Don’t let mental errors cloud your thinking. Image by Jan Buchczik for The Atlantic

by George Taniwaki

Arthur Brooks is a conservative social scientist. He is on the faculty of Harvard Business School and was formerly president of the American Enterprise Institute. Since 2019, he has been writing a series of articles in The Atlantic, now called “How to Build a Life.” With the onset of the Covid-19 pandemic, the articles have included advice on how to live a a happier and better life by understanding our life circumstances.

In his Apr 23, 2020 article entitled “Two Errors Our Minds Make When Trying to Grasp the Pandemic”, he makes the case that we would be happier if we understood the difference between two experiences that make us unhappy and two conditions that make us nervous. It is a very thought provoking article and I highly recommend it.

Regret and disappointment

Regret and disappointment both lead to unhappiness. They seem similar but are not. We should only feel regret for bad decisions that we have made. Then we should work hard to develop strategies to do better next time. But we should not feel disappointment.

In contrast, we should only feel disappointment when we are in situations where we had no control, like the Covid-19 pandemic. And once we recognize we have no control, we should endeavor to stop our disappointment and get on with other thoughts that will make us happy. As Brooks says, “rumination on what you would be doing if it weren’t for the coronavirus is a destructive waste of your time.”

Risk and uncertainty

Most people dislike risk and uncertainty. Again, these conditions seem similar but are not. As Secretary of Defense Donald Rumsfeld famously stated, “There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know.”

Risks can be thought of as the known unknowns. These are outcomes that we cannot accurately predict, but understand well enough that we can forecast them using stochastic models. We can also mitigate and manage risks by working hard using the appropriate strategies and interventions.

Uncertainty are the unknown unknowns. How many people will die from Covid-19? Is it safe to open schools in the fall? Will I or a family member get the disease? We don’t know and can’t predict these with the information currently available. That is, we as laypersons cannot convert uncertainty into risk. Thus, we should not spend a lot of time worrying about these questions. Doing so will exhaust us and make us unhappy without leading us to a better prediction.

Acknowledge, distinguish, resolve

Mr. Brooks has a three step solution to overcoming these two cognitive errors. He calls his solution “acknowledge, distinguish, resolve.” As he writes, “Disappointment and uncertainty are inevitable, but we don’t have to turn them into suffering.”

White-Privilege   white-privilege2

Examples of images when searching for “white privilege”

by George Taniwaki

Making people aware of their unconscious bias or stereotypes is an important part of helping them become better critical thinkers.  Many of these prejudices are based on visual cues such as race, gender, and class. They are learned very early in life and are difficult to overcome.

As described by the Kirwan Institute for the Study of Race and Ethnicity, implicit biases are pervasive. They “encompass both favorable and unfavorable assessments, are activated involuntarily and without an individual’s awareness or intentional control.”

Unlike known biases “that individuals may choose to conceal for the purposes of social and/or political correctness, implicit biases are not accessible through introspection.”

To learn more about your own implicit biases regarding race, create an account and take this test.

Teaching about white privilege may be counterproductive

Recently, the term white privilege has come into vogue. The phrase is often used to describe how Americans of European ancestry, even if they do not actively discriminate, enjoy passive advantages over minorities.

However, recent research shows that teaching people about white privilege may have the opposite effect as intended. That’s the conclusion of a paper by Erin Cooley, et al. published in J. Exper. Psy.: General, Apr 2019 (subscription required).

In a May 2019 article on Vice, professor Cooley does a very good job of describing the results of this research, including her own personal connection through her fiancé, a white man who grew up poor. It’s a great article and I strongly recommend reading it.

She says, “Given a career focused on race, I was fixated on the privileges of being a white man. I couldn’t stop myself from mentioning that white male poverty wasn’t exactly the worst injustice out there.

“I had evidence on my side. Poor white men can hide being poor more than Black people can hide being Black. And there are plenty of systemic barriers my fiancé was unlikely to face as he made his way up to being the successful professor he is today. Still, the fact that he was a poor white man had escaped my empathy radar. I wondered whether this might be connected to my liberal worldview.”

How the study was conducted

There were two studies conducted with a total of 1,189 participants. The sample was divided into two groups, social conservatives and social liberals. Within each group, some were provided a lesson on white privilege and some were not.

