Personalized license plates

Many years ago, I worked for The Polk Company, a provider of automotive data, much of it collected from state motor vehicle departments. While employed there, I came up with the idea of making a dictionary of personalized license plates.

Think of it. Often while driving, you see a personalized license plate with some odd combination of characters and it takes several seconds before you can figure out what it says. Wouldn’t it be fun to have a book full of these encoded messages? The Polk Company would be the perfect source for this data and I figured it would only take me a few weeks to put together a book and shop it around to a publisher.

Unfortunately, it turns out it had already been done at least once. Within a couple days several of my coworkers told me they had already seen such a book and one of them brought one in and gave it to me. Alas, the book has since been lost. (Sorry, Carol.) A search on Amazon turns up thousands of possible replacements, so maybe there would still be room for one more. Oh well.

One of many fun fact books on license plates. Image from Amazon

I hadn’t thought about that project in quite a while, but within the past two weeks artwork made from personalized license plates has caught my eye twice.

The first was a piece called Preamble by Mike Wilkins, an artist the same age as me. He recreated the preamble of the U.S. Constitution using license plates from all 50 states and Washington, DC. The work was completed in 1987 in celebration of the bicentennial of the Constitution. I saw it while visiting the American Art Museum in Washington.

He uses many of the abbreviations common to personalized license plates. I wonder what the censors at the department of motor vehicles thought of some of them:

WE TH | P PUL | OF TH | UNI | DIDD | ST8S

INNOR | DUR 2 | 4M A | MOR PUR | FEC UNE | NONE

S TAB | LISH | JUSTIZ | N SURE | DOME

ESTIK | TRAN | KWILI | T PRO | VIDE 4 | TH COM

UN DE | FENZ | PRO MOT | THE JEN R | L WEL

FARE N | C CURE | TH BLES | NGS OF | LIBBER | T 2 R

SELVES | N R POS | TERI T | DO R | DANE N

S-TAB | LISH | THIS | CON STI | 2 10 | 4 TH

U NI | TID | ST8S | OF AH | MARE | E CUH

The 51 plates are arrayed in a grid, in alphabetical order. (I think it would have been even more clever to lay them out to form the shape of the continental U.S. with the plates placed close to their geographical location. But that’s me.) You can see a photograph of this wonderful piece at http://www.americanart.si.edu/collections/search/artwork/?id=27722. (More on the reason for my visit to DC, an fMRI scan of my brain, in a future blog post.)

The second piece of license plate art I saw recently was in connection with the famous typographer and designer Jessica Hische. Her work is featured at the cleverly named website, http://jessicahische.is/awesome/.

The Society of Design in Pennsylvania recently invited Ms. Hische to speak at an event. In order to create the invitation, they crafted a heartfelt message. Then 35 members of the society each bought a personalized license plate with a piece of the message. The text was broken up as follows, notice the lack of cute abbreviations:

DEAR JES | SICA PLE | ASE CONS | IDER VIS

ITING SO | CIETY OF | DESIGN I | N PENNSY

LVANIA A | ND SHARI | NG CAPTI | VATING A

ND AMAZI | NG TYPOG | RAPHIC W | ORK THAT

WILL AMA | ZE ASTON | ISH MOTI | VATE AND

PROVE TO | BE BENEF | ICIAL TO | AN ENORM

OUSLY LA | RGE CROW | D THANKS

You can read about the project and see a photo of the actual invitation at http://invitinghische.com/.

 

Estimating peak load from average load

A common task that webmasters are asked to perform is to get bids for hosting a website. When gathering information to prepare a quote, the vendor will often ask what the peak load (in server requests per second) will be. As a webmaster you may well ask, how the heck do I do that?

Estimating page views per month

Estimating peak server requests per second is a four step process. First, we must estimate page views per month. Next, we estimate average page views per second during the heaviest or prime viewing period. Then we estimate peak page views per second during the prime viewing period. Finally, we estimate peak server requests per second during the prime viewing period.

Average page views per month can be obtained by looking at server logs. If server logs are not available or you are creating a new site, then it can be estimated using logs from similar sites. For this blog post, we will assume the website generates an estimated 2.6 million page views per month. (By comparison, this blog generates about 2,000 page views per month, mostly from bots, I think.)

Estimating average page views per prime viewing second

Assume your website that generates 2.6 million page views per month has traffic that is fairly steady all day and all night on every day. That is, of the 730 hours in a month (= 365 days per year / 12 months per year * 24 hours per day) , all of them will be prime viewing hours. In that case, we can calculate the mean page views per second by doing some simple arithmetic.

page views per second = 2.6 million page views per month / 730 hours per month / 3600 seconds per hour = approx. 1 page view per second

But what if traffic to the website isn’t steady. What if people only visit it during work hours? Well, there are about 168 work hours per month compared to about 730 actual hours per month, a ratio of about 4.3 to1. So during prime viewing hours there will be about 4.3 page views per second and 0 page views per second during non-work hours. (This assumes everyone works the same days and same hours regardless of time zone.)

