Memento mori, Part I

Julia Reiskind, a friend of ours, died a few days ago.

Memento Mori, for death is coming for all of us.

Bobbi told someone at Goucher College, their alma mater, that Julie had died. Today she (Bobbi, not Julie) got a call from someone from Goucher who wanted to know some details—like the date that Julia died. Perhaps this woman didn’t realize she could get answers from the internet by typing something like “Julia Reiskind Obituary” in her browser like I ended up doing. I found Julie’s obituary.

I decided to start work on my obituary because I’m not dead yet, and I can.

So here’s the first installment, following Julie’s as a template.

There’s more to come.

WOLF, MICHAEL BEAU

Michael Beau Wolf, 77+, beloved husband, father, grandfather, and friend, isn’t dead yet, but one of these days he will have—

passed on! Become no more! Ceased to be! Expired and gone to meet his maker! Become a stiff! Bereft of life! He will rest in peace! He’ll be pushing up the daisies! His metabolic processes will have become history! He will be off the twig! Kicked the bucket! Shuffled off his mortal coil, run down the curtain and joined the fucking choir invisible!! HE WILL HAVE BECOME AN EX-WOLF.

Glad we’ve got that out of the way.

Michael Beau Wolf was born at (or possibly in) Unity Hospital in Brooklyn, NY on December 30, 1942, just in time to earn a full year’s dependent child income tax deduction for his mother, Edith Judith “Sister” (Gaines) Wolf, and his reputed father, Milton Arthur “Menasha Alta” Wolf.

(Fun fact: His brother, Mark Jay Wolf, was born on January 1, 1945, and missed the window. He eventually overcame this poor start.)

According to family legend, here’s how Michael Beau got his middle name (Beau, for those not paying attention): His mother thought he was beautiful and wanted to name him Michael Beautiful Wolf. His father thought better of it and Beau was the compromise. “Beau” caused Michael embarrassment in grade school, but standards have changed, and he grew to like it, and now the name is worn (proudly, I hope) by his grandson, Lucas Beau Greenland.

Anyway, that’s the story, true or not.

Michael has not grown up and likely won’t. But he started to grow up on Crown Street in Flatbush, Brooklyn, continued on E. 55th Street in Brooklyn, then on Madison Avenue, in Baldwin, NY, graduating from Baldwin Jr/Sr High School in 1960.

Two of the three colleges he applied to rejected him. Fortunately, MIT took him, and he has a flashbulb memory of sitting in a plane flying up to Boston for rush week and realizing that he did not understand how planes could fly. He resolved to do something about that, and if being able to describe the Bernoulli effect constitutes doing something, he did.

He was pledged by Phi Kappa Sigma fraternity and spent at least his first several years at 530 Beacon Street, where he learned to drink and chase women, in that order because he was initially too shy to chase women when not drunk.

After he chased enough, he was no longer chaste, and stopped getting drunk.

He completed requirements for an SB in Mathematics from the Massachusetts Institute of Technology in 1964.

Fun fact; MIT doesn’t give a “Bachelor of Arts” (BA) degree, but instead provides a “Bachelor of Science.” MIT calls it an “SB” rather than a “BS” degree like many other schools because MIT is a no BS school, I guess.

Michael completed his degree work in 7 semesters rather than eight. By the end of his junior year he was sick of school and went off and joined the circus. Literally. He lasted about two days. He left the circus, went back to school, and completed his last two semesters in one., He did it by cramming for three days to take two exams that gave him the credits he needed to graduated. One of the courses was called “Differential Geometry.” Michael has no fucking idea what the other was called, but thinks it was something about topology. Michael has no fucking idea what differential geometry is for, other than getting out of MIT with a degree.

Fun fact: For years he thought he was very smart for being able to pass two MIT courses in three days. Now he thinks he’s more like an idiot for wasting the opportunity.

So much dumbness, masquerading as smartness.

After completing his course requirements, he fled to the University of Hawaii to avoid being drafted and sent to VietNam. He also went to hang out with his brother, and to take the courses he needed to apply to medical school to become a psychiatrist, which career-wise was as far away from MIT as he could get.

He dropped out of the University of Hawaii in his second semester to avoid failing out.

