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August 25, 2017

How much will that Texas rain be

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We are always interested in putting numbers into perspective, so we were interested in this article in which they put the Hurricane Harvey’s rain into perspective.

They’re predicting 30-40 inches of rain in a few days in Texas. They asked an expert to put that into perspective and he said:

Let’s put it in context. Much of the Northeast Corridor — Washington to New York and Boston — maybe receives maybe between 40 and 45 inches of rain a year. Think of all the rain you get in July through Christmas and put that in a couple days. It’s a lot of rain.

It’s easy for us to think in terms of New York City, so we looked up some weather data. See the table at the top of this post (all figures are in inches).

First thing we can notice is that the expert understated things, for New York at least. Thirty five inches would be equivalent to all the rain in NYC from April (not July) to December, inclusive.

But we agree that it’s a lot of rain.

We’ve always had trouble putting rain forecasts into perspective, so here are some rules of thumb we figured out from the data that we’re going to memorize. If you live in the corridor from DC to Boston, you may find these useful.

  • The average amount of rain per rainy day in NYC is .38 inches, which conveniently is about 1 cm.
  • When you hear it’s going to rain 1 cm or 3/8 inch, you can think “no big deal, that’s a typical NYC rainy day”.
  • If you hear it’s going to rain an inch, you can think “oh darn, that’s like three rainy days worth”.

Here’s R-markdown code if you want to play around:

August 16, 2017

Professorship in Operations, Information and Decisions (OID), Wharton, University of Pennsylvania

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The Operations, Information and Decisions Department at the Wharton School is home to faculty with a diverse set of interests in behavioral economics, decision-making, information technology, information-based strategy, operations management, and operations research. We are seeking applicants for a full-time, tenure-track faculty position at any level: Assistant, Associate, or Full Professor. Applicants must have a Ph.D. (expected completion by June 2018 is preferred but by June 30, 2019 is acceptable) from an accredited institution and have an outstanding research record or potential in the OID Department’s areas of research. The appointment is expected to begin July 1, 2018.

More information about the Department is available at: https://oid.wharton.upenn.edu/index.cfm

All interested individuals should complete and submit an online application via our secure website, and must include:

• A curriculum vitae
• A job market paper
• (Applicants for an Assistant Professor position) Three letters of recommendation submitted by references

To apply, please visit this web site: https://oid.wharton.upenn.edu/faculty/faculty-positions/
Further materials, including (additional) papers and letters of recommendation, will be requested as needed.
To ensure full consideration, materials should be received by November 1, 2017.

OID Department
The Wharton School
University of Pennsylvania 3730 Walnut Street
500 Jon M. Huntsman Hall
Philadelphia, PA 19104-6340

The University of Pennsylvania is an affirmative action/equal opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.

August 10, 2017

Postdoc in collective intelligence

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Professor Christopher Chabris is seeking an outstanding researcher for a position as a Postdoctoral Research Fellow at Geisinger Health System. The postdoc will carry out behavioral science research on collective intelligence. Specific research topics may include, but are not limited to:

• Measuring the collective intelligence of small groups and teams
• Understanding factors that increase, decrease, and correlate with collective intelligence, such as cognitive ability, social ability, and team composition
• Novel applications of crowdsourcing
• Tournaments as collective intelligence mechanisms
• Applications of collective intelligence research to business and government problems

The postdoc will report to Professor Chabris, may collaborate with other researchers across disciplines at Geisinger (and with outside colleagues), and will ideally be based in Lewisburg, Pennsylvania. The postdoc will assist in planning and carrying out web-based surveys, laboratory studies, field studies, and/or archival research, and in analyzing general patterns of responses as well as individual differences in collective intelligence studies. The postdoc will be expected to employ a combination of approaches, from the identification and analysis of existing real-world data sets, to the design and analysis of field or lab intervention studies that collect process and outcome measures. Prof. Chabris is particularly interested in candidates with strong statistical and computational skills.

Other duties include contribution to ongoing research projects, preparation of talks and participation in seminars, and drafting reports, grant applications, and papers for publication.

Duration: This is a one-year position with the expectation of renewal for additional years conditional on performance.
To apply: Please send a brief cover letter, C.V., and two representative publications or manuscripts in a single email to chabrispostdoc@gmail.com. Please include names, titles, and contact information for at least two references. Questions about the position may also be sent to the same address. Review of applications will begin immediately and will continue until the position is filled.

Required qualifications:
• A Ph.D. (completed by start of employment) in psychology, economics, decision sciences, management, computer science, or any other relevant scientific discipline
• Training in behavioral science research methods, including experimentation and multivariate data analysis
• Experience with statistical software (preferably R, others acceptable)
• Experience with programming (preferably Python, others acceptable)
Desired qualifications:
• Scientific publications
• Experience in interdisciplinary research, working in collaborative teams, and managing research assistants
• Experience with web programming and design
• Experience with econometrics, simulation, and/or computational modeling


August 1, 2017

Five kinds of weather you’ll meet in America

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The USA is a large country. How different are people’s experiences of the weather depending on where they live?