All participants then read a story about a person and the hardship they underwent. For some participants, the subject of the story was a poor white man. For the others, it was a poor black man. Afterwards, all participants answered a questionnaire to measure the level of sympathy they had for the subject of the story.


Overall, there are a total of eight subsamples. The chart below shows the data, with lines indicating the change before and after receiving the lesson on white privilege (actually with and without the lesson, since they are different samples). Social conservatives have solid red lines and social liberals in dashed blue lines. Story with a poor black man have a solid triangle marker and story with a poor white man have an open square marker.


Figure 1. Effect of reading a lesson on white privilege on sympathy score on story of a poor person

In general, social conservatives reported lower sympathy scores than social liberals. Without reading the lesson on white privilege, social conservatives reported higher sympathy scores when the character was black. Reading a lesson on white privilege causes sympathy to rise for both black and white characters in the story of the poor man. Further, the difference in score is reduced.

Without a lesson, social liberals reported similar sympathy scores for both white and black characters. However , reading the lesson on white privilege causes the sympathy score to fall for the poor white man and rise for the black man, causing the difference to increase.

Thus, providing lessons on white privilege in an effort to help reduce implicit bias against poor blacks may unintentionally harm poor whites.

As prof. Cooley states in Vice, “My prior insensitivity to the experiences of poor white people might be just the type of attitude that contributes to an increasingly polarized US political climate—a climate that ultimately causes further harm to Black people too.”

Since the sympathy scores in this study are self-reported, they need to be treated with skepticism. Further, the reading and sympathy survey were administered immediately after the lesson on white privilege was given. It would be interesting to see if the effects are transient and how long lasting they are.

[Update1: I fixed some typos and a broken hyperlink.]

[Update2: Moved description of my chart to new May 2019 blog post.]


Everyone, face left. Screenshot from The Atlantic

by George Taniwaki

Which side of your face do you show when someone takes a photograph of you? Most people naturally turn right which shows the left side of their face.

The Atlantic Feb 2014 features a video by the science writer Sam Kean that discusses this observation and a theory about why it occurs. It may add insight into the right-brain, left-brain debate. Or it could be completely unrelated.

I usually turn left, showing the right side of my face, though that isn’t a natural behavior for me. I learned it as a child. My natural inclination is to face directly at the camera. But in grade school, on picture day, a photographer told me not to look straight at the camera and to turn my head. When I do that, I naturally want to look left, which shows the right side of my head. This is true even though I part my hair on the left.

When asked to turn my head to the right and show the left side of my face, I feel like I am showing off.

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.


Driver would like to be organ donor
Driver tells DMV of decision
Driver would permit family to override decision


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


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.]

Yesterday’s New York Times has an incredibly detailed and sad story about the final hours onboard the Deepwater Horizon drilling vessel.


Deepwater Horizon drilling vessel prior to capsizing and sinking. Photo from NY Times

I wrote about the disaster and the role poor risk management played in it in two Jun 2010 blog posts.

In a Dec 2009 blog post, I wrote that too many patients with end-stage renal disease (ESRD) are waiting for a deceased donor kidney. They would have a much shorter wait and experience better outcomes if they could find a live kidney donor. I am currently working with Harvey Mysel and the Living Kidney Donors Network to set up a program in Seattle to provide training to patients to give them the tools and the confidence to find a donor.

Part of my effort includes learning as much as I can about working with patients. I have plenty of experience in public speaking, having been a market research consultant. But in that case the audience consists of highly driven business executives. I have some experience working with disadvantaged populations, having been a volunteer tutor in an adult literacy program. But I do not have any experience working with medical patients. How does one instruct and motivate kidney patients who are quite ill? Even more concerning to me, can I effectively work with patients who have behavioral or emotional problems that make me uncomfortable? What about physical appearance? The leading causes of kidney failure are diabetes mellitus and hypertension, both of which are highly correlated with obesity. Will I consciously or even unconsciously blame overweight patients for their disease? Hopefully, just knowing I have a potential bias may help prevent me from allowing it to affect my ability to help.

While pondering this, my wife forwarded an article entitled “How clinicians make (or avoid) moral judgments of patients: implications of the evidence for relationships and research” that appeared in Philosophy, Ethics, and Humanities in Medicine Jul 2010. It is a review of 141 articles on how clinicians form moral judgments regarding patients and how those evaluations affect empathy, level of care, and the clinician’s own well-being. Just reading the list of references to the article is an eye opener. Below are some selected quotes from the article.