He’d also decided he’d be a shitty psychiatrist.

Other stuff happened, but this has gone on long enough. So I’ll wrap it up in the more-or-less traditional way:

Michael better not be survived by his wife Bobbi, who has made him promise that he’ll outlive her, and who—if he dies first—has vowed to kill him.

He’d better be survived by three fully-grown zygotes, (in fertilization order order), Dana Elizabeth Wolf, Mira Dawn Greenland, and Alyssa Ann Eva Wilk, three tenured sons-by-marriage (in marriage order) John Sherwood Greenland, Jr, Konrad Rzeszutek Wilk, and Daniel Craig (Weidman) Wolf, and seven grandchildren: Kaya Ann Eva Greenland (Z2.1), Lucas Beau Greenland (Z2.2), Tasman Martin Greenland (Z2.3), Michael Konrad Wilk (Z3.1), Sylvia Eva Wilk (Z3.2), Siena Grace Wolf (Z1.1), Kyra Joy Wolf (Z1.2) and everyone else who hasn’t died first.

That last line was written by Mark and Michael for their father’s obituary and Michael wants it used in his.

Like lots of people, Michael’s life was part inspiration and part cautionary tale.

Looking back at his life, he’s pleased with how it turned out, despite doing so many stupid things when he was younger.

He hopes to continue to learn and be less stupid as he continues to live.

Michael isn’t dead yet but knows he’s going to being dead someday, all too soon. He’s not afraid of dying, but he’d rather not. If his body and mind would just stop deteriorating, he’d like to live forever to “find out how it all comes out” as Bill Harmon, father of his friend Dawn Hull, once told him.

And maybe to become a better person.

And maybe, as they say in “Person of Interest.”

if you mean something to someone… if you help someone… or loved someone… if even a single person remembers you… then maybe you never really die at all.

So maybe.

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​

Good conversations

What makes a good conversation?

Most conversations—including many that I’ve participated in—are unproductive.

I’m writing this to clarify what I mean by that statement—more for myself than for others.

I’d like my understanding to lead me to change my behavior so that my conversations are more productive.

If you know me, I’d like you to help me change my habits.

I want to be more conscious of the degree to which I am helping or not helping a conversation be productive.

And I’d appreciate ideas on how to do better.

Unproductive conversations

Definitions first. Operational,, of course.

By an “unproductive conversation,” I mean one that yields no new knowledge for any participant.

If a participant can truthfully say: “I learned something useful” and say what it was, then, by my definition, the conversation was productive for them. If not, unproductive.

If a conversation is not productive for any participant (or audience members), then it’s an unproductive conversation.

One goal I have: to help make conversations more productive.

Worse than unproductive

A conversation—or part of one—can be worse than unproductive. It can be that way for a participant, or everyone

If a part of a conversation that’s worse than unproductive, I’ll call it a “destructive element.”

A conversation can be productive or not and yet have destructive elements.

There’s a destructive element in a conversation if, as a result, someone feels they understand someone less; think less of another person’s views; think someone else’s views make less sense; like someone else less; want to avoid further interaction.

How can I make conversations productive?

In my definition, a conversation is productive to the degree that someone is learning something.

I can help make a conversation more productive by learning something.

I can raise the odds of learning something by asking a question whose answer I’d like to learn, and which I don’t already know.

I might also help by asking someone a question that no one has asked them before.

I could ask them a question that invites them to become aware of the current conversation—and how it might be better.

Questions whose answers interest me

Coming up with such questions should be easy, but it’s not. It’s a skill. Not one I’ve practiced. Or even thought about.

So now it’s time to think. Why is it hard? And what might make it easy?

One reason it’s hard: I’m a besserwesser. I believe I know more than most people about things likely to interest me. So why ask? I already have a better answer than the one they are likely to give. Indeed, asking is likely to cause me to think less of them. So asking is worse than unproductive.

But everyone knows things that I know little or nothing about, and I’m interested in almost everything. So with a bit of thought and creativity, I might be able to think of some questions.

Another: people have often already volunteered their answers to many questions I might ask. Or they might have provided me enough information about their worldview that I believe I already know how they will answer. Confirming my belief that I knew what they’d answer does not constitute knowledge. And neither does hearing what I already know.

