# Elon Musk’s Techno-Religion

A couple of people have written to me asking me to say something about Elon Musk’s simulation argument.

Unfortunately, I haven’t been able to find a verbatim quote from Musk about his argument, and I’ve seen a couple of slightly different arguments presented as being what Musk said. So I’m not really going to focus so much on Musk, but instead, just going to try to take the basic simulation argument, and talk about what’s wrong with it from a mathematical perspective.

The argument isn’t really all that new. I’ve found a couple of sources that attribute it to a paper published in 2003. That 2003 paper may have been the first academic publication, and it might have been the first to present the argument in formal terms, but I definitely remember discussing this in one of my philophy classes in college in the late 1980s.

Here’s the argument:

1. Any advanced technological civilization is going to develop massive computational capabilities.
2. With immense computational capabilities, they’ll run very detailed simulations of their own ancestors in order to understand where they came from.
3. Once it is possible to run simulations, they will run many of them to explore how different parameters will affect the simulated universe.
4. That means that advanced technological civilization will run many simulations of universes where their ancestors evolved.
5. Therefore the number of simulated universes with intelligent life will be dramatically larger than the number of original non-simulated civilizations.

If you follow that reasoning, then the odds are, for any given form of intelligent life, it’s more likely that they are living in a simulation than in an actual non-simulated universe.

As an argument, it’s pretty much the kind of crap you’d expect from a bunch of half drunk college kids in a middle-of-the-night bullshit session.

Let’s look at a couple of simple problems with it.

The biggest one is a question of size and storage. The heart of this argument is the assumption that for an advanced civilization, nearly infinite computational capability will effectively become free. If you actually try to look at that assumption in detail, it’s not reasonable.

The problem is, we live in a quantum universe. That is, we live in a universe made up of discrete entities. You can take an object, and cut it in half only a finite number of times, before you get to something that can’t be cut into smaller parts. It doesn’t matter how advanced your technology gets; it’s got to be made of the basic particles – and that means that there’s a limit to how small it can get.

Again, it doesn’t matter how advanced your computers get; it’s going to take more than one particle in the real universe to simulate the behavior of a particle. To simulate a universe, you’d need a computer bigger than the universe you want to simulate. There’s really no way around that: you need to maintain state information about every particle in the universe. You need to store information about everything in the universe, and you need to also have some amount of hardware to actually do the simulation with the state information. So even with the most advanced technology that you can possible imagine, you can’t possible to better than one particle in the real universe containing all of the state information about a particle in the simulated universe. If you did, then you’d be guaranteeing that your simulated universe wasn’t realistic, because its particles would have less state than particles in the real universe.

This means that to simulate something in full detail, you effectively need something bigger than the thing you’re simulating.

That might sound silly: we do lots of things with tiny computers. I’ve got an iPad in my computer bag with a couple of hundred books on it: it’s much smaller than the books it simulates, right?

The “in full detail” is the catch. When my iPad simulates a book, it’s not capturing all the detail. It doesn’t simulate the individual pages, much less the individual molecules that make up those pages, the individual atoms that make up those molecules, etc.

But when you’re talking about perfectly simulating a system well enough to make it possible for an intelligent being to be self-aware, you need that kind of detail. We know, from our own observations of ourselves, that the way our cells operates is dependent on incredibly fine-grained sub-molecular interactions. To make our bodies work correctly, you need to simulate things on that level.

You can’t simulate the full detail of a universe bigger that the computer that simulates it. Because the computer is made of the same things as the universe that it’s simulating.

There’s a lot of handwaving you can do about what things you can omit from your model. But at the end of the day, you’re looking at an incredibly massive problem, and you’re stuck with the simple fact that you’re talking, at least, about building a computer that can simulate an entire planet and its environs. And you’re trying to do it in a universe just like the one you’re simulating.

But OK, we don’t actually need to simulate the whole universe, right? I mean, you’re really interested in developing a single species like yourself, so you only care about one planet.

