FTA part 4: Anthropic reasoning

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

Mundane multiverses

A “multiverse” is a set of multiple sub-universes, which together comprise a single super-universe. The idea is that the universe that we know is a single sub-universe, and there are multiple other universes like ours. So we can imagine another sub-universe where everything is the same, except that ever coin flip comes up on the opposite side.  Or a sub-universe where everything is the same, but we’re all evil and have goatees. Or another sub-universe where

Typically, when physicists talk about parallel sub-universes, what they mean are non-interacting sub-universes. So, you can’t ever talk to the evil goatee’d version of yourself. Although, people sure like to imagine that sort of thing in sci-fi.  So let’s talk about the kind of parallel universe that we could, in principle, interact with. What if I told you that this kind of parallel universe is one we already know exists?

To travel to a parallel universe, just hop in a space ship, and travel 4 lightyears over to Proxima Centauri. You will find a universe exactly like ours, except that the sun is different, and the planets are different, and the constellations are different.

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FTA part 3: Ignorant hypotheses

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

Uncertainty vs Ignorance

Earlier, I showed this plot showing probability distributions for three possible hypotheses of the universe. The FTA contends that the naturalism hypothesis predicts distribution A, and the God hypothesis predicts distribution B.

A graph showing three possible probability distributions. A is a very broad probability distribution. B is a sharp probability distribution centered at x_0. C is a sharp probability distribution centered away from x_0.

Three possible probability distributions. x0 marks the parameters of the universe that we have in the real world.

I will refer to a distinction that is commonly made between “uncertainty” and “ignorance”. “Uncertainty” refers to a situation where you don’t know what’s going to happen, but you at least have probabilities, a way to quantify how much you don’t know. “Ignorance” refers to a situation where you don’t know what’s going to happen, and you don’t even know how much you don’t know. When we compare probability distributions in the FTA, the probability distributions are just cartoons.  We don’t have any real probabilities.  We’re operating from a state of ignorance, not uncertainty.

Or, in other words, we pulled these probability distributions out of our asses.

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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 ethics of educational experiments

Rebecca Watson had an interesting article/video about the ethics of A/B testing. A/B testing is a type of experiment often performed by tech companies on their users. The companies split users into two groups, and show two different versions of their software/website to each group, and measure the results. The problem is that when scientists perform experiments on human subjects, there’s a formal ethical review process. Should tech companies have an ethical review process too?

Of course, this question is being raised as a result of a specific experiment performed by a specific company. Pearson produces educational software, and performed an A/B test where some students were shown motivational messages. They presented results at a conference, and part of their conclusion was that this was a promising methodology for future research. But is it really, if they didn’t comply with the ethical standards in science? They certainly didn’t get consent from all those human test subjects.

Watson also brought up another case from 2014, when Facebook performed an experiment that changed the amount of positive/negative posts people saw in their news feeds. They published a study, and it was called “Experimental evidence of massive-scale emotional contagion through social networks”. Sounds pretty bad, eh?

Watson seems to conclude that A/B tests should get consent, at least in the case of Pearson. But I think this is going too far. The thing is, A/B testing is absolutely ubiquitous. Watson says, “having worked in marketing and seen A/B tests, it’s just a normal thing that companies do,” but I think this understates it. My fiance and I were trying to figure out how many A/B tests Google has running at any time, and we thought it might be one per employee, implying tens of thousands of experiments. And most of them are for boring things like changing fonts or increasing the number of pixels of white space. If we judge A/B tests on the basis of just two tests that appear in the news, “cherry picking” doesn’t even begin to describe it.

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The construction of emotions

I recently read How Emotions are Made, by Lisa Feldman Barrett. It explains the theory of constructed emotion and its implications. This is the best book of nonfiction I have ever read. Repeatedly throughout the book, I had to put it down because I was so blown away that I needed a moment to think through the implications. And since I’m a blogger, my thoughts would often drift towards how I might write about these ideas and share them. This post will be a bit of an introduction explaining the basic concepts as I understand them, and I hope to write more in the future.

Regular readers know that I believe in nominalism–I think there is a meaningful sense in which everything is socially constructed. I understand that a lot of readers disagree with this, and we may never persuade one another. But fortunately this is irrelevant. When we speak of the theory of constructed emotions, it isn’t a broad philosophical claim, it’s an empirical claim that is specific to human emotions.

When psychologists study emotions, they can record a number of objective measurements, such as facial configurations, positive/negative valence, high/low arousal, and activity in different parts of the brain. However, these objective measurements do not match up to emotional categories. A single emotional category could correspond to many different facial configurations, while a single facial configuration could correspond to any number of different emotions. Yes, there are many qualitatively distinct feelings we can feel. However, when we give a name to those feelings, and place those feelings in an emotional category, this categorization process is not purely based on the feelings themselves. It’s based on the emotional concepts that are available to us, it’s based on the context in which we have those feelings, and it’s based on what we think the purpose of those feelings are.

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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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