision Making has published the third special issue on “Recognition processes in inferential decision making (III)” edited by Julian N. Marewski, Rüdiger F. Pohl and Oliver Vitouch.  At first, there was supposed to be just one special issue on the topic, but so many good articles were received it was expanded to two (which explains our older post referring to “first of two” special issues) and now it has been expanded again to a third and final issue. All the articles address the recognition heuristic [Goldstein, D. G. & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75-90.]

This volume features a nice article by John Hauser, an MIT Marketing prof and a new contributor to JDM, on how recognition-based heuristics relate to the marketing literature. We at DSN think this is a very promising area for future research.

CONTENTS OF THE THIRD SPECIAL ISSUE
Recognition-based judgments and decisions: What we have learned (so far), pp. 359-380.
Julian N. Marewski, Rüdiger F. Pohl and Oliver Vitouch

Effects of ignorance and information on judgments and decisions, pp. 381-391.
Peter Ayton, Dilek Önkal and Lisa McReynolds

The beauty of simple models: Themes in recognition heuristic research, pp. 392-395.
Daniel G. Goldstein and Gerd Gigerenzer

A marketing science perspective on recognition-based heuristics (and the fast-and-frugal paradigm), pp. 396-408.
John Hauser

Recognising the recognition heuristic for what it is (and what it’s not), pp. 409-412.
Ben R. Newell

The limited value of precise tests of the recognition heuristic, pp. 413-422.
Thorsten Pachur

On the use of recognition in inferential decision making: An overview of the debate, pp. 423-438.
Rüdiger F. Pohl

Photo adapted from S. M. Daselaar, M. S. Fleck, and R. Cabeza. (2006) Triple Dissociation in the Medial Temporal Lobes: Recollection, Familiarity, and Novelty. Journal of Neurophysiology 96, 1902-1911.

This week the reader is directed to Messy Matters to read up on research conducted by Sharad Goel, Duncan Watts and Dan Goldstein in which they hunted for traces of “viral” diffusion on six web platforms including Twitter, Facebook, and Yahoo!. The results run counter to mainstream intuition.