But to make that planet behave absolutely correctly, you need to be able to correctly simulate everything observable from that planet. Its solar system, you need to simulate pretty precisely. The galaxy around it needs less precision, but it still needs a lot of work. Even getting very far away, you’ve got an awful lot of stuff to simulate, because your simulated intelligences, from their little planet, are going to be able to observe an awful lot.

To simulate a planet and its environment with enough precision to get life and intelligence and civilization, and to do it at a reasonable speed, you pretty much need to have a computer bigger than the planet. You can cheat a little bit, and maybe abstract parts of the planet; but you’ve got to do pretty good simulations of lots of stuff outside the planet.

It’s possible, but it’s not particularly useful. Because you need to run that simulation. And since it’s made up of the same particles as the things it’s simulating, it can’t move faster than the universe it simulates. To get useful results, you’d need to build it to be massively parallel. And that means that your computer needs to be even larger – something like a million times bigger.

If technology were to get good enough, you could, in theory, do that. But it’s not going to be something you do a lot of: no matter how advanced technology gets, building a computer that can simulate an entire planet and its people in full detail is going to be a truly massive undertaking. You’re not going to run large numbers of simulations.

You can certainly wave you hands and say that the “real” people live in a universe without the kind of quantum limit that we live with. But if you do, you’re throwing other assumptions out the window. You’re not talking about ancestor simulation any more. And you’re pretending that you can make predictions based on our technology about the technology of people living in a universe with dramatically different properties.

This just doesn’t make any sense. It’s really just techno-religion. It’s based on the belief that technology is going to continue to develop computational capability without limit. That the fundamental structure of the universe won’t limit technology and computation. Essentially, it’s saying that technology is omnipotent. Technology is God, and just as in any other religion, it’s adherents believe that you can’t place any limits on it.

Rubbish.

# One plus one equals Two?

My friend Dr24hours sent me a link via twitter to a new piece of mathematical crackpottery. It’s the sort of thing that’s so trivial that I might lust ignore it – but it’s also a good example of something that someone commented on in my previous post.

This comes from, of all places, Rolling Stone magazine, in a puff-piece about an actor named Terrence Howard. When he’s not acting, Mr. Howard believes that he’s a mathematical genius who’s caught on to the greatest mathematical error of all time. According to Mr. Howard, the product of one times one is not one, it’s two.

After high school, he attended Pratt Institute in Brooklyn, studying chemical engineering, until he got into an argument with a professor about what one times one equals. “How can it equal one?” he said. “If one times one equals one that means that two is of no value because one times itself has no effect. One times one equals two because the square root of four is two, so what’s the square root of two? Should be one, but we’re told it’s two, and that cannot be.” This did not go over well, he says, and he soon left school. “I mean, you can’t conform when you know innately that something is wrong.”

I don’t want to harp on Mr. Howard too much. He’s clueless, but sadly, he’s a not too atypical student of american schools. I’ll take a couple of minutes to talk about what’s wrong with his stuff, but in context of a discussion of where I think this kind of stuff comes from.

In American schools, math is taught largely by rote. When I was a kid, set theory came into vogue, but by and large math teachers didn’t understand it – so they’d draw a few meaningless Venn diagrams, and then switch into pure procedure.

An example of this from my own life involves my older brother. My brother is not a dummy – he’s a very smart guy. He’s at least as smart as I am, but he’s interested in very different things, and math was never one of his interests.

I barely ever learned math in school. My father noticed pretty early on that I really enjoyed math, and so he did math with me for fun. He taught me stuff – not as any kind of “they’re not going to teach it right in school”, but just purely as something fun to do with a kid who was interested. So I learned a lot of math – almost everything up through calculus – from him, not from school. My brother didn’t – because he didn’t enjoy math, and so my dad did other things with him.

