This week, Decision Science News is doing a special cross-posting with Messy Matters. The post below is by Sharad Goel and describes work that he and your Decision Science News editor Dan Goldstein are jointly undertaking at Yahoo!

Do you know what the #$*! your social media strategy is? Perhaps it’s “to facilitate audience conversations and drive engagement with social currency”? Or maybe, “to amplify word of mouth by motivating influencers”? Well, given all the lies and damned lies being told about social, fellow yahoo Dan Goldstein and I decided to enter the fray with statistics. We measured the extent to which your friends’ behavior predicts your own, and found that in several consumer domains the effect is substantial, complementing traditional demographic and behavioral predictors.

That friends are similar along a variety of dimensions is a long-observed empirical regularity—a pattern sociologists call homophily. As McPherson et al. write in their canonical review on the subject, “homophily limits people’s social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience.” Turning this statement around, where there is homophily, one can in principle predict an individual’s behavior based on the attributes and actions of his or her associates.

To assess the quality of such network-based predictions, we merged a large social network (based on email and IM exchanges) with offline sales data at an upscale, national department store chain. Thus, for each of over one million users, we had their past purchase amounts in dollars, and had the same information for each of their network contacts. Think about this for a minute: we not only know how much these individuals themselves spent at an offline retailer, but also how much their social contacts spent, a testament to how profoundly the Internet is changing the way we study human behavior. (Despite bolstering social science research, these newfound tools raise serious privacy issues. We left the matching to a third party that specializes in doing this securely, so neither we nor the department store had access to the other’s complete customer database.)

The plot below summarizes our findings. First, as indicated by the top line, consumers whose friends spent a lot, also spent a lot themselves, consistent with the hypothesis that homophily extends to consumer behavior. When friends (alters) on average spent $400 during the six-month observation period, the consumer herself (ego) spent nearly $600, more than twice the typical consumer (indicated by the dotted line). As our aim is prediction, however, the relevant question is not just whether friends are similar in their purchasing behavior, but rather how much information is conveyed by social ties relative to other attributes. One might conjecture that ties simply indicate demographic (i.e., age and sex) similarity, that those who spend a lot are more likely to be middle-aged women—the primary market segment for this department store—and that friends of middle-aged women tend also to be middle-aged women. To test this hypothesis, we first paired each individual with a randomly chosen consumer of identical age and sex. The bottom line shows that this demographically matched group is, perhaps surprisingly, pretty ordinary. In other words, looking only at age and sex, you can’t identify consumers whose friends spend a lot (and who we know spend a lot themselves).

Though it’s standard marketing practice to target consumers based on their demographics, it’s an admittedly noisy profiling technique. So, to put social through the wringer, we next took the “socially select” group—consumers whose friends spent a lot—and matched them to random consumers with identical age, sex, and past purchase amounts. Each social candidate, that is, was matched to a consumer not only of the same age and sex, but one who spent approximately the same amount as the social candidate during the previous six months. Even relative to this formidable baseline, social cues still provide considerable information. As the middle line indicates, knowing a consumer’s age, sex and past purchases, but not that their friends are shopaholics, one would still underestimate their future sales.^[1]

We repeated this analysis for two other domains—examining signups for Yahoo! Fantasy Football, and clicks on ten online banner ads for movies, apparel, government programs, and beyond—again finding that the predictive power of social persists even after adjusting for age, sex, and past behavior. Lest you run off to rejigger your social strategy, we should mention a couple of caveats. First, we have shown that consumers with big-spending friends tend to spend a lot—more, in fact, than demographics and past purchases alone would suggest. But since most people, even premium customers, don’t have shopaholic friends, social cues do not substantially boost average predictive performance. Second, though social signals help predict how much consumers spend, they don’t always help identify which consumers will spend the most. Those who recently spent fifty grand on sartorial elegance are likely to be habitual top spenders, regardless of what you know about their friends.

Assessing the value of social, as with most things, is a messy affair. On the one hand, network ties convey information not captured by the usual egocentric metrics, a conclusion that at the very least we find scientifically interesting. On the other hand, it’s not immediately obvious how to use that knowledge to take over the world. Well, rest assured that an army of social strategy gurus are waiting in the wings with a game-changing, technology-disrupting way to, you know, “leverage the social graph to deliver personalized experiences” or something.

N.B.Thanks to Randall Lewis and David Reiley for acquiring the sales data, Jake Hofman for assembling the email data, and Duncan Watts and Dan Reeves for comments. For related work in the telecom domain, check out the paper, “Network-Based Marketing: Identifying Likely Adopters via Consumer Networks,” by Shawndra Hill, Foster Provost, and Chris Volinsky.

