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September 2, 2014

Baseball: Probability of winning conditional on runs, hits, walks and errors

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We have a father-in-law who likes baseball. Occasionally, he asks us to figure out things, which we are more than happy to do. The last request was to figure out:

If a team scores X runs, what’s the probability it will win the game?

Luckily, we had the data to solve this problem (as mentioned in past posts). Looking back over 44 years of baseball games, we looked at how often a home team scored 1 run, and counted how often the home team won. We then looked at 2, 3, 4 runs, up to 11 runs. We stop at 11 runs because we only wanted to compute relative frequencies when there’s a decent amount of data. In all our analyses here, we cut the x-axis when there are fewer than 500 observations per bin. We analyzed the visiting team’s scores separately, to see the effect of the home team advantage.

The result is shown above. If you consistently score 3-4 runs a game, you’re winning about half the games. It’s simply not good enough. Going from 2 runs a game to 6 runs a game means going from winning 25% of the time to winning 75% of the time–all the difference in the world.

Because we had the data handy, we couldn’t help but looking at the same thing for the other key statistics: hits, walks, and errors. Results below.




Want to play with it yourself? The R / ggplot2 code that made this plot is below. ggplot and dplyr are Hadley Wickham creations.

August 28, 2014

How to make a Romeo and Juliet sandwich

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The question often arises: What is the best decision science food?

The answer is quite simple. It’s the Romeo and Juliet sandwich, which is an adaptation of the popular Brazilian desert “Romeu e Julieta”. We came across this delicacy quite accidentally last year and noticed a sudden reinvigoration of our decision science faculties. We then modified it slightly to make it easier for those shopping in North America and Europe by substituting cream cheese for Minas cheese. We also turned it into a sandwich, for portability.

Why is the Romeo and Juliet sandwich the perfect decision science food?

Decision science requires concentration, so its practitioners don’t want something that will put them into a food coma. As Benjamin Franklin said “Eat not to dullness“.

Decision science requires long hours at the computer, so its practitioners want something they can eat neatly at their desks.

Decision science requires satisfying multiple objectives, so its practitioners want something that delivers sweet and salty tastes at once.


  • 1 piece of bread
  • White cheese (e.g., queso fresco, canastra, minas cheese, cream cheese)
  • Guava paste (goiabada)

1) Slice off enough guava paste to cover half a piece of bread


2) Cover half the piece of bread with white cheese and lay the guava paste on top of it. Cut the piece of bread in half.


3) Place the remainder bread atop the sandwich. Cut again.


4) Enjoy!

Bonus points:
1) Use an almond flour pancake instead of a piece of bread

One of our offspring, despite sharing a name with Romeo and Juliet, refuses to eat the Romeo and Juliet sandwich. The reason given is a dislike of guava paste, which the child has never eaten. Neophobia, or fear of the new, has been used to characterize rats’ preferences for foods (p. 76) and childrens’ preferences for babysitters (p. 87). But please don’t let it stand in the way of your doing better decision science.

August 22, 2014

It’s the effect size

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Visualization by Kristoffer Magnusson (@RPsychologist)

Social science research puts the p-value on a pedestal. The p value, or probability of the data given the null hypothesis is true, is seen as the gateway to publication, giving authors an incentive to “p hack“, or use various tricks to get p-values down below .05. And they do this despite the lord loving the .06 as much as the .05. We have written on this before:

Cartoon by Decision Science News

One gripe with the p-value is that statistical significance is cheap. Most plausible hypotheses become statistically significant when the sample size is large enough. Among other things, statistical significance is a function of sample size. In the age of mTurk-scale data, attaining statistical significance is easier than ever. We have heard it said that that if you draw a line anywhere through the belly of the United States, you’ll find a significant different in height on opposite sides of the line because of the massive sample size. But it may be a puny difference.

Enter the concept of effect size. Effect size gives one a way to think about the magnitude of effects, not just the probability of the data given the null hypothesis (aka, the p-value). One popular measure of effect size, Cohen’s D, is discussed along with in the beautiful visualization pictured above. Learn more from the article It’s The Effect Size, Stupid, from which this post gets its name. And learn why you need lots of data to estimate effect sizes from our friends at Data Colada.

August 16, 2014

Student data science presentations on Citibikes and Stop and Frisk

Filed in Articles ,Ideas ,Programs ,Research News
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This summer, researchers at Microsoft Research NYC launched an outreach program called the Data Science Summer School (DS3 for short) an 8 week, 40 hour per week, hands-on course. It was pretty intense. Eight amazing undergrads were admitted and worked hard all summer. The program culminated in two research projects. The presentations were fantastic. We repost the slides, papers, and videos here for your learning pleasure.