When we were in high school, my brother got a job at a local fast food joint. At the end of the year, he had to do his taxes, and my dad insisted that he do it himself. When he needed to figure out how much tax he owed on his income, he needed to compute a percentage. I don’t know the numbers, but for the sake of the discussion, let’s say that he made $5482 that summer, and the tax rate on that was 18%. He wrote down a pair of ratios: $\frac{18}{100} = \frac{x}{5482}$ And then he cross-multiplied, getting: $18 \times 5482 = 100 \times x$ $98676 = 100 \times x$ and so $x = 986.76$. My dad was shocked by this – it’s such a laborious way of doing it. So he started pressing at my brother. He asked him, if you went to a store, and they told you there was a 20% off sale on a pair of jeans that cost$18, how much of a discount would you get? He didn’t know. The only way he knew to figure it out was to do the whole ratios, cross-multiply, and solve. If you told him that 20% off of $18 was$5, he would have believed you. Because percentages just didn’t mean anything to him.

Now, as I said: my brother isn’t a dummy. But none of his math teachers had every taught him what percentages meant. He had no concept of their meaning: he knew a procedure for getting the value, but it was a completely blind procedure, devoid of meaning. And that’s what everything he’d learned about math was like: meaningless procedures performed by rote, without any comprehension.

That’s where nonsense like Terence Howard’s stuff comes from: math education that never bothered to teach students what anything means. If anyone had attempted to teach any form of meaning for arithmetic, the ridiculous of Mr. Howard’s supposed mathematics would be obvious.

For understanding basic arithmetic, I like to look at a geometric model of numbers.

Put a dot on a piece of paper. Label it “0”. Draw a line starting at zero, and put tick-marks on the line separated by equal distances. Starting at the first mark after 0, label the tick-marks 1, 2, 3, 4, 5, ….

In this model, the number one is the distance from 0 (the start of the line) to 1. The number two is the distance from 0 to 2. And so on.

Addition is just stacking lines, one after the other. Suppose you wanted to add 3 + 2. You draw a line that’s 3 tick-marks long. Then, starting from the end of that line, you draw a second line that’s 2 tick-marks long. 3 + 2 is the length of the resulting line: by putting it next to the original number-line, we can see that it’s five tick-marks long, so 3 + 2 = 5.

Multiplication is a different process. In multiplication, you’re not putting lines tip-to-tail: you’re building rectangles. If you want to multiply 3 * 2, what you do is draw a rectangle who’s width is 3 tick-marks long, and whose height is 2 tick-marks long. Now divide that into squares that are 1 tick-mark by one tick-mark. How many squares can you fit into that rectangle? 6. So 3*2 = 6.

Why does 1 times 1 equal 1? Because if you draw a rectangle that’s one hash-mark wide, and one hash-mark high, it forms exactly one 1×1 square. 1 times 1 can’t be two: it forms one square, not two.

If you think about the repercussions of the idea that 1*1=2, as long as you’re clear about meanings, it’s pretty obvious that 1*1=2 has a disastrously dramatic impact on math: it turns all of math into a pile of gibberish.

What’s 1*2? 2. 1*1=2 and 1*2=2, therefore 1=2. If 1=2, then 2=3, 3=4, 4=5: all integers are equal. If that’s true, then… well, numbers are, quite literally, meaningless. Which is quite a serious problem, unless you already believe that numbers are meaningless anyway.

In my last post, someone asked why I was so upset about the error in a math textbook. This is a good example of why. The new common core math curriculum, for all its flaws, does a better job of teaching understanding of math. But when the book teaches “facts” that are wrong, what they’re doing becomes the opposite. It doesn’t make sense – if you actually try to understand it, you just get more confused.

That teaches you one of two things. Either it teaches you that understanding this stuff is futile: that all you can do is just learn to blindly reproduce the procedures that you were taught, without understanding why. Or it teaches you that no one really understands any of it, and that therefore nothing that anyone tells you can possibly be trusted.

# Bad Math Books and Cantor Cardinality

A bunch of readers sent me a link to a tweet this morning from Professor Jordan Ellenberg:

The tweet links to the following image:

(And yes, this is real. You can see it in context here.)

This is absolutely infuriating.

This is a photo of a problem assignment in a math textbook published by an imprint of McGraw-Hill. And it’s absolutely, unquestionably, trivially wrong. No one who knew anything about math looked at this before it was published.

The basic concept underneath this is fundamental: it’s the cardinality of sets from Cantor’s set theory. It’s an extremely important concept. And it’s a concept that’s at the root of a huge amount of misunderstandings, confusion, and frustration among math students.