Illustration by Kelly Savage

Footnotes

[1] It’s perhaps tempting to conclude from these results that shopping is contagious (i.e., to assert causation where only correlation has been shown). Though there is probably some truth to that claim, establishing such is neither our objective nor justified from our analysis.

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

The journal Judgment and Decision Making today published a special issue on “Recognition processes in inferential decision making” edited by Julian N. Marewski, Rüdiger F. Pohl and Oliver Vitouch. The special issue turns out to be the first of two special issues, something the editors had not anticipated:

What was originally planned as one issue consisting of about 6 contributions turned into two volumes with about 20 submitted articles, some of which are still under review. All submissions were and are subject to Judgment and Decision Making’s peer review process, under the direction of the journal’s editor, Jonathan Baron, and us.

Here is how the editors describe the contents of the two special issues:

Let us briefly provide an overview of the contents of the two issues. The first issue presents 8 articles with a range of new mathematical analyses and theoretical developments on questions such as when the recognition heuristic will help people to make accurate inferences; as well as experimental and methodological work that tackles descriptive questions; for example, whether the recognition heuristic is a good model of consumer choice.

The forthcoming second issue strives to give an overview of the past, current, and likely future debates on the recognition heuristic, featuring comments on the debates by some of those authors who have been heavily involved, early experiments on the recognition heuristic that were run decades ago, but thus far never published, as well as new experimental tests of the recognition heuristic and alternative approaches. Finally, in the second issue, we will also provide a discussion of all papers in the two issues, and speculate about what we should possibly learn from these papers.

In allocating accepted articles to the two issues, we strove to strike a balance between the order of submission, the order of acceptance, and the topical fit of the papers. We apologize to those authors who feel disfavored by our attempts to establish such a balance; either because they preferred to see their contributions appear in the first, or alternatively, in the second issue.

Also surprising to Decision Science News was that although the topic was recognition processes in inference, all the articles address one particular rule of thumb, Goldstein & Gigerenzer’s recognition heuristic.

Goldstein, D. G. & Gigerenzer, G. (2002). Models of ecological rationality: The recognition heuristic. Psychological Review, 109, 75-90. [Download]

In other RH news, editor Marewski et al has a 2010 paper on the heuristic and editor Pohl also has a 2010 recognition heuristic paper.

CONTENTS OF THE FIRST SPECIAL ISSUE

Recognition-based judgments and decisions: Introduction to the special issue (Vol. 1), pp. 207-215 (html). Julian N. Marewski, Rüdiger F. Pohl and Oliver Vitouch

Why recognition is rational: Optimality results on single-variable decision rules, pp. 216-229 (html). Clintin P. Davis-Stober, Jason Dana and David V. Budescu

When less is more in the recognition heuristic, pp. 230-243 (html). Michael Smithson

The less-is-more effect: Predictions and tests, pp. 244-257 (html). Konstantinos V. Katsikopoulos

Less-is-more effects without the recognition heuristic, pp. 258-271 (html). C. Philip Beaman, Philip T. Smith, Caren A. Frosch and Rachel McCloy

Precise models deserve precise measures: A methodological dissection, pp. 272-284 (html). Benjamin E. Hilbig

Physiological arousal in processing recognition information: Ignoring or integrating cognitive cues?, pp. 285-299 (html). Guy Hochman, Shahar Ayal and Andreas Glöckner

Think or blink — is the recognition heuristic an intuitive strategy?, pp. 300-309 (html). Benjamin E. Hilbig, Sabine G. Scholl and Rüdiger F. Pohl

I like what I know: Is recognition a non-compensatory determiner of consumer choice?, pp. 310-325 (html). Onvara Oeusoonthornwattana and David R. Shanks

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.

If you go to a consumer research conference, you will hear tales of how experiments have undergone particular statistical rites: the attainment of the elusive crossover interaction, the demonstration of full mediation through Baron and Kenny’s sacred procedure, and so on. DSN has nothing against any of these ideas, but is opposed to subjecting all ideas to the same experimental designs, to the same tests, the same alternative hypotheses (typically a null of no difference), and the same rituals.

Zhao, Lynch, and Chen point out in their recent Journal of Consumer Research article that Baron & Kenny’s Mediation Analysis is incredibly popular (ca 13,000 cites between 1986 and 2010), prescribed reflexively, though flawed in ways its users probably aren’t aware of. This article was invited by the journal “to serve as a tutorial on the state of the art in mediation analysis”.