But if I’ve got someone identified with a particular school of thought, I could ask them questions whose answers I might not predict. I might ask them, “When did you learn that?” or “Have you always believed that?” Wading into more dangerous territory: “Is there anything that could convince you that wasn’t true?” Or less provocatively: “Imagine something happened that led you to conclude that wasn’t true. What would that be?” Or perhaps: “What’s the best argument you’ve heard against that view?” Maybe Byron Katie’s Four Questions would be helpful.

There are lots of questions that I might ask, but the answers don’t interest me. But that’s because I’m too focused on what doesn’t interest me, not what does.

Another: there are questions I might ask, whose answers might interest me, but I imagine people might be offended if I asked. This deserves more thought and exploration.

Not done yet

A conversation can have productive elements, non-productive elements, and destructive elements.

My goal is to understand how I can make the conversations that I can influence as productive as I can and to minimize their destructive elements where I can.

Unproductive conversations waste time. They waste our lives. They have an enormous opportunity cost. Destructive conversations, of course, are worse.

I want to make the world better.

Helping make conversations better is one way to do that.

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​

A thought is harmless unless we believe it

A thought is harmless unless we believe it. It’s not our thoughts, but our attachment to our thoughts, that causes suffering. Attaching to a thought means believing that it’s true, without inquiring. A belief is a thought that we’ve been attaching to, often for years.
-Byron Katie

I first discovered Byron Katie’s “Work” fifteen or twenty years ago. I found it very useful. Then I forgot what I’d learned. Then I discovered it again and forgot it much more quickly. And again and even more rapidly. Goes to show what you can do when you practice.

This time I’m making a backup. That’s what my blog posts are for me, I realized the other day. Every post is a backup of the states of mind that I was in when I wrote them. I can read them later and re-enter that state of mind—or a reasonable approximation—easily. Or if the post is about exiting a state of mind, then I can exit again—quickly.

Here’s the story of my most recent “rediscovery” of Byron Katie. I could start it anywhere in the last 13.75 billion years, I suppose. But I’ll start it on June 26th when I restarted writing my morning pages.

I decided I was going to get back into the habit of writing my morning pages and found it surprisingly difficult. But I kept at it, on and off, all day. After hours of starts, diversions, restarts, abandoned directions, returns, new starts, I found a direction in which I could keep going. I concluded my 750 words that day, headed in that direction. The next day I started by rewriting a summary of my ending thoughts and went on from there.

The idea from June 26th, as I express it now, is this:

“I want to get up every morning with a clear goal (or direction) a plan for moving in that direction, enough energy to begin to make progress in that direction.

“If I have a goal or direction, but no plan, then my goal is to develop a plan, providing I have enough energy.

“If I have no particular goal or direction, then my goal is to choose one, providing I have enough energy.

The next day I considered: what do I do if I don’t have enough energy?

Many starts, redirections, halts, and restarts later, I decided this: it is never true that I don’t have enough energy. The idea “I don’t have enough energy” is a belief. And not a harmless one. And I remembered Byron Katie: beliefs are not axioms or natural laws. They are just beliefs. And they are subject to revision.

The next day I continued.

I considered what to do about that belief—and others like it. Byron Katie has a process for dealing with beliefs that don’t serve us well. It’s outlined in this little book. Find a troublesome belief. Ask yourself these four questions:

1. Is it true? (Yes or no. If no, move to question 3.)
2. Can you absolutely know that it’s true? (Yes or no.)
3. How do you react, what happens, when you believe that thought?

When I’ve used this technique in the past, I’ve found that these are the setup questions. The real payoff is the fourth one:

4. Who would you be without the thought?

When I talked to my Mom after twelve years of estranging myself from my family, I said that I had cut communication because “I was trying to hurt her.”

I did it, I said, “even though I knew I would hurt my Dad.”

She said, “You didn’t hurt me. You couldn’t.”

I said, “I know, but I could not have lived with myself if I didn’t try.”

She nodded as though that made sense.

WTF? What kind of belief was driving my behavior? And who would I have been without that thought?

Back then, I had not yet discovered Byron Katie. If I had, and if I’d done my Work, it might have change things. It might have helped me change my attachment to the angry thoughts that drove my behavior.