Cardinality, and the notion of cardinality relations between infinite sets, are difficult concepts, and they lead to some very un-intuitive results. Infinity isn’t one thing: there are different sizes of infinities. That’s a rough concept to grasp!

Here on this blog, I’ve spent more time dealing with people who believe that it must be wrong – a subject that I call Cantor crackpottery – than with any other bad math topic. This error teaches students something deeply wrong, and it encourages Cantor crackpottery!

Let’s review.

Cantor said that two collections of things are the same size if it’s possible to create a one-to-one mapping between the two. Imagine you’ve got a set of 3 apples and a set of 3 oranges. They’re the same size. We know that because they both have 3 elements; but we can also show it by setting aside pairs of one apple and one orange – you’ll get three pairs.

The same idea applies when you look at infinitely large sets. The set of positive integers and the set of negative integers are the same size. They’re both infinite – but we can show how you can create a one-to-one relation between them: you can take any positive integer $i$, and map it to exactly one negative integer, $0 - i$.

That leads to some unintuitive results. For example, the set of all natural numbers and the set of all even natural numbers are the same size. That seems crazy, because the set of all even natural numbers is a strict subset of the set of natural numbers: how can they be the same size?

But they are. We can map each natural number $i$ to exactly one even natural number $2i$. That’s a perfect one-to-one map between natural numbers and even natural numbers.

Where it gets uncomfortable for a lot of people is when we start thinking about real numbers. The set of real numbers is infinite. Even the set of real numbers between 0 and 1 is infinite! But it’s also larger than the set of natural numbers, which is also infinite. How can that be?

The answer is that Cantor showed that for any possible one-to-one mapping between the natural numbers and the real numbers between 0 and 1, there’s at least one real number that the mapping omitted. No matter how you do it, all of the natural numbers are mapped to one value in the reals, but there’s at least one real number which is not in the mapping!

In Cantor set theory, that means that the size of the set of real numbers between 0 and 1 is strictly larger than the set of all natural numbers. There’s an infinity bigger than infinity.

I think that this is what the math book in question meant to say: that there’s no possible mapping between the natural numbers and the real numbers. But it’s not what they did say: what they said is that there’s no possible map between the integers and the fractions. And that is not true.

Here’s how you generate the mapping between the integers and the rational numbers (fractions) between 0 and 1, written as a pseudo-Python program:

 i = 0
for denom in Natural:
for num in 1 .. denom:
if num is relatively prime with denom:
print("%d => %d/%d" % (i, num, denom))
i += 1


It produces a mapping (0 => 0, 1 => 1, 2 => 1/2, 3 => 1/3, 4 => 2/3, 5 => 1/4, 6 => 3/4, …). It’ll never finish running – but you can easily show that for any possible fraction, there’ll be exactly one integer that maps to it.

That means that the set of all rational numbers between 0 and 1 is the same size as the set of all natural numbers. There’s a similar way of producing a mapping between the set of all fractions and the set of natural numbers – so the set of all fractions is the same size as the set of natural numbers. But both are smaller than the set of all real numbers, because there are many, many real numbers that cannot be written as fractions. (For example, $\pi$. Or the square root of 2. Or $e$. )

This is terrible on multiple levels.

1. It’s a math textbook written and reviewed by people who don’t understand the basic math that they’re writing about.
2. It’s teaching children something incorrect about something that’s already likely to confuse them.
3. It’s teaching something incorrect about a topic that doesn’t need to be covered at all in the textbook. This is an algebra-2 textbook. You don’t need to cover Cantor’s infinite cardinalities in Algebra-2. It’s not wrong to cover it – but it’s not necessary. If the authors didn’t understand cardinality, they could have just left it out.
4. It’s obviously wrong. Plenty of bright students are going to come up with the the mapping between the fractions and the natural numbers. They’re going to come away believing that they’ve disproved Cantor.

