Decision Science News

INTERACTIVE R TOOL FOR GRAPHING PROSPECT THEORY AND MORE

Dan Wall from Columbia University’s Center for the Decision Sciences (*) writes in that he’s developed a new Web app using Shiny and RStudio. It allows users to edit Prospect Theory and Quasi-Hyperbolic Time Discounting Parameters and see the resulting changes to the graphs.

Try it out!

https://decisionsciences.shinyapps.io/Shiny/pt_qtd_shiny.Rmd

We find that it’s great for learning about the function and also great for generating Prospect Theory graphs to include in articles and chapters!

Dan used shinyapps to publish it to the website.

(*) Shout out to CDS, where Decision Science News was launched about a decade ago.

THE “NUDGE UNIT” AKA THE BEHAVIOURAL INSIGHTS TEAM

The UK’s Behavioural Insights Team is recruiting again and has vacancies at various different levels. See the official announcement at http://www.behaviouralinsights.co.uk/jobs

The Behavioural Insights Team is now seeking exceptional candidates for a range of opportunities:

Head of Energy and Sustainability (Principal Advisor) (closes 5 January 2015)
Principal Advisors (closes 5 January 2015)
Senior Advisors (closes 5 January 2015)
Advisors (closes 5 January 2015)
PhD Candidate (closes 19 December 2015)

Role specifications are outlined below, and for any queries please email info@behaviouralinsights.co.uk

Head of Energy and Sustainability

The Behavioural Insights Team (BIT) is looking for an exceptional candidate to become our new Head of Energy and Sustainability. As Head of Energy and Sustainability, you will be a member of the Senior Management Team reporting directly to the Managing Director but with regular policy discussions with the Chief Executive. You will lead a team of 2-4 people, but will be expected to grow this team two or three fold over the next 2 years.

You will interact with senior government officials, Ministers and clients on a regular basis, and be responsible for winning new work relating to energy and sustainability. You will be expected to manage and deliver projects to tight deadlines and budgets.

To be successful you must have in-depth experience in one of these areas and be capable of managing a team of people who have an expertise in the other areas:

Experience working on energy and sustainability policy (having worked in government, academia, industry or for a consulting firm);
Deep understanding of the behavioural science literature and how it can be applied to help solve complex policy problems; or
Ability to design and conduct rigorous evaluations, including Randomised Controlled Trials, difference in differences, regression discontinuity, and propensity score matching.

All candidates must also be able to demonstrate:

Strong leadership and management experience, including supporting team members to develop their own skills and expertise.

For more information see the specification (click here). Applications to be received no later than 9am on 5th January 2014.

Principal Advisors

The Behavioural Insights Team (BIT) is looking for exceptional candidates to become Principal Advisors. This is a senior level in the team, with successful candidates becoming responsible for one or two policy areas.

As a Principal Advisor, you will be a member of the Senior Management Team reporting directly to either the Managing Director or the Chief Executive. You will lead a team of 2-4 people, but will be expected to grow this team two or three fold over the next 2 years.

You will interact with senior government officials, Ministers and clients on a regular basis, and be responsible for winning new work and business development relating to your policy area. You will also take the lead in ensuring that quality is maintained across the business and will be expected to manage and deliver projects to tight deadlines and budgets.

For more information see the specification (click here). Applications to be received no later than 9am on 5th January 2014.

Senior Advisors

The Behavioural Insights Team (BIT) is looking for exceptional candidates to become Senior Advisors. Senior Advisors will work directly to Principal Advisors or the Managing Director/Chief Executive.

As a Senior Advisor, you will interact with senior government officials, Ministers and clients on a regular basis, and be responsible for winning new work and business development relating to your policy area. You will also be responsible for ensuring that projects you are responsible for are managed and delivered to tight deadlines and budgets, and will likely line manage one or more Advisors or Assistant Advisors.

For more information see the specification (click here). Applications to be received no later than 9am on 5th January 2014.

Advisors

The Behavioural Insights Team (BIT) is looking for exceptional candidates to become Advisors. Advisors work on one or more projects, reporting to either a Senior Advisor or Principal Advisor. As an Advisor, you will likely work on two or three different projects across one or more policy area. You will be responsible for delivering pieces of work to tight deadlines. You will be part of a team, and will be managed by either a Senior Advisor or Principal Advisor.

For more information see the specification (click here). Applications to be received no later than 9am on 5th January 2014.

PhD candidate

The Behavioural Research Centre for Adult Skills and Knowledge (ASK) and the Institute of Education’s National Research and Development Centre for adult literacy and numeracy (NRDC) are seeking a PhD candidate to work on a collaborative project.