And boy, was I was attached.

I was prepared to suffer, to cause my Dad to suffer, to deny my kids the benefits of knowing their grandparents and the rest of their family for those beliefs.

Who would I have been without the belief that I “needed” to try to get back at my Mom? What would have happened if I’d asked or been asked that question?

I’d like to hope I’d have changed.

After asking the four questions, the next step is to: “Turn the thought around.”

I see that as a way to restore the ability to take charge of beliefs.

It’s not a matter of getting rid of harmful beliefs—though that may happen, but rather a subject of inquiry. Asking questions. Considering alternatives.

Beliefs are tools that I can use, not forces that control me.

What person does follows from what a person believes.

To change behavior, change beliefs.

And the way to change beliefs is through force or rejection but through inquiry.

So that’s today’s backup.

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​

Updating your models

"The stock market is crazy right now!" "Stock prices make no sense!" "The market has lost all connection with reality." "The prices of stocks are wrong."

 No. 

The models that lead to these conclusions must be wrong.

I don't know the correct model, but here is what I do know:

I know that the market cannot have lost connection with reality. It is part of reality. It must be that the model that's lost its connection with reality.

The prices can't be wrong. They just don't represent or measure what your model said they do. It's your model that's wrong, not the prices.

If the prices seem to make no sense, it can only be because they don't fit your sense-making model. If you had a model that predicted (maybe not with certainty, but with say, 40% probability) the current prices, you'd say the prices made sense. If your model says 2% or 0.1%, it's likely your model that makes no sense in the current environment.

Do you think the stock market is crazy right now? No, sorry. It's your model that's crazy. And if you keep insisting that your model is right and reality is wrong, then it's you that's crazy.

## Creating and updating models

Reality is complicated, and people make models to make sense of it. They do it by creating a simplified view of reality that they believe represents reality well enough to understand what is happening or predict what will happen. 

To simplify reality, modelers must decide what components of reality have significant causal effects. And they remove from consideration those that they believe have little impact.

Then they propose simplified, sensible mechanisms that link cause and effect.

But they could be wrong about any of this. 

Things that they think are important might not be. Something that they think is unimportant might turn out to be. They may have missed some causal links and overestimated others.

When a model--however sensible--fails to match reality, it's the model that's wrong, not reality.

## The coin flipping game

Consider a coin-flipping game. It costs $1 to bet head or tails. If you guess right, you get $2.00. Wrong you get nothing. 

After examining the coin and seeing nothing strange about it, you start with a model that says that both heads and tails are equiprobable.  I see nothing wrong with that.

You flip the coin. It comes up heads. Do you change your model to say that heads is more likely? Probability 101 says, no. You've learned in school that the fact that it landed heads does not change the future probability distribution.  Your 50:50 model predicts heads half the time, and you got heads, so there's no need to change your model.

But Reality 101 says you should change your model and prefer heads.

What???

## Max and Betty flip coins

Consider Modeller Max and Bayesian Betty. Max studies the problem, develops a model, and sticks with it--unless there's some set of circumstances that the model cannot explain. His model predicts getting heads half the time, so there's certainly no need to change the model the first time he sees heads.

Like Max, Bayesian Betty starts with a probability distribution--the [Prior probability](https://en.wikipedia.org/wiki/Prior_probability)--based on her abstract model. But unlike Max, she considers the model as tentative. She adjusts the probability each time she makes an observation and uses that adjusted probability for the next round.

Max argues that it does not matter whether he bets head or tails. Since the probability is 50:50, he can bet heads every time, tails every time, or make his bet based on a (pseudo) Random Number Generator (RNG).

Betty argues that it does matter what she bets. Whether her initial bet pays off or not is a matter of luck. But she believes that updating her model on each coin flip will give her an edge--if there's an edge to be had.

Max laughs at her for falling prey to the Gambler's Fallacy. He even gives her a link to the [Wikipedia artice](https://en.wikipedia.org/wiki/Gambler%27s_fallacy). She is unmoved.