Self-Balancing Bikes

By Briana Vecchione, Franky Rodriguez, Donald Hanson II, Jahaziel Guzman


Bike sharing is an internationally implemented system for reducing public transit congestion, minimizing carbon emissions, and encouraging a healthy lifestyle. Since New York City’s launch of the CitiBike program in May 2013, however, various issues have arisen due to overcrowding and general flow. In response to these issues, CitiBike employees redistribute bicycles by vehicle throughout the New York City area. During the past year, over 500,000 bikes have been redistributed in this fashion. This solution is financially taxing, environmentally and economically inefficient, and often suffers from timing issues. What if CitiBike instead used its clientele to redistribute bicycles?

In this talk, we describe the data analysis that we conducted in hopes of creating an incentive and rerouting scheme for riders to self-balance the system. We anticipate that we can decrease vehicle transportations by offering financial incentives to take bikes from relatively full stations and return bikes to relatively empty stations (with rerouting advice provided via an app). We used publicly available data obtained via the CitiBike website, consisting of starting and ending locations, times, and user characteristics for each trip taken from July 2013 through May 2014. Using this dataset, we estimated CitiBike traffic flow, which enabled us to build agent-based simulation models in response to incentives and rerouting information. By estimating various parameters under which to organize incentive schemes, we found that such a program would help to improve CitiBike’s environmentalism and increase productivity, as well as being financially beneficial for both CitiBike and its riders.

For more details, please see the paper and talk.

An Empirical Analysis of Stop-and-Frisk in New York City

By Md.Afzal Hossain, Khanna Pugach, Derek Sanz, Siobhan Wilmot-Dunbar

Between 2006 and 2012, the New York City Police Department made roughly four million stops as part of the city’s controversial stop-and-frisk program. We empirically study two aspects of the program by analyzing a large public dataset released by the police department that records all documented stops in the city. First, by comparing to block-level census data, we estimate stop rates for various demographic subgroups of the population. In particular, we find, somewhat remarkably, that the average annual number of stops of young, black men exceeds the number of such individuals in the general population. This disparity is even more pronounced when we account for geography, with the number of stops of young black men in certain neighborhoods several times greater than their number in the local population. Second, we statistically analyze the reasons recorded in our data that officers state for making each stop (e.g., “furtive movements” or “sights and sounds of criminal activity”). By comparing which stated reasons best predict whether a suspect is ultimately arrested, we develop simple heuristics to aid officers in making better stop decisions. We believe our results will help both the general population and the police department better understand the burden of stop-and-frisk on certain subgroups of the population, and that the guidelines we have developed will help improve stop-and-frisk programs in New York City and across the country.

For more details, please see the paper and talk.

August 9, 2014

ACR 2014 preliminary program published

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The program for the upcoming ACR conference has been published. This can be useful for figuring out if your session was accepted.

ACR 2014 Preliminary Program

While we have your attention, you can register for the conference. Price goes up after September 1, 2014!

August 1, 2014

Behavioral economics job at the US Social Security Administration

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The Social Security Administration’s Office of Retirement Policy (http://www.ssa.gov/retirementpolicy) is looking for a Social Science Research Analyst who will conduct and review complex research on the behavioral and psychological factors that can influence retirement behavior, work effort, and well-being.

Through written papers, oral presentations, and participation in multidisciplinary workgroups, your contributions will inform Agency executives and external policymakers as they work to improve the retirement security of our beneficiaries and the administration of our programs.

For our current vacancy announcement, open from August 1st to August 5th, visit

July 23, 2014

Applied behavioral econ job at Capita in London

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Job advertisement at Capita

This is a senior role in a team which designs services for public and private sector clients which influence citizen and customer behaviours. Recent areas of focus include reducing reoffending, improving health behaviours, encouraging pro-environmental behaviours, prompting channel shift, increasing customer retention, and reducing fraud/error/debt. This is a senior role in a growing team, and offers the opportunity to shape how behavioural change is embedded in services which come into contact with 16 million people a day in the UK. Behaviour change is a critical component of Capita’s vision to achieve transformational change for our clients, while improving the quality of end-user experience. The Senior Manager of Behavioural Insight and Intelligence will be tasked over the longer term with supporting the Director of Behavioural Insight & Intelligence, in the development of a new strategic offer to internal and external clients of Capita plc: the integration of behavioural science, field testing and advanced analytics to continuously optimise outcomes. Masters degree or equivalent required

Specific RESPONSIBILITIES in 3 key areas include:

* Business development: Developing Capita’s transformational partnership offer to public and private sector clients, by identifying opportunities for behavioural science to add value to client solutions, and demonstrating resulting improvements across a range of outcomes.
* Building consensus: Working with a range of internal stakeholders, including service designers and solution developers, to understand user and system requirements and develop workable, impactful, behaviourally-led solutions.
* Operations: Ensuring Capita delivers behaviourally-led solutions in new and existing contracts, and enabling businesses to demonstrate the value of doing so
* Ensuring the Behavioural Insight and Intelligence team works in a fully integrated manner with associated teams within Group Marketing, including Service Design, Digital Innovation and Marketing Communications.
* Working to ensure that the various research and insight capabilities within Group Marketing (behavioural insight, analytics, qualitative research, quantitative survey methods) are coordinated to deliver compelling insight propositions to internal and external clients.