“The paucity of attention to moral judgment, despite its significance for patient-centered care, communication, empathy, professionalism, health care education, stereotyping, and outcome disparities, represents a blind spot that merits explanation and repair… Clinicians, educators, and researchers would do well to recognize both the legitimate and illegitimate moral appraisals that are apt to occur in health care settings.”

“[T]he treatment of medically unexplained symptoms… varied by patient ethnicity, physician specialty, the spatial layout of the clinic, and the path sequence of patient contact with physicians and ancillary personnel.”

“[N]urses judged dying patients by their perceived social loss, often giving ‘more than routine care’ to higher status patients and ‘less than routine care’ to the unworthy. People dying from a Friday night knife fight, or the adolescent on the verge of death who has killed others in a wild car drive, have their own social loss reinforced by an ‘it’s their own fault’ rationale.”

“The patients and physicians were able to gauge whether the other liked them, and that perception predicted whether they themselves liked the other. Physicians liked their healthier patients more than their sick patients, and healthier patients liked their physicians more. Physician liking predicted patient satisfaction a year later.”

“Poor patients belong to outgroups of particular interest in healthcare. Public hospitals serving these groups comprise only 2% of acute care hospitals in the United States but train 21% of doctors and 36% of allied health professionals. Primary care physicians serving poor communities are often troubled by what they perceive as their patients’ inadequate motivation and dysfunctional behavioral characteristics.”

“One of the factors that may prevent clinicians from triggering moral appraisals is interest, often equated with curiosity… Good teachers have stressed the value of curiosity for clinical care… ‘One of the essential qualities of the clinician is interest in humanity, for the secret of the care of the patient is in caring for the patient.’”

“Once a stimulus–or perhaps patient, for our purposes–appears beyond one’s comprehension and ability to manage, interest wanes. These appraisals mediate individual personality differences in curiosity and the experience of interest… [W]e can use interest to self-regulate our motivation. When intrinsic motivation lags, we can activate strategies to engage our interest and thereby remain motivated for the task.”

Most people are bad at thinking about low probability events and their eyes can glaze over as they think about very small or very large numbers. Further, how the data is framed has a big impact on how your react to them.

To take a personal example, I’m about to go in for surgery to donate a kidney next week. [Update: My surgery has been postponed, but that doesn’t affect this analysis.] The chances of me dying are very small, only about 0.02%. I guess that seems very safe. Now let’s frame it differently. There are about 6,500 live kidney transplants a year, which means on average one or two donors die each year. Now my surgery seems a lot more dangerous. By changing from a percentage to an actual count, the number seems more personal. I can image that the person who dies is me.

Here’s another example. According to the United Network for Organ Sharing (UNOS), as of June 24, there are 85,512 kidney patients in the U.S. waiting for a kidney transplant. It seems like an impossible task to find enough donors to help them all. Assuming no other additions or removals in the next week (which isn’t quite true), that number will drop to 85,511 after I complete my donation. It seems my contribution is insignificant. But I can frame the problem in another way. The Univ. Washington Medical Center, where my surgery will take place, has 416 people on the waiting list. After my donation, it will be 415. This makes the impact of my one donation seems a lot bigger.

To make the task more general, there are 249 transplant centers in the U.S. that perform live donor transplants, meaning an average of 341 patients per hospital. Finding 341 more people (per year) to donate seems like a solvable problem. This isn’t an unachievable task. I just need to find a group of live donor champions at each hospital, probably previous nondirected donors. Then I need to convince them that they just have to help each of the 341 patients (on average) at each hospital find a donor and the waiting list will go away. To make the task seem even smaller, I can state the problem is to find one donor for each patient on the waiting list. Now it really seems easy. Small integers have a concrete aspect to them. Very large numbers or very small fractions do not because you just can’t picture them in your mind.

Keeping this smaller goal in my head should help keep me motivated as I prepare my outreach efforts to help kidney patients find live donors. (Yes, I’m fooling myself, which violates my Real Numeracy credo. So what?)

Choice A Choice B
Which option seems riskier? A 0.02% chance of dying from surgery (outcome per patient) 1 to 2 deaths per year (outcome per population)
Which task seems harder? Find 85,510 donors for the kidney patients on UNOS waiting list (count per population) Find 1 donor for each kidney patient on the UNOS waiting list (count per patient)