The prime viewing hours for a website can be even more compressed. Let’s say you run a website for NBC and it has a blog that contains a synopsis of the television show Grimm and an update is posted immediately after each new episode airs. In that case, perhaps all of the page views will occur during a 4 hour period starting at 10:00 pm every Friday. Thus, there will be 16 prime viewing hours per month during which there will be 45 page views per second and 0 page views per second during the rest of the month.

The chart below shows the page view distribution for the three cases described above. This model is quite simplified. It can obviously be made more complex by assuming that the prime viewing hour is dependent on time zone, that page views do not drop to zero during the non-prime viewing hours, and having multiple variables that affect page views during a particular hour.

Three ways to achieve 2.6 million page views per month. Image by George Taniwaki

Let’s look at the distribution of page views in more detail. In the four-hour prime viewing period case we said that there were 0 page views per second before and after the prime viewing period and an average of 45 page views per second during the prime viewing period. If the number of page views is constant throughout the prime viewing period, then the distribution curve is rectangular as shown by the blue line in the chart below.

But it is unlikely that the change in page view rate is so abrupt. It is more likely that page views rise steadily to a peak and then fall. If the distribution is triangular and spread across four hours, then the average page views at the maximum point will be 90 per second (=45*2) as shown by the brown line below. If the distribution is bell shaped, called the normal distribution, then the average peak page views at the maximum point will be somewhere in between as shown by the green line below.

Three ways to achieve 625,000 page views in an evening. Image by George Taniwaki

One caveat, I tried to draw the curves in the chart above so that all three would have similar variance but didn’t actually do the calculations to verify it.

Estimating peak page views per prime viewing second

All of the work above was to find the average number of page views per second during the prime viewing time. However, visitors to the website will arrive randomly. So we can expect that there will be some fluctuation in the number of page views during a second. Some seconds during the prime viewing time will have fewer than the average number of visitors and some seconds will have more. We can model this random arrival of visitors using the Poisson distribution.

Since the arrival of visitors will be random, we cannot estimate the maximum number of visitors the website will ever receive in a second. That number is actually infinite. But we can estimate it for a variety of confidence levels, such as 90%, 99%, and even 99.999% (the so-called five 9s availability level). In this case confidence level indicates the proportion of one second intervals that will be below the peak.

Using Excel’s Poisson distribution function we can estimate the ratio between peak page views per second to average page views per second at various confidence levels. The results are shown in the three tables below. Notice that although the average page views per second can be a fraction, the peak page views per second is always an integer.

Avg. page views per month	Avg. page views per second at max. point*	Peak page view per second at 0.9 confidence level	Ratio of peak to average at max. point
1	0.00000165	0 or 1	0 or 604,800
1,000	0.00165	0 or 1	0 or 605
1,000,000	1.65	3	1.81
2,600,000	4.30	7	1.63
1,000,000,000	1653	1706	1.03

Avg. page views per month	Avg. page views per second at max. point*	Peak page view per second at 0.99 confidence level	Ratio of peak to average at max. point
1	0.00000165	0 or 1	0 or 604,800
1,000	0.00165	0 or 1	0 or 605
1,000,000	1.65	5	3.0
2,600,000	4.30	10	2.3
1,000,000,000	1653	1749	1.06

Avg. page views per month	Avg. page views per second at max. point*	Peak page view per second at 0.99999 confidence level	Ratio of peak to average at max. point
1	0.00000165	0 or 1	0 or 604,800
1,000	0.00165	1	605
1,000,000	1.65	9	5.4
2,600,000	4.30	16	3.7
1,000,000,000	1653	1830	1.11

*Assumes 168 prime viewing hours per month with uniform distribution

Also notice that when the average page views per second is low, the peak page views per second can have two solutions, 0 or 1. These cases occur when the average page views per second is below 1- confidence level. For instance, if all you care is that your web server can handle all the traffic 99% of the time, and your average traffic is less than 0.01 page views per second you don’t need a web server at all! That’s because 99% of the time (during the prime viewing period), there is no traffic to your website.

However, if your goal is to be able to serve 99% of your visitors during the peak viewing time, then you need a web server than can deliver at least one page view per second. And if you provide a web server to deliver one page every 100 seconds, your ratio of peak to average will be 100. Providing a complete web server ready to serve the rare visitor results in tremendous overhead costs, which is why cloud computing, where resources are shared among many websites, is becoming so popular.

Finally, notice that when the average page views per second is high (say 1,000) , then the ratio of peak to average is close to 1 and does not grow very much even at high availability (or confidence) levels. At high levels of average page views, the error in estimating the average number of page views per second is likely to be much greater than the error introduced by ignoring the random distribution of page views per second.

Estimating peak server requests per prime viewing second

Our last step is to estimate the number of server calls generated by a single web page request from a user. A typical web page consists of a static html file plus one or more images, videos, ads, and JavaScript widgets displayed on the page. (In the case of dynamic pages, the content will be generated on the server, usually as a jsp file, or a aspx file if you are using the Microsoft .NET Framework.) If the page is not cached, sending all of the page contents may take over 100 requests to the server.