The candidate will undertake the PhD with Integrated Research and Methods Training at the Institute of Education (IOE) full time over the course of three or four years.

They will be co-supervised by:

an academic from the IOE’s National Research and Development Centre for Adult Literacy and Numeracy (NRDC), who will provide expert guidance on the elements of study related to numeracy and/ or literacy; and
the Head of Research at the Behavioural Insights Team, who will provide expert guidance on running randomised control trials in the field.

Course costs and a salary will be fully funded by the BIT.

For more information see the specification (click here). Applications to be received no later than 5pm on the 19th December 2014.

A BIG DATA ANALYSIS WITH NEW YORK CITY CAB DATA

Students of judgment and decision making (aka behavioral economics) are familiar with the idea that cab drivers work until they hit an income target and then quit, ignoring opportunities to make more money on especially profitable days, such as when it rains. They probably get the idea from hearing people talk about the paper Labor Supply of New York City Cabdrivers: One Day at a Time. We say “hearing people talk about the paper” because that paper does not say that cab drivers make more money when it rains and is otherwise quite cautious (e.g., it concludes “because evidence of negative labor supply responses to transitory wage changes is so much at odds with conventional economic wisdom, these results should be treated with caution.”)

A working paper and presentation by Princeton economist Henry Farber looked at recently released data on every cab ride taken in New York City from 2009-2013 (about 900 million trips, which he down-samples). Some of Farber’s conclusions are:

• Increase in demand and reduction in supply make it difficult to find a taxi in the rain.
• But wage is no higher when it rains.
• Lower supply is not the result of drivers stopping after having reached their target.
• Lower supply is result of less pleasant driving in the rain.

ABSTRACT

In a seminal paper, Camerer, Babcock, Loewenstein, and Thaler (1997) find that the wage elasticity of daily hours of work New York City (NYC) taxi drivers is negative and conclude that their labor supply behavior is consistent with target earning (having reference dependent preferences). I replicate and extend the CBLT analysis using data from all trips taken in all taxi cabs in NYC for the five years from 2009-2013. The overall pattern in my data is clear: drivers tend to respond positively to unanticipated as well as anticipated increases in earnings opportunities. This is consistent with the neoclassical optimizing model of labor supply and does not support the reference dependent preferences model.

I explore heterogeneity across drivers in their labor supply elasticities and consider whether new drivers differ from more experienced drivers in their behavior. I find substantial heterogeneity across drivers in their elasticities, but the estimated elasticities are generally positive and only rarely substantially negative. I also find that new drivers with smaller elasticities are more likely to exit the industry while drivers who remain learn quickly to be better optimizers (have positive labor supply elasticities that grow with experience).

REFERENCE
Farber, Henry S. (2014). Why You Can’t Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers. National Bureau of Economic Research Working Paper Series No. 20604 http://www.nber.org/papers/w20604

H/T Eric Jaffe http://www.citylab.com/weather/2014/10/why-new-yorkers-cant-find-a-taxi-when-it-rains/381652/
Photo credit: https://www.flickr.com/photos/chrisschoenbohm/6186211082/

REDDITORS’ BEFORE AND AFTER MEASUREMENTS ANALYZED

Click to enlarge

How many pounds do you need to lose in order to reduce your waistline by one inch? How many kilos do you need to lose to reduce your waistline by one centimeter?

We wanted to find out. We were having trouble finding published data (though we are expecting some soon), so we turned to Reddit, where the progresspics subreddit contains people’s before-and-after weight change stories. Most posts contain only pictures, but if you do some web scraping, you can find cases in which people post their before-and-after waist measurements.

We found 46 such cases, typed them up, ran them through R, tidyr, dplyr, and ggplot and made the picture above.

Multiple regression tells us that on average, for every 8.5 pounds lost, people dropped an inch off their waist. (And for every 1.5 kilograms lost, people dropped a centimeter off their waist.)

Every 10 pounds lost was accompanied by 1.18 inches of waistline reduction. (Every 5 kg lost was accompanied by 3.33 cm of waistline reduction.)

The picture is a bit rosier if you are losing under 55 pounds (25kg): You only have to lose 6.1 pounds to lose an inch (or 1.1 kg to lose a centimeter).

Want to see the data split out by gender? Voila:

Click to enlarge

Want to make this graph yourself? OK.

Why am I doing this? Hal are following up our face morphing stuff with body morphing stuff.