* An expert in behavioural science, educated to at least Masters level in psychological science, social psychology, health psychology, decision science, or similar
* Experience in applied behavioural science, ambitious to be at the leading edge of applying their discipline to real world challenges
* A pro-active self-starter, keen to make services more efficient, effective and engaging for users
* Capable of engaging and influencing senior executives, colleagues and clients
* A strategic, blue sky thinker, who is committed to getting the detail right
* Able to work in a team and individually.
* Quantitative research skills
* Commercial awareness

* As the team grows, there may be the potential to take on managerial responsibilities, so experience in a managerial capacity is desired
Personal Attributes
* Enthusiastic and charismatic, capable of engaging and influencing senior executives, colleagues and clients

July 18, 2014

Conference on Digital Experimentation (CODE), Oct 10-11, 2014 at MIT

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Conference on Digital Experimentation (CODE) 2014

The ability to rapidly deploy micro-level randomized experiments at population scale is, in our view, one of the most significant innovations in modern social science. As more and more social interactions, behaviors, decisions, opinions and transactions are digitized and mediated by online platforms, we can quickly answer nuanced causal questions about the role of social behavior in population-level outcomes such as health, voting, political mobilization, consumer demand, information sharing, product rating and opinion aggregation. When appropriately theorized and rigorously applied, randomized experiments are the gold standard of causal inference and a cornerstone of effective policy. But the scale and complexity of these experiments also create scientific and statistical challenges for design and inference. The purpose of the Conference on Digital Experimentation at MIT (CODE) is to bring together leading researchers conducting and analyzing large scale randomized experiments in digitally mediated social and economic environments, in various scientific disciplines including economics, computer science and sociology, in order to lay the foundation for ongoing relationships and to build a lasting multidisciplinary research community.

Eric Anderson, Kellogg
Alessandro Aquisti, CMU
Susan Athey, Stanford
Eric Horvitz, Microsoft
Jeremy Howard, Khosla Ventures
Ron Kohavi, Microsoft
Karim R. Lakhani, Harvard
John Langford, Microsoft
David Lazer, Northeastern
Sendhil Mullainathan, Harvard
Claudia Perlich, Distillery
David Reiley, Google
Hal Varian, Google
Dan Wagner, Civis
Duncan Watts, Microsoft

Important Dates
Workshop: October 10-11, 2014
Abstract Submission Deadline: August 15, 2014
Notification to Authors: September 1, 2014
Final Abstract Submission: September 12, 2014
Early Registration Deadline: September 19, 2014
Onsite Registration: October 10, 2014

July 8, 2014

A better way to teach? Professors take note.

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With technology, old ways of doing things give way to simply better alternatives. We no longer need to pick up a phone to buy a plane ticket or hail a cab. We no longer need to carry cash around to pay for most things. Soon, students may not need to listen to traditional lectures to learn science and math because technology can make most STEM lessons participatory. But will the new model of instruction be as effective as traditional lecturing? In what its authors call “the largest and most comprehensive meta-analysis of undergraduate STEM education published to date”, the answer seems to be yes.


To test the hypothesis that lecturing maximizes learning and course performance, we metaanalyzed 225 studies that reported data on examination scores or failure rates when comparing student performance in undergraduate science, technology, engineer- ing, and mathematics (STEM) courses under traditional lecturing versus active learning. The effect sizes indicate that on average, student performance on examinations and concept inventories in- creased by 0.47 SDs under active learning ( n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing ( n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on con- cept inventories more than on course examinations, and that ac- tive learning appears effective across all class sizes — although the greatest effects are in small ( n = 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.

Freeman, Scott, Sarah L. Eddy, Miles McDonough, Michelle K. Smith, Nnadozie Okoroafor, Hannah Jordt, and Mary Pat Wenderoth. (2014). Active learning increases student performance in science, engineering, and mathematics, PNAS, 111 (23), 8410-8415; published ahead of print May 12, 2014, doi:10.1073/pnas.1319030111. [Download]

June 30, 2014

SJDM Newsletter and 2014 JDM Conference deadline

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The Society For Judgment and Decision Making is pleased to announce that the current newsletter is ready for download:



Dan Goldstein
SJDM newsletter editor

P.S. Don’t forget the SJDM conference submission deadline is June 30, 2014. The conference will be held November 21-24, 2014 in Long Beach, California. The call for abstracts is available at: http://www.sjdm.org/programs/2014-cfp.html