Assume your website consists of a single page that contains 100 items and all of those items reside on a single server. Now assume a single user calls for that page and you don’t want the user to have to wait more than one second before being able to interact with any part of it. That means the web server will need to be able to handle at least 100 requests per second per page view. (There are other potential bottlenecks in rendering the web page including Internet traffic, ISP speed, and the speed of the client computer, but we’ll ignore these for purposes of this blog post.)

Using the five nines confidence level, the final results for page views and server calls are shown in the table below. For our website with an expected 2.6 million page views per month, we need a web server that can handle 1,600 requests per second.

Avg. page views per month	Avg. server requests per month*	Peak page view per second at 0.99999 confidence level**	Peak server requests per second at 0.99999 confidence level*,**
1	100	1***	100***
1,000	100,000	1	100
1,000,000	100,000,000	9	900
2,600,000	260,000,000	16	1,600
1,000,000,000	100,000,000,000	1830	183,000

*Assumes 100 server calls per page view
**Assumes 168 prime viewing hours per month with uniform distribution
***Assumes goal is to satisfy 99.999% of visitor requests, not 99.999% of time

*****

Update: If your website is one of many hosted on a single server, then you should skip the calculations for estimating peak page views per prime viewing second. That’s because traffic to your site will be combined with traffic to other sites. In that case, it is up to the company hosting the sites to combine the average traffic from all the sites first, then calculate the peak page views based on their promised availability.

 

Comparing apples to orangeries*

A Dec 2011 article in The Fiscal Times purports to show that eating at restaurants is cheaper than cooking at home. It’s an intriguing idea that has appeared in many articles in the past. However, the analysis presented in The Fiscal Times article is flawed and the conclusions are not supportable.

Before going into the specifics of the errors in The Fiscal Times article, let’s consider how one could compare whether cooking at home is more expensive than eating at restaurants. The typical cost-benefit analysis for eating in versus dining out goes something like as follows. Cooking a meal at home isn’t free. From a classical economic point of view one should include the opportunity cost of the time needed to buy groceries , drive it home, store it,  prepare a meal, and clean up afterwards. Further, one should include the implicit rental value of the automobile used to transport the groceries and the kitchen and dining room used to prepare and serve the meal.

However, shopping, cooking, and cleaning are not just chores that one is required to do. They are a form of entertainment, social interaction, and a way to share your skills with others as Nathan Myhrvold insightfully states in this Dec 2011 Slate interview. The cook receives utility from hosting a meal, even if it is a regular daily event. Naturally, if one hates to shop, cook, or clean, then there can be disutility as well. When deciding whether to eat at home or dine out, a person will want to maximize the expected utility from the decision.

Examining the wrong factors

The Fiscal Times article briefly mentions some of the above factors, but then totally ignores them when doing the price comparisons. Instead, it mentions differing inflation rates between in-home meals and restaurant meals. Relative inflation rates should be irrelevant to the decision to eat at home or dine out. The author also throws in a few additional factors that also seem to be irrelevant in comparing costs,

“We also didn’t factor in whether one meal or another would be healthier, or friendlier to the environment. But that’s part of the point: Eating right and finding the extra savings that could be had by comparison shopping comes with a time trade-off that many families can’t afford to make these days.”

Hard to interpret charts

The Fiscal Times article has two time series charts which I will reproduce below. Some of the problems I found in the first chart:

1. For some reason the first chart is labeled Chart 2 and the second is labeled Chart 1.
2. Chart 2 (the first chart) has two different scales (left scale has a range of 1.4% while the right scale has a range of 0.4%) even though both display values from the same dataset (percent share of consumption). This means the data using the right scale will appear to be more variable
3. The black arrows both point toward the right scale, though “Grocers” (what’s with the quotation marks?) says it is set to the left hand scale (LHS)
4. Neither scale shows the 0% origin point or the 100% end point (Note that if the scale did go from 0 to 100%, then there would be no need for two different scales.
5. Assuming the left scale applies to “Grocers” and the right scale to “Restaurants”, then “Grocers share is always above “Restaurants”. It does not cross as the chart shows
6. There is no source attribution for the data so no way to judge how valid it is or to review the original data

The second chart (entitled Chart 1) also has several flaws.

1. The color code has been reversed. Dining out share was shown in the blue line in the first chart, while inflation is shown in gold. Similarly, eat at home share was shown in gold in the first chart, while inflation is in blue
2. The label for each line has changed. “Restaurants” in the first chart is now called Food away from home while “Grocers” is now Food at home
3. The use of different labels makes one wonder if the same assumptions, data sets, and cost allocations are used in the two charts and whether the same analysts produced both charts. My guess is no, which means the two charts cannot be used together
4. As mentioned above, relative inflation rate should not directly impact the consumer’s choice to eat at home or at a restaurant, so this chart isn’t very useful

Nonequivalent price comparisons

The Fiscal Times article includes a slideshow that compares the cost of selected meals at restaurants with the cost of preparing the meal at home. In five out of six cases, the restaurant meal is cheaper.