It turns out that the Gambler's Fallacy is itself a fallacy, hewn to steadfastly by people who have fallen prey to what Nick Taleb calls the [Ludic fallacy](https://en.wikipedia.org/wiki/Ludic_fallacy)

## What does it take to change your mind?

Suppose you flip a coin and get three heads in a row. Most people will say, "my model predicts I will get three heads (or tails) in a row quite often." (The probability of 3 flips all heads is 1/8, and all tails is 1/8. So the probability of three the same in three flips is 1/4. 

So they don't update the model.

What about ten heads in a row? 

People schooled in the Gambler's Fallacy will say: it makes no difference. Heads and tails are both equally probable.

Stop and think for a minute. How many heads in a row would it take to convince you that your model was wrong? 

Think of a number?

Would it be 20? 50? 100? How about 1,000,000? 

I hope that you would not say, "No, the coin is fair, and no amount of evidence would convince me otherwise. My model lets me calculate the probability of 1,000,000 coin flips in a row or even 10E200. These are unlikely but still possible, so my model is valid."

I hope you would pick an actual number and say, "When I see this many heads in a row, I will agree that it's not 50:50. I don't know why, but I'm now convinced my model is wrong."

Let's say your number is 100.

Then, I would ask: "What about 99? What's magical about 100?" 

If that number of heads in a row would convince you that heads and tails were not equally likely, why would one less not persuade you?

## Theory and practice

In theory, a coin that is assumed to be fair is, in fact, fair. 

In practice, a coin that is assumed to be fair may not, in fact, be fair.

A coin is a physical object, not an ideal one. A real coin may not act like an ideal coin. If it's not perfectly balanced, it may land on one side rather than the other.

Whatever process is used to flip is a physical process. Although it's intended to flip it in a way that makes the outcome random, the procedure might introduce some bias. 

But maliciousness or trickery is possible. A real coin doesn't appear just because someone assumes it exists. Whoever picked it might have rigged the coin or the flipping mechanism. 

In theory, the Gambler' Fallacy is a fallacy. 

In practice, it's a false fallacy.

## Expected payoffs

Modeler Max uses a Random Number Generator to choose his bets. His expected winnings are zero, whether the coin is biased or not. Max bets heads roughly half the time and tails roughly half the time. So fair coin or not, he can expect to break even.

Bayesian Betty uses her Bayesian model to guide her bets. If the coin is fair, Betty will break even. But if Betty the coin is unfair, Betty's model will reflect that, and she will win. The more unfair the coin, the more she'll win. 

## The probability that fair is fair

Max's model does not consider this important detail:  what _is__ the probability that a game, like coin flipping, or roulette, or dice is fair? 

Max assumes that if it's said to be fair, it's fair.

Intuitively we know this is wrong the wrong thing to assume.

Suppose a guy walks up to you in a bar offers the following bet: he'll flip a coin ten times that he's pulled out of his pocket. If it comes up tails even once in those ten tries, he'll pay you $100. Only if it comes up heads ten times in a row, will you have to pay him $100. 

Would you take the bet? 

I wouldn't.

I have a prior that says: "If anyone in a bar offers you to make a bet that you think he's unlikely to win, the probability of him winning is 100%."

What if he lets you inspect the coin?

My prior says, "The probability is 100% that he is better at concealing the way he's going to win his rigged bet than I am at discovering it." So, no.

I don't think you would either.

## Back to the market

So what would explain the current state of the stock market relative to the state of the economy? Where might our models be wrong?

(Again, they must be wrong if they don't predict reality. We need to discover what models might predict what we see.)

The coin-flipping case illustrates a common failing: what assumptions are we making that are not true?

I'll offer a couple of possibilities in the next post. 

I'll lead with one that I heard from Andrew Yang today on his podcast interview with Sam Harris: 

"And you can see it in what the stock market is saying where when people are announcing record layoffs, their prices go up, though the stock values go up because investors know that if you can shrink your workforce, then the returns on capital will be higher."

"Yes," you might say, "but those laid-off workers are consumers. The fact that they are laid off **will** reduce their buying power. As a result, these companies **will** do less well? Investors **should** realize that. So the stock prices **should** go down, not up."

If that's what you are thinking, my answer is: "Each of those "shoulds" and "wills" is a prediction of your model, not necessarily a fact about reality. Some might be correct. But if reality doesn't match your model, your model is wrong."

