Captain Trump — Making flying Great Again

In trying to find a way to explain that one can be concerned about the state of the nation, from a completely non-partisan position, Sam Harris offered an analogy.

Personally, from what I’ve observed, I think the analogy is a bit unfair to Trump, but I do think it nails the point that in the middle of a pandemic and the greatest economic shutdown in history, one should be able to express concern about leadership without accusation of simply being a shill for the other side:

The Analogy:

Imagine you’re on an airplane, cruising along at 30,000 feet, and at some point on the descent, you see the pilot come stumbling out of the cockpit. He appears just visibly drunk, or insane. Let’s say he gropes one of the attendants.

He gets on the PA system and begins bragging about how rich he is. And maybe he starts castigating the passengers for having insulted him. He might say, “If you want me to land this plane, you have to be nicer to me.” — i.e. something completely out of keeping with the role and responsibility he has to safeguard the lives of the people on that plane.

Then he could launch into a conspiracy theory about how the airline is actually run by a shadowy group of maintenance workers, who’ve been undermining him. He thinks they’ve been monkeying with the instruments in the cockpit.

And maybe he fires the co-pilot, and sends him to the back of the plane, telling him not to move.

He could do a dozen other things like that, in the span of an hour, that prove beyond any shadow of a doubt that this isn’t a “normal” situation. This is not a normal pilot.

So when it comes time to land this plane, the danger has been horribly magnified. And — as a passenger — you’re simply worried about this.

Now, imagine worrying about this out loud, and then noticing there are people on this plane that are actually inspired by this pilot’s antics, and goading him on.

He’s now threatening to punch some old woman in the face, and people are yelling he should do it.

And then people are turning to you, as you worry about this out loud, and accuse you of having “pilot derangement syndrome”, and that you should stop worrying and just enjoy the flight, and that Captain Trump is making flying great again.

So the question is: How much of your concern about dying in a plane crash, is due to partisanship? It should be obvious that that’s not even a variable.

The case against identity politics

In episode 45 of his “Waking Up” podcast, I loved this piece by Sam Harris on identity politics, and wanted to capture it here for future reference:

Sam Harris:

As far as I can tell, becoming a part of a movement doesn’t help anybody think clearly, so I distrust identity politics of all kinds. I think we should talk about specific issues, whether it’s trade or guns or immigration or foreign interventions or abortion or anything else. And we should reason honestly about them.

And I’m not the first person who has noticed that it’s pretty strange that knowing a person’s position on any one of these issues, generally allows you to predict his position on any of the others. This shouldn’t happen. Some of these issues are totally unrelated. Why should a person’s attitude towards guns be predictive of his views on climate change? Or immigration? Or abortion?

And yet, it almost certainly is in our society. That’s a sign that people are joining tribes and movements. It’s not the sign of clear thinking.

If you’re reasoning honestly about facts, then the color of your skin is irrelevant. The religion of your parents is irrelevant. Whether you’re gay or straight, is irrelevant. Your identity is irrelevant. In fact, if you’re talking about reality, its character can’t be predicated on who you happen to be. That’s what it means to be talking about reality.

And this also applies to the reality of human experience, and human suffering. For example, if vaccines don’t cause autism, if that is just a fact—which is what the best science suggests at this point—well, then to argue against this view, you need data. Or a new analysis of existing data. You need an argument. And the nature of any argument is that its validity doesn’t depend on who you are. That’s why a good argument should be accepted by others, no matter who they are.

So in the case of vaccines causing autism, you don’t get to say, “As a parent of a child with autism, I believe X, Y and Z.” Whatever is true about the biological basis of autism, can’t depend on who you are. And who you are in this case, is probably adding a level of emotional engagement with the issue, which would be totally understandable, but would also be unlikely to lead you to think about it more clearly.

The facts are whatever they are. And it’s not an accident that being disinterested—not uninterested, but disinterested, meaning not being emotionally engaged—usually improves a person’s ability to reason about the facts.