If you only consider the price of store-bought food to the price of a cooked meal at a restaurant, there is probably no way the prices of food ingredients in a competitively priced retail store could exceed the price in a non-subsidized restaurant. Certain restaurants can serve meals at lower than expected prices because of subsidized food (school lunch programs), volunteer labor (homeless shelters or church meal programs), or subsidized rent (canteen stores or cafeterias in office buildings).

So how did The Fiscal Times get these unlikely results? I think the following errors were made:

1. The restaurant meal prices are for a single serving while the grocery store prices are for full cans, boxes, or other package. This will provide much more food than the restaurant meal
2. The grocery store prices are for FreshDirect, a grocery delivery service in New York. Delivery groceries are more expensive than self-serve and NYC is the most expensive city in the U.S.
3. The restaurant meal prices exclude the tip
4. The grocery store prices include some prepared deli foods. Grocery store deli food can be more expensive than restaurant food since it is an impulse buy

*An orangery is a greenhouse that is designed to look like a normal building. One of the most famous orangeries is the one at the Royal Botanic Gardens in London. It is now used as a restaurant.

 

Combining implicit consent with opt-out

[Note: This is the third blog post comparing opt-in and opt-out organ donor registration. The series starts here.]

The previous blog post argued that switching from opt-in to opt-out could increase the number of people on the organ donor registry but could actually reduce the number of organs recovered.

That is because an opt-out process creates ambiguity about the intent of those on the organ donor registry. This would make the Uniform Anatomical Gift Act (UAGA) harder to enforce.

One way to avoid this problem is to couple the use of an opt-out-donor registry with increased training of clerks at the DMV to inform each customer that they will be added to the registry unless they opt-out. In addition to training costs, there will be increased labor costs since each customer transaction may be about a minute longer as the clerk explains what the organ donor registry is and sells the benefits of organ transplantation to the customer.

This extra effort to educate the public is needed to get implicit consent from the driver. Unlike presumed consent where the customer is never told that a decision is being made for them, implicit consent creates a true decision. Unlike mandated choice where the customer is forced to make a cognitively complex choice in a short time span, implicit consent relies on framing to make the default option (the one most people will pick) the one that is most beneficial for society.

Using the same hypothetical data presented in the prior blog posts, I have created a table showing the organ recovery rate when combining opt-out with implicit consent. Assume that an opt-out registration system results in 88% of drivers registering to be organ donors (same rate as in table 2 of that blog post). Of these, the OPO is able to get 99% of families to cooperate (same as in table 1). The OPO does not approach the families of patients who were on the opt-out list (same as in table 2). The overall organ recovery rate is 87%, significantly higher than the 81% rate in the opt-in case or the 79% rate for opt-out without implicit consent. This appears to be a big win.

Implicit conse	nt case	Patient on	organ registry
		Yes	No	Row total
Family agrees to	Yes	87 (99%)	0 (0%)	87
donation	No	1 (1%)	12 (100%)	13
	Col. total	88	12	100

By combining opt-out with implicit consent, 88% of drivers register and 87% of organs are recovered

What impact could the combination of opt-out and implicit consent make in the United States? That is difficult to predict since no state has attempted to implement them together. Legislation was introduced to implement opt-out and presumed consent in New York last year by an assemblyman whose daughter had received two kidney transplants. But the bill never made it out of committee. (See debate in New York Times May 2010.) Similar legislation was introduced in Colorado earlier this year but was withdrawn after public protests and consultation with Donor Alliance, the local OPO.

Why isn’t the combination of opt-out and implicit consent gaining political traction in the U.S.? Most likely it is because the training required to implement implicit consent correctly would be expensive. Even with training, at least one unwilling donor family will probably request an injunction against the OPO. The potential result of this litigation was described in the last blog post. The resulting media coverage and lobbying would likely put pressure on the state legislature to eliminate the opt-out nature of the registry. It could also cause them to revoke the UAGA. This could make it harder for OPOs to recover organs than before the switch to opt-out since they currently can recover organs without consent of the family under opt-in.

Finally, if the driving public feels it is being coerced into becoming donors, it may result in falling donation rates (higher opt-out rates) and reduce trust in the healthcare system. Overall, the combination of opt-out and implicit consent just doesn’t seem like a winning strategy to increase organ recovery rates.

Much thanks to thank Alexandra Glazier, Vice President & General Counsel at The New England Organ Bank, for clearly explaining that adopting an opt-out registration process does not automatically result in adopting presumed consent. Each issue needs to be analyzed separately.

 

Uncertainty and opt-out

In the previous blog post, I showed how the registration of organ donors using a Boolean variable leads to some drivers to be misclassified. I also showed how requiring drivers to opt-in to the donor registry causes less severe types of misclassifications than opt-out.