I’m sure some people will argue with that last point. My evidence in support of it? I came up with a proof of that in high school. Fortunately, my math teacher was able to explain why it was wrong. (Thanks Mrs. Stevens!) Since I write this blog, people assume I’m a mathematician. I’m not. I’m just an engineer who really loves math. I was a good math student, but far from a great one. I’d guess that every medium-sized high school has at least one math student every year who’s better than I was.

The proof I came up with is absolutely trivial, and I’d expect tons of bright math-geek kids to come up with something like it. Here goes:

1. The set of fractions is a strict subset of the set of ordered pairs of natural numbers.
2. So: if there’s a one-to-one mapping between the set of ordered pairs and the naturals, then there must be a one-to-one mapping between the fractions and the naturals.
3. On a two-d grid, put the natural numbers across, and then down.
4. Zigzag diagonally through the grid, forming pairs of the horizontal position and the vertical position: (0,0), (1, 0), (0, 1), (2, 0), (1, 1), (0, 2), (3, 0), (2, 1), (1, 2), (0, 3).
5. This will produce every possible ordered pair of natural numbers. For each number in the list, produce a mapping between the position in the list, and the pair. So (0, 0) is 0, (2, 0) is 3, etc.

As a proof, it’s sloppy – but it’s correct. And plenty of high school students will come up with something like it. How many of them will walk away believing that they just disproved Cantor?

# Not a theory! Really! It’s not a theory!

I know I’ve been terrible about updating my blog lately. I’ve got some good excuses. (The usual: very busy with work. The less usual: new glasses that I’m having a very hard time adapting to. Getting old sucks. My eyes have deteriorated to the point where my near vision is shot, and the near-vision correction in my lenses needed to get jumped pretty significantly, which takes some serious getting used to.) And getting discussions of type theory right is a lot of work. Type theory in particular takes a lot of work, because it’s a subject that I really want to get right, because it’s so important in my profession, and because so few people have actually written about it in a way that’s accessible to non-mathematicians.

Anyway: rest assured that I’m not dropping the subject, and I hope to be getting back to writing more very soon. In the meantime, I decided to bring you some humorous bad math.

Outside the scientific community, one of the common criticisms of science is that scientific explanations are “just a theory”. You hear this all the time from ignorant religious folks trying to criticize evolution or the big bang (among numerous other things). When they say that something is just a theory, what they mean is that it’s not a fact, it’s just speculation. They don’t understand what the word “theory” really means: they think that a theory and a fact are the same class of things – that an idea starts as a theory, and becomes a fact if you can prove it.

In science, we draw a distinction between facts and theories, but it’s got nothing to do with how “true” something is. A fact is an observation of something that happens, but doesn’t say why it happens. The sun produces light. That’s a fact. The fact doesn’t say why that happens. It doesn’t have to say how it happens. But it does. That’s an observation of a fact. A theory is an explanation of a set of facts. The combined gravitational force of all of the particles in the sun compress the ones in the center until quantum tunnelling allows hydrogen atoms to combine and fuse producing energy, which eventually radiates as the heat and light that we observe. The theory of solar hydrogen fusion is much more than the words in the previous sentence: it’s an extensive collection of evidence and mathematics that explains the process in great detail. Solar hydrogen fusion – mathematical equations and all – is a theory that explains the heat and light that we observe. We’re pretty sure that it’s true – but the fact that it is true doesn’t mean that it’s not a theory.

Within the scientific community, we criticize crackpot ideas by saying that they’re not a theory. In science, a theory means a well-detailed and tested hypothesis that explains all of the known facts about something, and makes testable predictions. When we say that something isn’t a theory, we mean that it doesn’t have the supporting evidence, testability, or foundation in fact that would be needed to make something into a proper theory.

For example, intelligent design doesn’t qualify as a scientific theory. It basically says “there’s stuff in the world that couldn’t happen unless god did it”. But it never actually says how, precisely, to identify any of those things that couldn’t happen without god. Note that this doesn’t mean that it’s not true. I happen to believe that it’s not – but whether it’s true or not has nothing to do with whether, scientifically, in qualifies as a theory.

That’s a very long, almost Oracian introduction to today’s nonsense. This bit of crackpottery, known as “the Principle of Circlon Synchronicity”, written by one James Carter, has one really interesting property: I agree, 100%, with the very first thing that Mr. Carter says about his little idea.

