FTA part 2: Prediction distributions and inflation

This is the second part of a series discussing the Fine Tuning argument (FTA). The outline is here.

Comparing Hypotheses

Previously, I explained how the Fine-Tuning argument assumes this kind of picture:

A graph showing the probability of life vs the parameters of the universe. The probability is sharply peaked at x_0.

The probability of life is sharply peaked. x0 marks the parameters of the universe that we have in the real world.

Does this graph mean that life is unlikely? No, not necessarily. It depends on the probability distribution of the parameters of the universe. For example, here are three possible probability distributions A, B, and C. Under probability distribution A, or C, life is very unlikely. Under probability distribution B, life is much more likely.

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The Fine-Tuning Argument: A walkthrough

The Fine-Tuning Argument (FTA) is one of those standard arguments for the existence of God. The argument goes that humans can only arise when the parameters of the universe are tuned exactly right. And while it’s possible that we just got lucky, the argument goes that it’s far more likely that God did the tuning.

The standard way to talk about the FTA is delve into a bunch of math equations.  Not that there’s anything wrong with math, but here I wanted to write an in-depth overview that doesn’t talk about the math.  There will, however, be a lot of physics.  The goal here is not to refute the FTA (although refutations will occur incidentally), but to explore it, and to understand how we test hypotheses about the universe.

Outline

(Links to be added later)

1. The Fine-Tuning Argument: A walkthrough
2. Prediction distributions and inflation
3. Ignorant hypotheses
4. Anthropic reasoning

Perma-link to entire series

The parameters of the universe

The core premise of the FTA is that the universe is fine-tuned. Which is to say, the probability of life looks like this:

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The Second Law and its misuses

An OrbitCon session brought to my attention to the fact that Steven Pinker spouts a lot of bullshit about the Second Law of Thermodynamics. (The Second Law is, “entropy cannot decrease over time in a closed system.”) In Pinker’s book, Enlightenment Now, he begins by refuting the creationist argument that the Second Law contradicts the theory of evolution. This is easy to do, you just say that the earth isn’t a closed system, and dramatically point at the sun. But Pinker then proceeds to forget about the sun, and argues that the Second Law of Thermodynamics explains poverty. I don’t have the book available, but Pinker has written an essay along similar lines:

Poverty, too, needs no explanation. In a world governed by entropy and evolution, it is the default state of humankind. Matter does not just arrange itself into shelter or clothing, and living things do everything they can not to become our food. What needs to be explained is wealth.

Here’s the thing: creationists are really really wrong about the Second Law. There’s plenty of room to be less wrong than creationists, but still really really wrong. For those of us who have taken an interest in fighting creationism, we know we can just point at the sun and be done with it. But just because you’re familiar with this argument, please don’t mistake that for an understanding of the Second Law. Don’t be like Steven Pinker.

Here I will state and explain a few basic principles about entropy, with the goal of going beyond a mere refutation of creationist arguments.

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Sleeping Beauty and Quantum Mechanics

This is a repost of an article I wrote in 2014.  Note that Sean Carroll also wrote about this, and he’s an author of the cited paper.

My newest favorite philosophical dilemma is the Sleeping Beauty problem.  The experiment goes as follows:

1. Sleeping Beauty is put to sleep.
2. We flip a coin.
3. If the coin is tails, then we wake Sleeping Beauty on Monday, and let her go.
4. If the coin is heads, then we wake Sleeping Beauty on Monday.  Then, we put her to sleep and cause her to lose all memory of waking up.  Then we wake her up on Tuesday, and let her go.
5. Now imagine Sleeping Beauty knows this whole setup, and has just been woken up.  What probability should she assign to the claim that the coin was tails?

There are two possible answers.  “Thirders” believe that Sleeping Beauty should assign a probability of 1/3 to tails.  “Halfers” believe that Sleeping Beauty has gained no new relevant information, and therefore should assign a probability of 1/2 to tails.  The thirder answer is most popular among philosophers.

This has deep implications for physics.

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Magic-Angle Graphene Superconductors

A couple weeks ago, there was an exciting discovery in my (former) field of research. It was found that if you take two layers of graphene, and rotate one of the layers by a “magic angle” of 1.1°, then you can create a superconductor.

Some brief background on superconductors. A superconductor is a kind of material that conducts electricity with zero resistance. That means you could transport electrical power without any energy loss. Or you could create so much electrical current that it creates a powerful magnet (used in MRI machines). Superconductors also have special magnetic properties that allow for magnetic levitation (used in maglev trains). But superconductors need to be cooled below a certain temperature to work, otherwise they’re just ordinary materials.

As of 1957, physicists have a working theory of superconductors, but the theory only explains certain varieties of superconductors, called conventional superconductors. Magic-angle graphene is an unconventional superconductor.

So, why would you ever try rotating two layers of graphene? Graphene is simply a layer of carbon atoms that form a hexagonal pattern. If you overlay two hexagonal patterns with a bit of rotation, you create what’s called a Moiré pattern.

Two hexagonal grids, one rotated by 10 degrees, form a moire pattern when overlaid.

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Things I liked about grad school

To cap off my series on why grad school sucks, I’d like to talk about some of the things I did, after all, like about the experience. This will be more personally focused, and may describe aspects of grad school that other people would miss out on, or dislike.

I can read papers

I started out this series by talking about how physics talks are really bad.  Even now after finishing a PhD, I find that most talks are still incomprehensible. In contrast, I feel pretty good about my improved ability to read papers.

Note, the best way to understand more physics presentations, is to understand when a presentation is best skipped, and it’s the same way with papers. A lot of skill in reading technical papers comes from knowing when to skip a paper, or when to skip large sections of it. But also, as I got further in my Ph.D., there were fewer sections that I needed to skip, and I could return to old papers and understand them better. Some of my most satisfying experiences were going beyond mere reading, being able to critique papers in detail.

This ability extends beyond my own field of study, to other fields of physics, and to other disciplines entirely. I’ve mentioned before, I’ve read scholarly papers in math, psychology, sociology, gender studies, and law. Of course, some disciplines are more difficult than others.

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Is grad school doing what you love?

Many people place a special value on “doing what you love”. Should you become a corporate tool, or a real-life scientist? “You should do what you love” is the reply. And it’s a reply that is detached from any real cost-benefit analysis. Like, maybe you only sorta love being a real scientist, and maybe you don’t love the working conditions of a scientist, and maybe the salary of a corporate tool is so much higher that it enables you to do other things that you love. But you can’t make a snappy motto out of such considerations.

The problem with “doing what you love” is that it doesn’t come for free. If academic institutions need a certain number of grad students,* then they need to provide incentives for just enough people to apply. “Doing what you love” is one incentive, and it takes the place of other incentives that academic institutions could have offered instead. In other words, they don’t need to pay you well, or treat you well. However much grad students are willing to tolerate in order to do what they love–that’s how much they end up having to tolerate.

*I’m only talking about Ph.D. students and not Masters students. I’ve never heard anyone describe a Masters degree as doing what you love.

In economic terms, we can speak of the “marginal” grad student (a concept similar to the “swing voter”). For the marginal grad student, the expected costs and benefits are exactly equal, such that the decision to go to grad school could go either way. It may be that the marginal grad student thinks they would love being a scientist, but this is exactly offset by the costs. So for some people, grad school may be a good deal. But the deciding factor is not merely whether you love grad school, it’s whether you’d love it more than the marginal grad student.

Beyond that, I think even the marginal grad student is getting a bad deal. The marginal grad student expects they would love grad school, but ends up loving it less than they predicted.

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