ABSTRACT
Baron and Kenny’s procedure for determining if an independent variable affects a dependent variable through some mediator is so well known that it is used by authors and requested by reviewers almost reflexively. Many research projects have been terminated early in a research program or later in the review process because the data did not conform to Baron and Kenny’s criteria, impeding theoretical development. While the technical literature has disputed some of Baron and Kenny’s tests, this literature has not diffused to practicing researchers. We present a nontechnical summary of the flaws in the Baron and Kenny logic, some of which have not been previously noted. We provide a decision tree and a step-by-step procedure for testing mediation, classifying its type, and interpreting the implications of findings for theory building and future research.

REFERENCES
Baron, Reuben M. and David A. Kenny (1986), Moderator-Mediator Variables Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations, Journal of Personality and Social Psychology, 51(6), 1173–82.

Bullock, J. G., Green, D. P, & Ha, S. E. (2010). Yes, But What’s the Mechanism? (Don’t Expect an Easy Answer), Journal of Personality and Social Psychology, Vol. 98, No. 4, 550–558.

Zhao, X., Lynch, J. G., Chen, Q. (2010).Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. Journal of Consumer Research, 37, 197-206.

R Package for Causal Mediation Analysis

SPSS Code (see the Zhao, Lynch, and Chen article)

Who doesn’t like oxytocin? Who could dislike any substance referred to as a cuddle chemical? The answer may be you, if you are not in with the crowd feeling the effects of the hormone.

Carsten de Dreu and a super-long list of co-authors (listed below), have administered oxytocin to experimental participants and validated its bright side (cooperation among people in a group), but uncovered its dark side (defensive aggression towards people in other groups). Read all about it.

CITATION
Carsten K. W. De Dreu, Lindred L. Greer, Michel J. J. Handgraaf, Shaul Shalvi, Gerben A. Van Kleef, Matthijs Baas,Femke S. Ten Velden, Eric Van Dijk, Sander W. W. Feith. (2010) The Neuropeptide Oxytocin Regulates Parochial Altruism in Intergroup Conflict Among Humans. Science, 328(5984), 1408 – 1411.

ABSTRACT
Humans regulate intergroup conflict through parochial altruism; they self-sacrifice to contribute to in-group welfare and to aggress against competing out-groups. Parochial altruism has distinct survival functions, and the brain may have evolved to sustain and promote in-group cohesion and effectiveness and to ward off threatening out-groups. Here, we have linked oxytocin, a neuropeptide produced in the hypothalamus, to the regulation of intergroup conflict. In three experiments using double-blind placebo-controlled designs, male participants self-administered oxytocin or placebo and made decisions with financial consequences to themselves, their in-group, and a competing out-group. Results showed that oxytocin drives a “tend and defend” response in that it promoted in-group trust and cooperation, and defensive, but not offensive, aggression toward competing out-groups.

H/T author Michel Handgraaf
Photo credit 1: http://en.wikipedia.org/wiki/File:Oxytocin_with_labels.png
Photo credit 2: http://www.flickr.com/photos/markusschoepke/305865244/

John Lynch, Richard Netemeyer, Stephen Spiller, Alessandra Zammit have recently published in the Journal of Consumer Research this article on the propensity to plan and financial well being

ABSTRACT

Planning has pronounced effects on consumer behavior and intertemporal choice. We develop a six-item scale measuring individual differences in propensity to plan that can be adapted to different domains and used to compare planning across domains and time horizons. Adaptations tailored to planning time and money in the short run and long run each show strong evidence of reliability and validity. We find that propensity to plan is moderately domain-specific. Scale measures and actual planning measures show that for time, people plan much more for the short run than the long run; for money, short- and long-run planning differ less. Time and money adaptations of our scale exhibit sharp differences in nomological
correlates; short-run and long-run adaptations differ less. Domain-specific adaptations predict frequency of actual planning in their respective domains. A “very long-run” money adaptation predicts FICO credit scores; low planners thus face materially higher cost of credit.

And while reading the article is fun, it’s also a hoot to take the propensity to plan test yourself, and give it to your friends and family. Give it a whirl, see if it accords with their behavior. Here are the items. Feel free to post your score in the comments.

For each question, answer on a scale from 1 to 6 in which 1 means “I strongly disagree” and 6 means “I strongly agree.”
Propensity to Plan for Money—Short Run:
1. I set financial goals for the next few days for what I
want to achieve with my money.
2. I decide beforehand how my money will be used in
the next few days.
3. I actively consider the steps I need to take to stick to
my budget in the next few days.
4. I consult my budget to see how much money I have
left for the next few days.
5. I like to look to my budget for the next few days in
order to get a better view of my spending in the future.
6. It makes me feel better to have my finances planned
out in the next few days.