EVERY U.S. FLIGHT IN 2013 ANALYZED

(click to enlarge)

If you read Decision Science News, you are probably interested in decision making, you probably fly a lot, and you probably like making decisions about flying.

Data of the type the U.S. Government provides enable us to predict how delayed we will be when we fly at various hours of the day.

To make the plot above, we analyzed every single flight in the United States in 2013 for which there were Bureau of Transportation Statistics data. Filtering out flights between midnight and 6AM that leaves us with a little over six million flights (6,283,085 flights, to be precise). The BTS defines delay as the difference between the time the plane actually arrived and the time listed in the computerized reservation system. Many flights got in early, but because we’re just interested in delays (not speedups), we negative delays with zeroes.

What do we learn?

The later you leave, the greater the average delay you will face until around 6PM when things flatten out and 10PM when we see benefits in leaving later. It makes sense that delays increase as the day goes on because, we understand, the primary cause of delays is waiting for the plane to arrive from another city. The first flights out in the morning don’t have this problem.

About 60% of flights had no delay at all (3,726,061/6,283,085 or 59.3% to be precise). This has something to do with padding the expected arrival times in the computerized reservation system. Hence all the “negative” delays.

Leaving at 11PM gives you the same delay as leaving at 11AM. Miracle of miracles. Want a rule of thumb? Try not to leave between 11AM and 11PM.

The arrival and departure curves are quite similar. To save space, we’ll only look at departure delays from here on.

Now, you may be thinking “20 minutes delay if you depart at the worst possible time? That’s not such a big deal.” But remember, these are averages and 60% of the time there will be zero delay. To show you how bad things can get, here we plot the 95th and 75th percentiles of the delay distribution:

If you leave at the worst time of day,  1 time in 4 you’ll be delayed more than 20 minutes, and 1 time in 20  you’ll be delayed more than an hour and a half!

Do different airports have differing delay patterns? One might expect them to due to weather, total number of flights, longitude and the like. We isolate the ten airports with the most passenger traffic below:

In an early analysis, we thought we’d discovered something pretty cool about day of the week effects. We had chosen two months at random and noticed certain days were predictably worse than others. But then, when we looked at two different months, different days emerged as the worst ones. Digging deeper, we found that the day-0f-week effects are attributable mostly to rather random events which change from month to month. Here we look at median (not mean) delays on every day of 2013. Each panel represents one month.

The big spike on April 18, 2013? Five inches of rain in Chicago. December 9th, 2013? Delays are mostly due to winter weather in Texas. These little bumps can really alter the day-of-week findings.

Bon voyage!

R-code, as usual, for those who want it. To get the flight data, just go to … aw heck, I’ll be nice and let you download my cleaned up copy (25 Mb)

This is our first use of Hadley Wickham’s tidyr package. We like it!

ADDENDUM

1. We just learned of some extensive analyses pre-2009 flight data you might find interesting. See the FlowingData blog post. The supplemental information in this paper has some interesting analysis of flight delays. For example, hub airports tend to have a lot of outbound delays because they hold planes when an incoming flight is late. This leads to a lot of arrival delays at non-hub airports. See wicklin-supplemental.pdf page 7.

2. Poking around at this link, we were above to find somewhat steady day of week patterns in this poster which draws on multi-year data.

BATTING AND AGING

With baseball’s World Series drawing to a close, we thought we’d get in one last 2014 post on the US national pastime.

Keeping up with our aging theme, we’ll look at what happens to players’ batting averages as they age. We use the Lahman package in R, which has data from 1871 to 2013. We take the set of players who played in the majors for at least two years and look at the mean batting average at every age.

The green line (above, with smoothed plots, below with raw results with standard error bars) shows this basic result. Pro baseball players have their highest averages just over age 30. The area of the circles is proportional to the number of observations in that point.

When you look at results like those in the green line, however, you must stop to consider that the players who show up in the graph only tell part of the story. At a given age, there were other players who are not plotted because they were cut from the team years before (often due to their poor batting performance).

To illustrate this, at each age, I plot in the blue line the batting average of players who are in their last year of major league play. As one would expect, batting averages are low the year before players disappear from the major leagues. In the red line, we see the performance at each age of players who are not in their last year. For this subset of the data, peak batting average occurs at age 36 and the maximum is a bit flatter.

What is up with the increase in the blue line? The increasing trend is present even if you exclude the first two unusually low points. We are no experts on baseball (or sports of any kind) and are open to suggestions.

One thing to keep in mind is that people whose last year was at age 20 probably only played 2 years (I only considered players who played at least 2 years), while people whose last year was age 40 probably played about 20 years.