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Models and sense-making

A friend said this in a forum we both post to:

>The stock market is behaving unusually for the current economic climate in the US / the world.  Why is it going up - while businesses and the economy looks to be degrading.    This doesn't make much sense.

To say that market is not "behaving unusually" means that you don't have the right model and you using the model, not reality, as the standard.

If you had a more accurate model, you'd see that it was "behaving correctly."

To say, "This does not make much sense" means that reality does not conform to your model. If you had a useful model, what was happening would make senes.

Responding with "my model is wrong" starts the search for a correct model. Any other choice defends the current model.

Lots of people are saying the same kinds of things because they have the same sorts of model. This is confirmation bias for their models. A better strategy is acknowledging that the mode is wrong, not reality, and finding a model that makes reality comprehensible.

What's a better model? I don't know. I have some clues. But that's a different discussion.

This is about what I think is the right move when "things don't make sense" or "seem unusual" or "seem crazy."

You take that as evidence that your model is wrong, and--if you have the time and interest and maybe skill--you look for a better model.
.
## Does a better model exist?

Either (a) there can be a model that explains the current behavior of the market based on a set of measurable parameters, or (b) no such model can exist. 
 
To choose (b) is equivalent to saying, "there is no causal basis for what now exists." I think that's wrong. 
 
It could be that the causal structure is so complicated that modeling is theoretically possible, but pragmatically impossible. 
 
That might be true.

My belief without foundation is that things that happen have causes, and you can build a model that abstracts at least some of that information and provides insight--even if you can't make an exact prediction, you can still make a model. 
 
So I will assert that a model can exist. 

But that's just my belief about the orderly nature of the universe.
 
I don't know the model. Maybe nobody knows it, but I would not be surprised to learn that some people have figured out some parts of it and are making money. I would also expect that other people have made lucky guesses and are making money, too, but that will come to an end when their luck runs out.
 
Or maybe not. 
 
I am trying to argue that when tempted to say things, as in the Vox article "the market makes zero sense" and "nobody knows what's going on" that the better answer is "I don't understand the market because I don't have a model" or "the people I know don't have good models." 
 
I don't claim I know the right model. But I have collected some ideas from other people and written about them, and using those models, my view is closer to "I see some reasons that this might be happening" rather than "this makes no sense." 
 
In any case, my point here, to reiterate: when something seems crazy, it's your model that's wrong. Not reality.

Disinfecting my mind: a case study

Earlier today, I wrote a post, Defending our minds because the day before a friend posted a link to an article.

Don’t read the article

I encourage you not to read it. It will make you stupider.

Even reading the headline and the parts I’ve excerpted—each followed by an explanation of what’s wrong with it—risks making you stupider.

Sorry. But I thought it might be worth following my prescriptive post with a practical example of the harm that can come from wasting time, reading a shitpost, and the harm that might come from sharing it.

Before the article, the facts.

Neil Ferguson (epidemiologist) - Wikipedia), is an epidemilologist at Imperial College London.

He’s the leader of the Imperial_College_COVID-19_Response_Team. There are about 30 people on the team.

The Response Team has so far produced 30 papers. The most significant, concerning government policy, is probably Report 9 - Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand with 32 co-authors, and Ferguson as the lead.

He’s a member of the Scientific Advisory Group for Emergencies. There are 22 other members, most with relevant science backgrounds.

At the end of March and the first week in April, a woman named Anita Staats traveled across London to spend time with him. He’s 51 and married. She’s 38 and married.

The article in question comes from ZeroHedge. If you know ZeroHedge, none of this will surprise you.

The title: is: Scientist Whose Doomsday Models Sparked Global Lockdown Resigns After Breaking Quarantine To Bang Married Lover | Zero Hedge

Stop for a minute.

What’s the likelihood that this article will make you any wiser, better informed about things that matter?

I didn’t stop. Someone who I like and respect had posted it. I should have known better.

The lurid headline misleads in many ways. The model wasn’t his, but the product of a team. It was one of several other models almost all of which said the same thing: without mitigation, the UK would see the kinds of problems that Italy, Spain, and France were experiencing, and China had experienced.