When talking about violence in our society, again, the facts are whatever they are. How many people got shot? How many died? What was the color of their skin? Who shot them? What was the color of their skin? Getting a handle on these facts doesn’t require one to say, “As a black man, I know X, Y and Z.” The color of your skin, simply isn’t relevant information.

When talking about the data, that is, what is happening throughout a whole society, your life experience isn’t relevant information. And the fact that you think it might be, is a problem.

Now this isn’t to say that a person’s life experience is never relevant to a conversation. Of course it can be. And it can be used to establish certain kinds of facts. If someone says to you, “Catholics don’t believe in hell”, it’s perfectly valid to retort, “Actually my mom is a Catholic, and she believes in hell.” Of course there’s a larger question of what the Catholic doctrine actually is, but if a person is making a statement about a certain group of people, and you are a member of the group, you might very well be in a position to falsify his claim, on the basis of your experience.

But a person’s identity and life experience usually aren’t relevant, when talking about facts. And they’re usually invoked in ways that are clearly fallacious. And many people seem to be making a political religion out of ignoring this difference, so I urge you not to be one of those people, whether you’re on the left or the right.

Taxes paid vs benefits received

The Heritage Foundation has an interesting 2015 article on, The Redistributive State: The Allocation of Government Benefits, Services, and Taxes in the United States. In it, I saw this interesting chart comparing taxes paid versus benefits received, based on income. I’ll refer to this next time someone mentions that the wealthy aren’t paying their fair share:

An eye-opening example of the importance of mathematical literacy

If you’d like to understand the importance of having a good knowledge of probability theory, consider the following eye-opening example (inspired by something I read at David Siegel’s site.)

A rare disease is known to exist in 1% of the population. A test for the disease is known to be 98% accurate, meaning that if you have the disease, the test will return positive 98% of the time.

Now, you’re curious whether you might have the disease and so you go take the test. It comes back positive. What is the probability you actually have the disease? The results might surprise you.

To solve this problem, we use Baysian probability theory, which says:

P(A|B) = P(A)*P(B|A)/P(B)

Where:

  • A = You have the disease
  • B = You test positive

In words, this means that the probability that you have the disease (A) and you test positive (B) is the probability that you have the disease, P(A), times the probability that you test positive given that you have the disease, P(B|A), divided by the probability that you test positive, P(B).

So to make this calculation we need three numbers:

  1. P(A) — We know that P(A) (the probability we have the disease) is 1%.
  2. P(B|A) — We know that P(B|A), the probability that we test positive if we have the disease, is 98%.
  3. P(B) — We don’t know this one, and have to calculate it.

We can compute P(B)—i.e. the probability that a random person taking the test returns positive—using “conditional” probability:

P(B) = P(B|A)P(A) + P(B|!A)P(!A)

This means that the probably of testing positive, is the sum of the conditional probabilities that (a) we test positive given that we have the disease times the probability that we actually have the disease, plus (b) the probability that we test positive given that we don’t have the disease, times the probability that we don’t have the disease.

In the above conditional probability equation, we know all the values except P(B|!A). How to determine this? Well, we know the following must be true:

P(B|A) + P(B|!A) = 100%

Therefore, since we know P(B|A) is 98%, we can conclude that P(B|!A) must be 2%.

P(B|!A) is known as the “false positives”, i.e. those who test positive but don’t have the disease.

Therefore:

P(B) = 0.98*0.01 + 0.02*0.99 = 0.0296

So now we have the all the numbers to calculate P(A|B), i.e. that chances that we actually have the disease given that we tested positive for it:

P(A|B) = 0.01*0.98/0.0296 = 0.33

Surprising no? If we went to the doctor, took this test, and tested positive, there would only be a 33% chance that we actually have the disease.

How can we make sense of this? It’s actually quite logical.

Imagine a random population sample of 1,000,000 people. Of those, 10,000 (1%) will have the disease. Of those 10,000 tested, 9,800 (98%) will diagnose correctly in the test. Of the 990,000 (99%) who don’t have the disease, 19,800 will test positive, i.e. the 2% false-positive percentage.

So of the 1,000,000 people tested, 29,600 will test positive, but very few of those will really have the disease, i.e. 9,800/29,600 or 33%.