Propensity to Plan for Money—Long Run:
1. I set financial goals for the next 1–2 months for what
I want to achieve with my money.
2. I decide beforehand how my money will be used in
the next 1–2 months.
3. I actively consider the steps I need to take to stick to
my budget in the next 1–2 months.
4. I consult my budget to see how much money I have
left for the next 1–2 months.
5. I like to look to my budget for the next 1–2 months
in order to get a better view of my spending in the
future.
6. It makes me feel better to have my finances planned
out in the next 1–2 months.

Propensity to Plan for Time—Short Run:
1. I set goals for the next few days for what I want to
achieve with my time.
2. I decide beforehand how my time will be used in the
next few days.
3. I actively consider the steps I need to take to stick to
my time schedule the next few days.
4. I consult my planner to see how much time I have left
for the next few days.
5. I like to look to my planner for the next few days in
order to get a better view of using my time in the
future.
6. It makes me feel better to have my time planned out
in the next few days.

Propensity to Plan for Time—Long Run:
1. I set goals for the next 1–2 months for what I want
to achieve with my time.
2. I decide beforehand how my time will be used in the
next 1–2 months.
3. I actively consider the steps I need to take to stick to
my time schedule in the next 1–2 months.
4. I consult my planner to see how much time I have left
for the next 1–2 months.
5. I like to look to my planner for the next 1–2 months
in order to get a better view of using my time in the
future.
6. It makes me feel better to have my time planned out
in the next 1–2 months.

ARTICLE TEXT [Download]

MEDIA MENTIONS
Wall Street Journal: http://jcr.wisc.edu/publicity/authors/docs/SUNJ.AA.1A020.A1.361Z2009.pdf

Yahoo Finance: http://finance.yahoo.com/retirement/article/109540/fast-track-to-financial-success

Decision Science News (meta-reference): http://www.decisionsciencenews.com/2010/06/18/the-propensity-to-plan-is-good-for-your-wallet/

You know how in cheesy 80s movies and TV shows there will be a romantic scene, like two young people on a date, and the guy will say something like “I can read minds, you know” and the girl will say “Ok” and scrunch up her eyes and say “What am I thinking about now?” and then the guy will say something particularly cheesy?

Well, in the future they’ll be able to do that scene and the guy will say “apple” and the girl will go “that’s amazing!” and the guy will go “well, the base rate was one in 60″ and the girl will go “can I get out of this fMRI now?”

In any case, read this by Marcel Just et al

A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes

This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating … the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind.

In order words, they can read your mind.

I like this task description:

Task: When a word was presented, the participants’ task was to actively think about the properties of the object to which the word referred.

… I wonder if the subjects were tempted to scrunch their eyes.

Find the full article here (free PDF download): http://www.plosone.org/article/info:doi/10.1371/journal.pone.0008622

REFERENCE: Just MA, Cherkassky VL, Aryal S, Mitchell TM (2010) A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes. PLoS ONE 5(1): e8622. doi:10.1371/journal.pone.0008622

photo credit: The movie “Can’t Buy Me Love”, which doesn’t have the aforementioned scene, but does have the kind of nerdy-guy-dates-popular-girl device that causes writers to trot out the “I can read minds” bit.

The New York Times just ran a piece called Some Novel Ideas for Improving Retirement Income about having people read Victorian novels in order to increase their retirement savings rates.

Actually, that is not true.

But it did feature some newer ideas from Psychology and Behavioral Finance and Economics presented at a Allianz-sponsored event on Monday in NYC on improving retirement decision making, including:

• Work by Hal Ersner-Hershfield, Dan Goldstein, and Bill Sharpe using age-morphed photos of people with varying emotional expressions as a way to increase how connected people feel to their future selves. It is like the scene in a Christmas Carol in which Scrooge sees the future and upon returning promises: “I will live in the Past, the Present, and the Future. The Spirits of all Three shall strive within me. I will not shut out the lessons that they teach.” Like the Distribution Builder, this technology helps people imagine what the future may be like.

Hal, sad about saving now, but psyched about spending later

• Work by Eric Johnson on high sensitivity to loss among the elderly
• Findings by Alessandro Previtero on how recent stock market returns affect people’s decisions to buy annuities (which of course last a long, long time)
• Ideas by George Loewenstein on using mental accounts to help people achieve goals

These projects and more can be read about in the new report from Allianz entitled Behavioral Finance and the Post-Retirement Crisis.

photo credit: www.flickr.com/photos/nrg-photos/4199392655

Even people who aren’t avid baseball fans (your DSN editor included) can get something out of this one.

When two baseball teams play each other on two consecutive days, what is the probability that the winner of the first game will be the winner of the second game?