To look into this question, we downloaded high temperature data for over 1,300 airport weather stations in the contiguous USA for every day for five years (2012-2016 inclusive).

We then used k-means clustering, a workhorse of machine learning, to cluster weather stations according to how similar their high temperatures are.

After some exploring, we settled on five clusters because it captures the gist of what is going on.

The result is shown above, where the letters A through E denote the different clusters (which were ordered by their average temperature in the last week of the year). We see broad East-West stripes, with a few patches of cooler temperatures in the Rocky Mountains, and something unusual going on in coastal California and Oregon.

How different are the clusters? To look at this, we plot the smoothed average high temperature in each cluster for each week of the year.


This was eye opening, and gave us two basic generalizations about the weather in the USA

1. As you more North and South, the temperature patterns are similar, just vertically shifted.
2. The Pacific coast is different

On the Pacific coast, temperatures are pretty steady over the year. California and Florida both have nice warm winters, but when you look at the summers, you can see why they put the movie studios in Hollywood. Low variance makes it easy to plan.

Speaking of variance, look how cluster A (Minnesota and Maine) is actually hotter than cluster D (Pacific Coast) around the middle of the year.

Another cool factoid is that the American experience is pretty similar in summer (less than 20 degrees between cluster A and E) and highly varied in winter (about 45 degrees between cluster D and cluster A).

R, ggplot2, tidyverse, weatherData, etc. code are below for those who wish to reproduce the analysis. We scraped the temperatures ourselves, but we’ll save you the trouble and let you download the temperature data here. Just create a subdirectory called “data” and expand weather_data.zip there. Leave 5 the yearly files in gzip (.gz) because R reads and writes .gz files seamlessly.

July 26, 2017

Judgment and Decision Making leads in open data

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We came across this article that looks at the relationship between sharing data and other variables of interest.

We were especially interested in its Figure 1 which shows the percentage articles having open data. The journals listed are:

JDM – Judgment and Decision Making
PLOS – Public Library of Science
PS – Psychological Science
JBDM – Journal of Behavioral Decision Making
FP – Frontiers in Psychology

Closed circle – No open data policy that year
Open cirle – Open data policy that year

We were delighted to see JDM leading the pack on this important issue. This is due to the tireless efforts of Jon Baron, who almost single handedly produces the journal. From making editorial decisions to typsetting the articles, Jon does it all without a publisher and almost entirely without a budget. It’s an amazing thing, the likes of which we’ve never seen. It’s a testament to what one person can achieve. We are glad to see it getting some recognition here.

Nuijten, M. B., Borghuis, J., Veldkamp, C. L. S., Alvarez, L. D., van Assen, M. A. L. M., & Wicherts, J. M. (2017, July 13). Journal Data Sharing Policies and Statistical Reporting Inconsistencies in Psychology. Retrieved from psyarxiv.com/sgbta

h/t Michael Schulte

July 19, 2017

The SJDM Newsletter is ready for download

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The quarterly Society for Judgment and Decision Making newsletter can be downloaded from the SJDM site:


Dan Goldstein
SJDM Newsletter Editor

July 10, 2017

RAND Behavioral Finance Forum Oct 24, 2017, deadline Friday, Jul 28, 2017

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The RAND Behavioral Finance Forum, in partnership with The Pew Charitable Trusts, will be holding its annual conference this year on Tuesday, October 24, 2017 at The Pew Charitable Trusts’ conference center at 901 E Street, NW in Washington, D.C.

We are inviting submissions for session presentations in the broad areas of behavioral finance, behavioral economics, and financial decision making. The conference will bring together leaders from academia, government, regulatory agencies, and industry to share the latest research and exchange ideas on how to leverage behavioral principles to promote financial well-being. The agenda will allow ample exposure to current policy issues, opportunities to highlight your work, and a focus on building collaborations across individuals, sectors, and institutions.

The RAND BeFi Forum is comprised of academic research sessions consisting of 20 minute presentations, followed by a discussion by practitioners and policymakers. The goal of the conference is to encourage the incorporation of cutting-edge research into policy and financial products that best serve the public’s interests. Presentations are for a mixed audience, and should geared towards providing insights for practitioners and policy makers. See presentations from last year’s conference at https://www.rand.org/events/2016/11/14.html

Submission Guidelines
Interested presenters are invited to submit abstracts by Friday, July 28, 2017. Please send an abstract (500-word limit) summarizing the title and key findings, along with implications/applications that you would like to highlight to befi at rand.org.