The Principle of Circlon Synchronicity is not a Theory

They’re absolutely correct. It’s not a theory. It’s a bundle of vague assumptions, tied together by a shallow pretense at mathematics.

The “introduction” to this “principal” basically consists of the author blindly asserting that a bunch of things aren’t theories. For example, his explanation for why the principal of circlon synchronicity is not a theory begins with:

There are many different theories that have been used to explain the nature of reality. Today, the most popular of these are quantum mechanics, special relativity, string theories, general relativity and the Big Bang. Such theories all begin with unmeasured metaphysical assumptions such as fields and forces to explain the measurements of various phenomena. Circlon synchronicity is a purely mechanical system that explains local physical measurements. You only need a theory to explain physical measurements in terms of non-local fields, forces and dimensions.

This is a novel definition of “theory”. It has absolutely nothing to do with what the rest of us mean by the word “theory”. Basically, he thinks that his explanations, because they are allegedly simple, mechanical, and free of non-local effects, aren’t theories. They’re principals.

The list of things that don’t need a theory, according to Mr. Carter, is extensive.
For example:

The photon is not a theory. The photon is a mechanical measurement of mass. The photon is a conjoined matter-antimatter pair that is the basic form of mass and energy in the Living Universe. All photons move at exactly the speed of light relative to one another within the same absolute space. Photons are produced when a proton and electron are joined together to form a hydrogen atom. The emission of a photon is a mini annihilation with part of the electron and part of the proton being carried away by the photon. A photon with mass and size eliminates the need for both Planck’s constant and the Heisenberg uncertainty principle and also completely changes the meaning of the equation E=MC2. This is not a theory of a photon. It is the measurements describing the nature of the photon.

This is where we start on the bad math.

A photon is a quantum of light, or some other form of electromagnetic radiation. It doesn’t have any mass. But even if it didn’t: a photon isn’t a measurement. A photon is a particle (or a wave, depending on how you deal with it.) A measurement is a fundamentally different thing. If you want to do math that describes the physical universe, you’ve got to be damned careful about your units. If you’re measuring mass, you your units need to be mass units. If you’re describing mass, then the equations that derive your measurements of mass need to have mass units. If a photon is a measurement of mass, then what’s its unit?

Further, you can’t take an equation like $e=mc^2$, and rip it out of context, while asserting that it has exactly the same meaning that it did in its original context. Everyone has seen that old equation, but very few people really understand just what it means. Mr. Carter is not part of that group of people. To him, it’s just something he’s seen, which he knows is sciency, and so he grabs on to it and shouts about it in nonsensical ways.

But note, importantly, that even here, what Mr. Carter is doing isn’t science. He’s absolutely right when he says it’s not a theory. He asserts that the whole meaning of $e=mc^2$ changes because of his new understanding of what light is; but he doesn’t ever bother to explain just what that new understanding is, or how it differs from the old one.

He makes some hand-waves about how you don’t need the uncertainty principle. If his principles had a snowballs chance in hell of being correct, that might be true. The problem with that assertion is that the uncertainty principle isn’t just a theory. It’s a theory based on observations of facts that absolutely require explanations. There’s a great big fiery-looking ball up in the sky that couldn’t exist without uncertainty. Uncertainty isn’t just a pile of equations that someone dreamed up because it seemed like fun. It’s a pile of equations that were designed to try to explain the phenomena that we observe. There are a lot of observations that demonstrate the uncertainty principle. It doesn’t disappear just because Mr. Carter says it should. He needs to explain how his principles can account for the actual phenomena we observe – not just the phenomena that he wants to explain.

Similarly, he doesn’t like the theory of gravity.

We do not need a theory of gravity to explain exactly how it works. Gravity is a simple measurement that plainly shows exactly what gravity does. We use accelerometers to measure force and they exactly show that gravity is just an upwardly pointing force caused by the physical expansion of the Earth. The gravitational expansion of matter does not require a theory. It is just the physical measurement of gravity that shows exactly how it works in a completely mechanical way without any fields or non-local interactions. You only need a theory to explain a non-local and even infinite idea of how gravity works in such a way that it can’t be directly measured. Gravity not a theory.