Now I will discuss how opt-out can result in uncertainty in the composition of drivers listed in the registry. This uncertainly can impact the behavior of organ procurement coordinators and family members.

Role of certainty in interactions between counselors and family

In states that maintain a donor registry, they share the list of names on the registry with the organ procurement organization (OPO) that is responsible for recovery and distribution of organs for transplant. If a patient dies under conditions that allow the organs to be recovered, an organ recovery coordinator at the OPO will see if the patient’s name is on the organ registry.

Under opt-in, if the patient’s name is on the registry the coordinator can be fairly certain the deceased patient wanted to be a donor (categories 1a and 1b as defined in the previous blog) and can confidently tell the family this and proceed with recovery. Under the Uniform Anatomical Gift Act enacted in most states, a gift by a donor cannot be revoked by the family.

If the patient’s name is not on the registry, the intent of the patient isn’t known. Perhaps the patient wanted to donate (category 3a), didn’t want to donate (2b or 3b), or wanted the family to decide (2a or 4). The coordinator can say the patient’s wishes were not known and politely request the family to make an organ donation on behalf of the deceased patient.

Under opt-out, there are more categories of drivers included in the registry. This reduces the certainty in the composition of the donor registry. This is true even if no drivers are misclassified (i.e., no drivers fall into categories 3a, 3b, or 4), This uncertainty will have an impact on the behavior of the coordinators.

Specifically, if the deceased patient’s name is on the registry, the coordinator cannot be certain the deceased patient wanted to be a donor. She must rely on presumed consent. However, if the family complains that it was not the deceased patient’s intent to be a donor, then the ambiguous nature of the composition of the registry may lead to a delay, which will make recovery impossible. If the OPO pushes the issue, eventually, a court case may resolve the issue, but if the ruling is in favor of the patient’s family, then the entire registry is placed at risk.

Conversely, if the patient’s name is not on the registry, then having the coordinator approach the family to request a donation is also problematic since a donation would require the family to override the wishes of the deceased. If that is allowed, then the wishes of the deceased should be allowed to be overridden if she is on the organ donor registry as well. Again, if the OPO pushes the issue, the organ donor registry is placed at risk.

A hypothetical example of outcomes

Let’s look at some hypothetical numbers to illustrate a possible outcome. In the first table below, the state has an opt-in registration system and has a 64% registration rate. (This is very high, but is achieved in Washington, the state where I live.) The OPO approaches the family of every patient who dies under conditions that allow the organs to be recovered. For patients on the registry it works to enforce the UAGA and gets 99% of families to cooperate in time. For patients not on the registry, it works hard to persuade the family to donate and gets half to cooperate. Overall 81% of organs are recovered.

Opt-in case		Patient on	organ registry
		Yes	No	Row total
Family agrees to	Yes	63 (99%)	18 (50%)	81
donation	No	1 (1%)	18 (50%)	19
	Col. total	64	36	100

Under opt-in, 64% of drivers register to be donors and 81% of organs are recovered

Now suppose that the state switches to an opt-out registration system and the registration rate rises to 88%. However, the cooperation rate among families drops from 99% to 90%. Also, the OPO does not approach any of the families of patients who were on the opt-out list. Overall, the organ recovery rate drops to 79%, lower than it was before the switch. Naturally, I set the numbers to make my case, but it illustrates that switching from opt-in to opt-out will not on its own automatically ensure that donation rates will increase.

Opt-out case		Patient on	organ registry
		Yes	No	Row total
Family agrees to	Yes	79 (90%)	0 (0%)	79
donation	No	9 (10%)	12 (100%)	21
	Col. total	88	12	100

Under opt-out, 88% of drivers register to be donors but only 79% of organs are recovered

Mandated choice

As mentioned in the previous blog entry, there is another option besides opt-in and opt-out called mandated choice. Under mandated choice, the state wants to eliminate the last categories 3a, 3b, and 4 (driver choice undeclared or driver undecided) that create ambiguity. Thus, the law requires the DMV clerk to ask every driver to declare a choice. (It’s not clear what happens if the driver refuses to make a choice or if the clerk forgets to ask or forgets to record the choice.) Several states have tried it, but have given up and returned to opt-in. Currently, only California is experimenting with it, see Jun 2010 blog post.

Texas, which had about a 15% registration rate with opt-in, increased it to about 20% with mandated choice. Unfortunately, I can’t find any data to show if overall organ recovery rate rose or fell after this change. However, the state has abandoned mandated choice, so my guess is the OPOs in that state either saw a drop in donation rates or feared one would occur and lobbied for the return to opt-in.

How opt-out and mandated choice may reduce donation rates

Why has mandated choice failed, and why could opt-out cause donation rates to fall? I think a lot of it may be because of people’s fear of death. Signing up to be an organ donor while applying for a driver’s license is an admission by the registrant that she may die in an accident and needs to make a decision about the disposition of her organs in the event that happens.