As usual, those who want to reproduce this in R are welcome to do so.

SOCIETY FOR JUDGMENT AND DECISION MAKING NEWSLETTER

The quarterly Society for Judgment and Decision Making newsletter is ready for download from the SJDM site.

http://sjdm.org/newsletters/

This issue includes the 2014 conference program.

While we have your attention

• Don’t forget to vote in the 2014 SJDM elections. Polls close on October 26.
• Don’t forget to register for the conference, which takes place November 21-24th in Long Beach, CA! Information at http://sjdm.org

Enjoy!

Dan Goldstein
Your Decision Science News / SJDM Newsletter Editor

HEURISTICS FOR ESTIMATING LIFE EXPECTANCY

Life expectancy: The predicted average number of years remaining in a person’s life.

We once learned from a doctor a rule of thumb for predicting how much longer a person will live (i.e., the life expectancy). The doctor’s heuristic was:

(100 minus the patient’s age) divided by 2

We wanted to see how accurate this rule was, so we downloaded life expectancy data from the US government and plotted the model’s predictions against the official estimates of life expectancy. See above. The black dots are the government’s calculated life expectancies at every age from 30 to 110. The doctor’s model is in blue. It’s pretty good in the 65 to 95 age range.  The doctor worked in a nursing home. The heuristic fit the environment.That said, the doctor’s rule lousy outside that age range. And of course it assumes people will die by 100.

The doctor’s heuristic is a simple linear model. How well does simple linear regression do? We solved for it and plotted it in red above. We’ll see later how they compare in error, but it’s safe to say they’re both pretty lousy.

Let’s fit some better models. Forget survival models. Too hard for mortals to apply. Looking at the life expectancy curve, it seems like a polynomial and a two-part linear function would do a good job. They do.

However, our goal is to get something that someone could do in their head. Something like the doctor’s heuristic, but smarter.

We came up with two candidates.

1) The heuristic bi-linear model. We made this by making the best bi-linear model a bit simpler to apply.
It goes:

If you’re under 85, your life expectancy is 72 minus 80% of your age.
Otherwise it’s 22 minus 20% of your age

2) The 50-15-5 model. This one asks you to remember some key values and then to interpolate between those values. It goes:

The life expectancies of 30, 70, 90 and 110 year olds are about 50, 15, 5, and 0.
Go forth and interpolate!

Here is the performance of the heuristic models:

Pretty awesome!

It’s not a horserace without some measure of accuracy. Below we plot the mean absolute deviation for all the models. Except for the linear models, the heuristics make estimates that are off by less than one year on average. That said, one needs to understand that one’s life expectancy is just the best guess, but there’s a lot of variation around that best guess. The 90% confidence interval around my estimated age at death spans roughly 40 years!

The lesson is, linear fits to life expectancy are bad. Everything else we tested was pretty good. The heuristic bi-linear is especially easy to remember and do in your head.

Can you come up with better heuristics? Here’s some R code to see if you can:

For fun, check out this sweet plot of how the mean absolute error changes as you vary the cut point in the best bi-linear model. The cut point is 30+”cut” in the graph, so we cut around age 80.

ADDENDUM: Dean Foster just stopped by my desk and promoted

• The Gompertz  curve for estimating the probability of dying in the next year
• His life calculator

WHERE THE SJDM MEMBERS ARE FROM IN THE WORLD

Last week, we looked at where the Society for Judgment and Decision Making members were from in terms of academic areas. This week we look at where they’re from geographically.

To start, we note that most members (1195/1714 or 70%) are from the USA.

Inside the USA, there are members in 45 states, as seen at the top of the page. The states with more than 5 members are these:

Outside the US, we have members in North America, Europe, Asia, Oceania, South America and Africa.

And those regions comprise 39 countries. Here are the countries with more than 5 members.

Now, you’re probably wanting to reproduce these graphs. Or, if you’re like most people, you aren’t.

You’ll need directory.csv.gz, state_table.csv.gz, and regions.csv.gz. And the code below.

H/T to @winston_chang’s R Graphics Cookbook, from which I borrowed a code snippet or two.

ANALYSIS OF SJDM MEMBERS’ DEPARTMENTS

Over the next two posts, we’ll look at the membership of SJDM, the Society for Judgment and Decision Making.

Next week, we’ll break down the membership by region, country, and state.

This week, we look at where our members come from in terms of departments. To make the plot above, we had to do a fair amount of recoding, so results are approximate.

R code is below. You can download some anonymized member information here. Save it to your R working directory to proceed.

R CODE TO REPRODUCE THIS FIGURE