Submissions need not reflect completed papers. Key topic areas include, but are certainly not limited to:

1. Incorporation of behavioral and social insights into government programs, into the design of international development interventions, and into emerging products and marketplaces.
2. Promoting healthy financial inclusion among the economically vulnerable
3. Building financial capability and financial literacy
4. Building savings and reducing debt
5. Differences across different ages and stages of life
6. Strategies to increase precautionary savings
7. Asset management and decumulation post-retirement (e.g., retirement income products)
8. Improving consumer credit behavior
9. Use of behavioral finance on the supply side (including how services/products are framed and how it affects consumer choice)
10. Challenges and opportunities facing government regulators in enhancing, evaluating, and protecting individual financial welfare in associated marketplaces; how behavioral interventions can be used to improve unit effectiveness.
11. Personal balance sheet management

Travel and lodging expenses will be provided for invited presenters. Please forward to anyone who might be interested in presenting. Feel free to email us with questions or clarifications at befi at rand.org

July 3, 2017

SPUDM conference, Haifa, Israel, August 20-24, 2017. Program available.

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Please note that the full program of the 2017 Biennial SPUDM 26 conference to be held at the Technion – the Israel Institute of Technology in Haifa, Israel, is now available online at:


For Questions and further assistance please contact the organizing committee at Spudm26 at idc.ac.il

We are looking forward to welcoming you in Haifa.

SPUDM 26 organizing committee:

* Shahar Ayal, IDC Herzelia
* David Budescu, Fordham University
* Ido Erev, Technion – Israel Institute of Technology
* Andreas Glöckner, Göttingen University
* Ilana Ritov, Hebrew University
* Shaul Shalvi, University of Amsterdam
* Richárd Szántó, Corvinus University of Budapest

June 26, 2017

Weighted population density

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In Alaska, there’s about one person for each square mile of land.

You might picture a typical Alaskan not being able to see the next house.

But it’s not that way of course.

Most of Alaska is uninhabited. People have crowded into a few areas.

The average Alaskan experiences a population density of about 72 people per square mile.

That’s a lot more than one.

In the R code below, we roughly estimate the weighted population density for each US state, that is, the population density that the average person experiences. We do this for each state by taking the average of its counties’ population densities, weighted by the population of each county. It would be even better to do this for smaller areas, such as census tracts, but we were too lazy to chase down the data.

In the figure at the top of this post, we see the weighted and unweighted population densities for each state.

Note how New Jersey has a higher population density than New York, but when you look at what the average person experiences, it flips.

Amazingly, the average person in New York State shares a square mile with more than 10,000 other residents!

What states have the biggest ratios of weighted to unweighted densities? The chart below shows states with a 10x or greater ratio in blue.


Here are the states with the biggest ratios:

Alaska – The average person experiences a population density that is 62 times greater than the state’s density.
New York – 39 times
Nebraska – 24 times
Utah – 21 times
Colorado – 16 times
Minnesota – 14 times
Oregon – 13 times
New Mexico – 12 times
Kansas – 12 times
Texas – 12 times
Illinois – 12 times


Want to tinker with this yourself?

Here have the data, or download it from the URL in the source code.

H/T Jake Hofman for getting me to do this and talking R.
H/T to Hadley Wickham for creating the tidyverse.

June 19, 2017

Counterintuitive problem: Everyone in a room keeps giving dollars to random others. You’ll never guess what happens next.

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When we were giving a talk at the Department of Electrical Engineering and Computer Science at Northwestern we met Uri Wilensky, who shared with us a simulation he likes to assign.

Imagine a room full of 100 people with 100 dollars each. With every tick of the clock, every person with money gives a dollar to one randomly chosen other person. After some time progresses, how will the money be distributed?

If on quick reflection you thought “more or less equally”, you are not alone. I asked 5 super-smart PhDs this question and they all had the same initial intuition.

How does the distribution look? Play the movie above to see. Here’s how it works.

The movie shows 5,000 clock ticks in less than a minute.

The Y axis shows the number of dollars each person has. It starts at 45 dollars each.

On the x-axis we have 45 people.

The red bars show the wealth of each person at each tick of the clock.

The blue bars are the same as red bars, but sorted to show how wealth is distributed. The rightmost blue bar is the height of the highest red bar, and so on down.

Don’t believe it? Play with R and tidyverse and gganimate code yourself.

Inequality can arise from seemingly innocuous policies — you need to keep an eye on it.


There’s some confusion in the comments below and on other sites that we thought we’d address. The point is not that some people become rich and never lose their top position. This runs infinitely and will contain every possible sequence of good and bad luck for every person. The richest will become the poorest, everyone will experience every rank, and so on. The interesting thing is that this simple simulation arrives at a stationary distribution with a skewed, exponential shape. This is due to the boundary at zero wealth which, we imagine, people don’t consider when they think about the problem quickly.

See this paper and see mathematician Jordan Ellenberg’s post on this post