[If you like fun, write down your prediction.]

DSN’s father-in-law told him that recently the Mets beat the Phillies 9 to 1, but the very next day, the Phillies beat the Mets 10 to 0. How could this be? If the Mets were so good as to win by 8 points, how could the exact same players be so bad as to lose by 10 points to the same opponents 24 hours later?

Let’s call this situation (in which team A beats team B one one day, but team B beats team A the very next day) a “reversal”, and we’ll say the size of the reversal is the smaller of the two margins of victory. In the above example, the size of the reversal was 8.

Using R (code provided below), DSN obtained statistics on all major league baseball games played between 1970 and 2009 and calculated how often each type of reversal occurs per 100,000 pairs of consecutive games. The result is in the the graph above. Big reversals are rare. A reversal of size 8 occurs in only 174 of 100,000 games; a size 12 reversal happens but 10 times per 100k. A size 13 reversal never happened in those 40 years. One might think this is because it would be uncommon for a team that is so good to suddenly become so bad and vice versa, but note that big margins of victory are rare: only 4% of games have margins of victory of 8 points or larger.

Back to our question:

If a team wins on one day, what’s the probability they’ll win against the same opponent when they play the very next day?

We asked two colleagues knowledgeable in baseball and the mathematics of forecasting. The answers came in between 65% and 70%.

The true answer: 51.3%, a little better than a coin toss.

That’s right. When you win in baseball, there’s only a 51% chance you’ll win again in more or less identical circumstances. The careful reader might notice that the answer is visible in the already mentioned chart. The reversals of size 0, (meaning no reversal, meaning the same team won twice) occur 51,296 times per 100,000 pairs of consecutive games.

[At this point, DSN must admit that it is entirely possible that it has made a computational error. It welcomes others to reproduce the analysis with the code or pre-processed data at the end of this post.]

What of the adage “the best predictor of future performance is past performance”? It seems less true than Sting’s observation “History will teach us nothing“. Let’s continue the investigation.

Here were plot the probability of winning the second game based on obtaining various margins of victory in the first game. We simply calculated the average win rate for each margin of victory up to 11 games, which makes up 98% of the data, and bin together the remaining 2%, comprising margins of victory from 12 to 27 points. (Rest assured, the binning makes the graph look prettier, but does not affect the outcome.)

The equation of the robust regression line is: Probability(Win_Second_Game) = .498 + .004*First_Game_Margin which suggests that even if you win the first game by an obscene 20 points, your chance of winning the second game is only 57.8%

Still in disbelief? Here we do no binning and plot the margin of victory (or loss) of the first game winner as a function of its margin of victory in the first game. The clear heteroskedasticity is dealt with by iterative reweighted least squares in R’s rlm command. Similar results are obtained by fitting a loess line. This model is Expected_Second_Game_Margin = -.012 + .030*First_Game_Margin

One final note. The 51.3% chance you’ll win the second game given you’ve won the first is smaller than the so called “home team advantage”, which we found to be a win probability of 54.2% on first games and 53.8% on second games.

When the home team wins the first game, it wins the second game 54.7% of the time.
When the home team loses the first game, it wins the second game 52.8% of the time.
When the visitor wins the first game, it wins the second game 47.2% of the time.
When the visitor loses the first game, it wins the second game 45.3% of the time.

Surprisingly, when it comes to winning the second game, it’s better to be the home team who just lost than the visitor who just won. So much for drawing conclusions from winning. Decision Science News has always wondered why teams are so eager to fire their coaches after they lose a few big games. Don’t they realize that their desired state of having won those same few big games would have been mostly due to luck?

There you have it. Either we have made an egregious error in calculation or recent victories are surprisingly uninformative.

Do your own analysis alternative 1: The pre-processed data
If you wish, you can cheat and get the pre-processed data at http://www.dangoldstein.com/flash/bball/reversals.zip

This may be of interest for people who don’t use R or for impatient types who just want to cut to the chase.

No guarantee that our pre-processing is correct. It should be all pairs of consecutive games between the same two teams.

Do your own analysis alternative 2: The code

I’ll provide the column names file for your convenience at http://www.dangoldstein.com/flash/bball/cnames.txt. I left out a bunch of columns names I didn’t care about. The complete list is at: http://www.dangoldstein.com/flash/bball/glfields.txt

R CODE
(Don’t know R yet? Learn by watching: R Video Tutorial 1, R Video Tutorial 2)

#Home team advantage second game given home lost first game
#Equals 1 - Visitor p win second game given visitor won first game
with(relmerge[relmerge$dx < 0,],mean(dy > 0))