Once again, we see that he really doesn’t understand what theory means. According to him, gravity can be measured, and therefore, it’s not a theory. Anything that can be measured, according to Mr. Carter, can’t be a theory: if it’s a fact, it can’t be a theory; even more, if it’s a fact, it doesn’t need to be explained at all. It’s sort-of like the fundamentalists idea of a theory, only slightly more broken.

This is where you can really see what’s wrong with his entire chain of reasoning. He asserts that gravity isn’t a theory – and then he moves in to an “explanation” of how gravity works which simply doesn’t fit.

We do not need a theory of gravity to explain exactly how it works. Gravity is a simple measurement that plainly shows exactly what gravity does. We use accelerometers to measure force and they exactly show that gravity is just an upwardly pointing force caused by the physical expansion of the Earth. The gravitational expansion of matter does not require a theory. It is just the physical measurement of gravity that shows exactly how it works in a completely mechanical way without any fields or non-local interactions. You only need a theory to explain a non-local and even infinite idea of how gravity works in such a way that it can’t be directly measured. Gravity not a theory.

The parade of redefinitions marches on! “Exactly” now means “hand-wavy”.

We’re finally getting to the meat of Mr. Carter’s principle. He’s a proponent of the same kind of expanding earth rubbish as Neal Adams. Gravity has nothing to do with non-local forces. It’s all just the earth expanding under us. Of course, this is left nice and vague: he mocks the math behind the actual theory of gravity, but he can’t actually show that his principal works. He just asserts that he’s defined exactly how it works by waving his hands really fast.

I can disprove his principle of gravity quite easily, by taking my phone out of my pocket, and opening Google maps.

In 5 seconds flat (which is longer than it should take!), Google maps shows me my exact position on the map. It does that by talking to a collection of satellites that are revolving around the earth. The positions of those satellites are known with great accuracy. They circle the earth without the use of any sort of propellant. If Mr. Carter (or Mr. Adams, who has a roughly equivalent model) were correct – if gravity was not, in fact, a force attracting mass to other masses, but instead was an artifact of an expanding earth – then the “satellites” that my phone receives data from would not be following an elliptical path around the earth. They’d be shooting off into the distance, moving in a perfectly straight line. But they don’t move in a straight line. They continue to arc around the earth, circling around and around, without any propulsion.

In any reasonable interpretation of the expanding earth? That doesn’t make sense. There’s no way for them to orbit. Satellites simply can’t work according to his theory. And yet, they do.

Of course, I’m sure that Mr. Carter has some hand-wavy explanation of just why satellites work. The problem is, whatever explanation he has isn’t a theory. He can’t actually make predictions about how things will behave, because his principles aren’t predictive.

In fact, he even admits this. His whole screed turns out to be a long-winded advertisement for a book that he’ll happily sell you. As part of the FAQ for his book, he explains why (a) he can’t do the math, and (b) it doesn’t matter anyway:

The idea that ultimate truth can be represented with simple mathematical equations is probably totally false. A simple example of this is the familiar series of circular waves that move away from the point where a pebble is dropped into a quiet pool of water. While these waves can be described in a general way with a simple set of mathematical equations, any true and precise mathematical description of this event would have to include the individual motion of each molecule within this body of water. Such an equation would require more than the world’s supply of paper to print and its complexity would make it virtually meaningless.

The idea of the circlon is easy to describe and illustrate. However, any kind of mathematical description of its complex internal dynamics is presently beyond my abilities. This deficiency does not mean that circlon theory cannot compete with the mathematically simplistic point-particle and field theories of matter. It simply means that perhaps ultimate truth is not as easily accessible to a mathematical format as was once hoped.