Under the current opt-in process, those who are not afraid of death opt-in. Those who are afraid don’t state their preference. For those who don’t opt-in, the decision to donate is still available later to the family. Under opt-out, people who are willing to donate (or let their family decide) but are not willing to admit they may die will opt-out. This is a firm decision, precluding the family from making the donation later.

In the next blog post we will explore ways to make opt-out compatible with individual choice and consent.

 

Thoughts about opt-out donor registration

Nearly every state in the U.S. maintains a registry of people willing to become deceased organ donors. The intent of an individual to be a donor is stored as a Boolean value (meaning only yes or no responses are allowed) within the driver’s license database. Nearly all states use what is called an opt-in registration process. That is, the states start with the assumption that drivers do not want to participate in the registry (default=no) and require them to declare their desire (called explicit consent) to be a member of the registry either in-person, via a website, or in writing.

One of the frequent proposals to increase the number of deceased organ donors is to switch the registration of donors from an opt-in system to an opt-out system. In an opt-out system, all drivers are presumed to want to participate (default=yes) and people who do not wish to participate must state their desire not to be listed.

Let’s look at the logical and ethical issues this change would present.

Not just a framing problem

Several well-known behavioral economists have stated that switching from opt-in to opt-out is simply a framing problem. For instance, see chapter 11 of Richard Thaler and Cass Sunstein’s book Nudge and a TED 2008 talk by Dan Ariely using data from papers by his colleagues Eric Johnson et al., in Transpl. Dec 2004 and Science Nov 2003 (subscription required).

The basic argument is that deciding whether to donate organs upon death is cognitively complex and emotionally difficult. When asked to choose between difficult options, most people will just take the default option. In the case of an opt-in donor registration, this means they will not be on the organ donor registry. By switching to an opt-out process, the default becomes being a donor. Thus, any person who refuses to make an active decision will automatically become a registered organ donor (this is called presumed consent). This will increase the number of people in the donor registry without causing undue hardship since drivers can easily state a preference when obtaining a driver’s license.

However, these authors overlook two important practical factors. First, switching from opt-in to opt-out doesn’t just reframe the decision the driver must make between two options. It will actually recategorize some drivers.

Second, it changes the certainty of the decision of those included in the organ registry, which affects the interaction between the organ recovery coordinators at the organ procurement organization (OPO) and the family member of a deceased patient.

There are more than two states for drivers regarding their decision to donate

Note that the status of a driver’s intent to be an organ donor is not just a simple two-state Boolean value (yes, no). There are actually at least three separate states related to the intension to be an organ donor. First, upon the driver’s death, if no other family members would be affected, would she like to be an organ donor (yes, no, undecided). Second, has she expressed her decision to the DMV and have it recorded (yes, no). Finally, would she like her family to be able to override her decision (yes, no, undecided). The table below shows the various combinations of these variables.

Category	Driver would like to be organ donor	Driver tells DMV of decision	Driver would permit family to override decision	Comment
1a	Yes	Yes	No	Strong desire
1b	Yes	Yes	Yes or Undecided	Weak desire
2a	No	Yes	Yes or Undecided	Weak reject
2b	No	Yes	No	Strong reject
3a	Yes	No	Yes, No, or Undecided	Unrecorded desire
3b	No	No	Yes, No, or Undecided	Unrecorded reject
4	Undecided	Yes or No	Yes*	Undecided

*No or Undecided options make no sense in this context

Opt-in incorrectly excludes some drivers from the donor registry

Now let’s sort these people into two groups, one that we will call the organ donor registry and the other not on the registry.

Under the opt-in process, only drivers in categories 1a and 1b are listed on the organ registry. These drivers have given explicit consent to being on the registry. Drivers in categories 2a, 2b, 3a, 3b, and 4 are excluded from the registry. Thus, we can be quite certain that everyone on the registry wants to be a donor. (There is always a small possibility that the driver accidentally selected the wrong box, changed their mind between the time they obtained their driver’s license and the time of death, or a computer error occurred.)

In most states the drivers not on the organ registry are treated as if they have not decided (i.e., as if they were in the fourth category). When drivers not on the registry die under conditions where the organs can be recovered, the families are asked to decide on behalf of the deceased.

Under an opt-in process, drivers in category 2a are miscategorized. They don’t want to be donors and didn’t want their family to override that decision, but the family is still allowed to decide. The drivers in categories 3a and 3b are miscategorized as well. The ones who don’t want to be donors (3b) are also forced to allow their families to decide. The ones who want to be donors (3a) are now left to let their families decide.

Opt-out incorrectly includes some drivers in the donor registry

Under an opt-out process, drivers in categories 1a, 1b, 3a, 3b, and 4 are grouped together and placed on the organ registry. If the donor registry is binding and the family is not allowed to stop the donation, then the process is called presumed consent. (Note that many authors use opt-out and presumed consent interchangeably. However, they are distinct ideas. Opt-in is a mechanical process of deciding which driver names are added to the registry. Presumed consent is a legal condition that avoids the need to ask the family for permission to recover the organs.)