The paper proposes that the response might be mitigation or the stronger measures of suppression.

suppression will minimally require a combination of social distancing of the entire population, home isolation of cases, and household quarantine of their family members. This may need to be supplemented by school and university closures.

Was the model correct? We don’t know. But the number of deaths in the UK was growing exponentially—as it had in Italy and Spain before those countries took drastic action. So it was probably the reality as much as the model that prompted action.

The story tells us that:

Ferguson, who resigned from his Government advisory position on Tuesday, predicted that up to 500,000 Britons and 2.2 million in the US would die without measures. Somehow, Sweden - which enacted virtually no measures to mitigate the virus, has a lower per-capita mortality rate than the UK, Italy, Spain, France, Belgium and the Netherlands - all of which enacted lockdown measures.

First off, Sweden did enact measures. Although not as many as were enacted in the UK. But never mind that detail. What conclusion are we to draw from this paragraph? That enacting “virtually no measures” reduced the per-capita mortality rate?

Why don’t we compare Sweden with some nearby countries that are a bit more demographically and culturally similar to see how well Sweden is doing with “virtually no measures.”

Our World In Data provides an interactive graphic here (and you can go there and play with some other countries to test your own theories. The screen capture is from May 7. YMMV

.png?dppx=2).png)

Jeez. Now Sweden doesn’t look so good. But let’s throw the UK back into the mix and think for just a second. Really only one second.

If a group of countries clusters around 50 deaths per million WITH measures, and a similar country has 5-6 times as many deaths with measures, and if the UK has more deaths than that with measures, what would likely happen in the UK if there were no measures?
a) Half as many, because measures are bad
b) Many more, because measures are good

I could go on. And on. The article is a hot mess of inuendo. What effect does the fact that the lead researcher for a modeling group can’t his dick in his pants have on the validity of his model?

I’m sorry I read the article and damaged my mind.

I’m sorry that I then needed to spend time finding the facts so that I could undo some of the damage.

I’m not sorry that I wrote this. I hope you’re not sorry if you read it.

​

Defending our minds

Your mind is a delicate device, evolved over nearly 14 billion years.

Spend time reading, listening to, and interacting with that which will make your mind—and you—better.

Avoid reading, listening to, and interacting with that which will make you—and your mind—worse.

Share only things that will make others better.

Avoid mental contamination.

Everything changes your mind

Whenever someone reads something or listens to something or has a conversation with someone, it changes the contents and structure of their mind.

Of course.

Some interactions are neutral. Most interactions change minds for better or worse.

If our minds are made worse, we are made worse.

If our minds are made worse, we need to repair the damage, or our minds will remain worse.

We can keep ourselves from things that make us worse.

We can avoid connecting others with things that make them worse.

What makes us worse

An interaction that delivers false information makes us worse.

An interaction that delivers misleading information makes us worse.

An interaction that adds useless information makes us worse.

An interaction that misplaces importances makes us worse.

An interaction that produces unhelpful emotion makes us worse.

An interaction that distracts us from productive activity makes us worse.

An interaction that creates confusion makes us worse.

What makes us better

An interaction that corrects an error makes us better.

An interaction that helps us represent knowledge more simply makes us better.

An interaction that adds useful knowledge makes us better.

An interaction that helps us organize knowledge makes us better.

An interaction that helps us think more clearly makes us better.

An interaction that helps us focus on what we deem important makes us better.

An interaction that produces helpful emotion makes us better.

An interaction that creates the optimal balance between order and disorder makes us better.

An interaction that builds our cognitive skills—attention, perception, reasoning—makes us better.

What does not make us better makes us worse

Any interaction costs us the time that we spent in that interaction.

If it does not make us enough better to offset that cost, then we are worse.

The harm done by an interaction that will persist until we spend additional time undoing the harm.

If we do not have the necessary skills, we may not be able to undo the harm.

Avoiding harm is better than repairing it

When we carry out an interaction that causes us mental harm, we’ve wasted the time spent in the interaction, we’ve cost ourselves the harm to ourselves, and will cost ourselves the time we will need to spend undoing the harm.

When we expose others to a source of harm, we risk causing them to waste time on the interaction, the harm to themselves, and the time that they must spend undoing the harm.

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