Drivers in category 3a who wanted to be registered are now correctly placed on the registry. But any drivers in category 3b who don’t want to be on the registry are now assumed to want to be donors, a completely incorrect categorization. Similarly, all drivers in the fourth category who were undecided are now members of the definite donor group and the family no longer has a say.

Only drivers in category 2a and 2b are excluded from the registry. We can be quite certain these people do not want to be donors. But some (category 2a) were willing to let the family decide. Now they are combined with the group of drivers who explicitly do not want to donate.

The distribution of categories into the registry under the opt-in and opt-out process and how they are treated are shown in the table below.

	Categories added to donor registry	Categories not added to donor registry	Implications
Opt-in process	1a, 1b both treated as if in category 1a (explicit consent)	2a, 2b, 3a, 3b, 4 all treated as if in category 4 (family choice)	Drivers in registry are nearly certain to want to be donors. Actual desire of drivers not on registry is ambiguous
Opt-out process	1a, 1b, 3a, 3b,4 all treated as if in category 1a (presumed consent) or 1b (family choice)	2a, 2b both treated as if in category 2b (explicit reject)	Drivers not in registry are nearly certain to not want to be donors. Actual desire of drivers on registry is ambiguous

 

Ethical implications of misclassification

If there are no drivers in categories 3a, 3b, and 4, then switching from opt-in to opt-out will have no impact on the size of the donor registry. However, if there are any drivers in these categories, then some will be incorrectly categorized regardless of whether opt-in or opt-out is used. This miscategorization will lead to some ethical problems.

Under opt-in, there may exist cases where the drivers has made a decision to donate (category 3a) or not (categories 2a or 3b) but family members overrules it. These errors are hard to avoid because they are caused by the lack of agreement between the drivers and other family members.

However, under opt-out combined with presumed consent, there may exist cases where neither the driver (category 3b) nor the family want to donate, but cannot stop it. Similarly, the driver may want to let the family choose whether to donate (category 4) and the family does not want to donate but cannot stop it.

It appears that from an ethical perspective, opt-in is less likely to create a situation where the respect for individual’s right to make decisions about how the body should be treated is denied. For further discussion of the ethical issues see  J. Med. Ethics Jun 2011, and J. Med. Ethics Oct 2011 (subscription required).

Next we will look at the impact switching from opt-in to opt-out will have on the interaction between the organ recovery coordinator and the family. See Part 2 here.

[Update: This blog post was significantly modified to clarify the “decision framing” issue.]

 

Hope Strength Love’s smart plan

A huge front-page story in today’s Denver Post features Love Hope Strength Foundation’s effort to sign up people to the bone marrow registry. The group hands out flyers as people drive into the parking lot of rock concerts (and other events) and then takes cheek swabs (later used to create an HLA profile) as they walk into the venue.

Rob Rushing of the Love Hope Strength Foundation at Red Rocks Amphitheater. Photo by Hyoung Chang for The Denver Post

I’m fascinated by the group’s motto of “Saving lives one concert at a time.” Here is an organization that has found a way to attract the young people who make the best bone marrow donors. It makes itself highly visible at rock concerts.

By doing so, Love Hope Strength generates publicity for its cause. Recruiting people at the event itself is very useful. But by doing it in a public venue it makes joining the registry a socially acceptable activity. And it does more. It generates interest in donating money and in becoming a volunteer. Finally, it educates the public about the important role adult stem cells play in fighting cancer and other diseases. Overall, this is a wonderful model to engage young adults in a healthcare related activity.

 

Stoopid Internet tricks Part 2–Search engines

Does using Internet Explorer make you stupid? I think not, but sometimes it can trick you. (See part 1 of this story here.)

I use a variety of browsers and operating systems, but my favorite is Internet Explorer 9 running on Windows 7. I like the feature that combines the address bar with the search box into a single text edit field. It allows me to just type a company name in the search box and the browser will resolve it into a domain name for me. (Of course, not everyone likes this design.)

Anyway, a few minutes ago I was using Safari on my Mac and typed “Ikea” in the address bar. Naturally, what I really wanted was “www.ikea.com”. Safari doesn’t automatically send invalid URLs to the search engine like IE9 does. I have Comcast broadband at home. Comcast detects and captures any invalid URLs and displays its own custom DNS error page, a practice called DNS hijacking. A portion of the page is shown below.

Custom DNS error page. Image from Comcast

Notice that the first item is a sponsored link that has the title “IKEA.com – Official Site” and has the URL www.ikea.com that I wanted highlighted in green. Naturally, I clicked on it. After a few redirections, this is what I see:

It sort of looks like an Ikea home page. Image from rewardsclub.com

This looks like it could be the official IKEA site, but it isn’t. The domain name displayed in the address bar is not for ikea.com but for rewardsclub.com, one of those credit card scam companies that is basically a phishing site. The top part of the page is designed to look like it is complete. But you will notice that the scroll bar indicates there is more content below the fold. If you are willing to scroll down, you’ll see the following disclaimer:

IKEA is a registered trademark of Inter IKEA Systems B.V. BigBrandRewards.com is not affiliated with IKEA®. All IKEA® trademarks are the property of IKEA® and BigBrandRewards.com does not, in any way, claim to represent or own any of the IKEA® trademarks or rights. IKEA® does not own, endorse, or promote BigBrandRewards.com or this promotion.

This Gift Program is not endorsed, sponsored by or affiliated with the manufacturers and retailers of the gift items listed above in anyway. All trademarks, service marks and logos are property of their respective owners.

Well, I guess that disclaimer may protect them from lawsuits by Ikea (trademark infringement) or from disgruntled customers and state attorneys general (fraud and deceptive trade practices). But I doubt it.

This sucks. Only a credulous rube would actually purchase a prepaid credit card. But everyone is forced to waste time figuring out that this is not the Ikea website and either manually typing in the correct URL to get there or go back to Comcast’s search page and click on a different link.

However, I don’t blame Comcast for this travesty, at least not directly. I believe the search results on the DNS server not found error page are provided by Yahoo (which uses Microsoft Bing as its search engine) and that Yahoo and Microsoft run the keyword auctions that populate the sponsored links. Thus, it is up to them to ensure that the green text in the sponsored link ads matches to the domain that the user will be redirected to.

 

Stoopid Internet tricks Part 1–Plausible lies

Does using Internet Explorer make you stupid? I think not, but maybe smart people are willing to believe any lies about Internet users.

Recently, a Canadian firm called ApTiquant issued a press release saying that it conducted an online IQ test using over 100,000 people. It recorded the results and the web browser used by the participants. They produced a downloadable white paper that contains a couple impressive looking graphs that show people using Internet Explorer 6 had lower than average IQ.

Users of Internet Explorer 6 are getting stoopid. Chart from ApTiquant

The story was picked up by many media outlets including CNN, Forbes, and others. Some even included an explanation of why this data would be expected. For instance, it could be that Internet Explorer 6 was released in 2001 and so anyone who was still using it would be more likely to be poor and have lower educational attainment.

Another is that many users of Internet Explorer just use the product because it was installed by default when purchasing a computer. Users of other browsers often need to download it, install it, and use it. Only more determined people, who presumably are also of higher intelligence will do this. As they exit the population of Internet Explorer users, they leave behind a pool of less intelligent users.

Finally, certain companies and the government agencies require all employees to use a specific browser, like IE6, to maintain compatibility with line of business websites. This implies workers at organizations that lack IT resources to upgrade internal tools bring down the average.

But alternative views did appear. The BBC quoted Professor David Spiegelhalter of Cambridge University’s Statistical Laboratory, who said: “I believe these figures are implausibly low – and an insult to IE users.”

Eventually, it was revealed that the whole story was a hoax. There is no company called ApTiquant. No IQ tests were performed and the white paper was a fabrication. The ruse was  perpetrated by an online shopping comparison website called AtCheap.com to raise awareness that Internet Explorer 6 was not compliant to web standards and rendered many web pages incorrectly. The company encourages users of IE6 to switch to a more modern browser in order to view more websites in a manner that their creators intended.

(See part 2 of this story here.)

 

The Endangered Alphabets Project

The historian Edward Tenner has posted an article on The Atlantic Aug 2010 that describes the work of Tim Brookes on the Endangered Alphabets Project. As some of you know, I am a lover of calligraphy and  good typography. So I just have to put in a plug for this project. You can contribute to the Endangered Alphabets Project by going to Kickstarter and making a pledge.

There are literally thousands of rare languages that are spoken by fewer than 1,000 people each. Many more are already lost and nobody remembers them. Many of these languages also had their own written alphabets (or more accurately, scripts).

Mr. Brookes is a writer and a calligrapher/sculptor who is attempting to preserve many of these rare scripts by carving text into blocks of curly maple, a beautiful tree wood that grows near his home in Vermont. An example of Manchu is shown below. Manchu is one of the written languages derived from the Mongolian script. It is written vertically from top to bottom with the columns running left to right. Characters in a word are joined in a  cursive style with spaces between the words. Diacritical marks are used to clarify pronunciation. (Think of it as Arabic rotated counterclockwise 90 degrees.)

Manchu carved in curly maple. Image from the Endangered Alphabets Project

The Endangered Alphabets Project consists of 14 carvings. Each carving is in a different rare language (Inuktitut, Baybayin, Manchu, Bugis, Bassa Vah, Cherokee, Samaritan, Mandaic, Syriac, Khmer, Pahauh Hmong, Balinese, Tifinagh and Nombut) but all composed of the same text, Article One of the Universal Declaration of Human Rights, of the United Nations.

As Mr. Brookes notes, “The irony, of course, is that many of these forms of writing are endangered precisely because human beings do not always act towards one another in that spirit.”

Although these languages may disappear in their spoken form, their written forms may continue to live on. Each of these written scripts are described within the Unicode system of computer text encoding.

[Update: Added hyperlinks to language names.]