It’s particularly interesting to consider this “explanation” in light of some recent experiments in computational fluid dynamics. Weather prediction has become dramatically better in the last few years. When my father was a child, the only way to predict when a hurricane would reach land was to have people watching the horizon. No one could make accurate weather predictions at all, not even for something as huge as a storm system spanning hundreds of miles! When I was a child, weathermen rarely attempted to predict more than 2 days in advance. Nowadays, we’ve got 7-day forecasts that are accurate more often than the 2-day forecasts were a couple of decades ago. Why is that?

The answer is something called the Navier Stokes equations. The Navier-Stokes equations are a set of equations that describe how fluids behave. We don’t have the computational power or measurement abilities to compute N-S equations to the level of single molecules – but in principle, we absolutely could. The N-S equations – which demonstrably work remarkably well even when you’re just computing approximations – also describe exactly the phenomenon that Mr. Carter asserts can’t be represented with mathematical equations.

The problem is: he doesn’t understand how math or science work. He has no clue of how equations describe physical phenomena in actual scientific theories. The whole point of math is that it gives you a simple but precise way of describing complex phenomena. A wave in a pool of water involves the motion of an almost unimaginable number of particles, with a variety of forces and interactions between those particles. But all of them can be defined by reasonably simple equations.

Mr. Carter’s explanations are, intuitively, more attractive. If you really want to understand relativity, you’re going to need to spend years studying math and physics to get to the point where its equations make sense to you. But once you do, they don’t just explain things in a vague, hand-wavy way – they tell you exactly how things work. They make specific, powerful, precise predictions about how things will behave in a range of situations that match reality to the absolute limits of our ability to measure. Mr. Carter’s explanations don’t require years of study; they don’t require to study esoteric disciplines like group theory or tensor theory. But they also can’t tell you much of anything. Relativity can tell you exactly what adjustment you need to make to a satellite’s clock in order to make precise measurements of the location of a radio receiver on the ground. Mr. Carter’s explanations can’t even tell you how the satellite got there.

# Arabic numerals have nothing to do with angle counting!

There’s an image going around that purports to explain the origin of the arabic numerals. It’s cute. It claims to show why the numerals that we use look the way that they do. Here it is:

According to this, the shapes of the numbers was derived from a notation where for each numeral contains its own number of angles. It’s a really interesting idea, and it would be really interesting if it were true. The problem is, it isn’t.

Look at the numerals in that figure. Just by looking at them, you can see quite a number of problems with them.

For a couple of obvious examples:

• Look at the 7. The crossed seven is a recent invention made up to compensate for the fact that in cursive roman lettering, it can be difficult to distinguish ones from sevens, the mark was added to clarify. The serifed foot on the 7 is even worse: there’s absolutely no tradition of writing a serifed foot on the 7; it’s just a font decoration. The 7’s serifed foot is no more a part of the number than serifed foot on the lowercase letter l is an basic feature of the letter ls.
• Worse is the curlique on the 9: the only time that curly figures like that appear in writing is in calligraphic documents, where they’re an aesthetic flourish. That curly thing has never been a part of the number 9. But if you want to claim this angle-counting nonsense, you’ve got to add angles to a 9 somewhere. It’s not enough to just add a serifed foot – that won’t get you enough angles. So you need the curlique, no matter how obviously ridiculous it is.

You don’t even need to notice stuff like that to see that this is rubbish. We actually know quite a lot about the history of arabic numeral notation. We know what the “original” arabic numerals looked like. For example, this wikipedia image shows the standard arabic numerals (this variant is properly called the Bakshali numerals) from around the second century BC:

It’s quite fascinating to study the origins of our numeric notation. It’s true that we – “we” meaning the scholarly tradition that grew out of Europe – learned the basic numeric notation from the Arabs. But they didn’t invent it – it predates them by a fair bit. The notation originally came from India, where Hindu scholars, who wrote in an alphabet derived from Sanskrit, used a sanskrit-based numeric notation called Brahmi numerals (which, in turn, were derived from an earlier notation, Karosthi numerals, which weren’t used quite like the modern numbers, so the Brahmi numerals are considered the earliest “true” arabic numeral.) That notation moved westward, and was adopted by the Persians, who spread it to the Arabs. As the arabs adopted it, they changed the shapes to work with their calligraphic notations, producing the Bakshali form.

In the Brahmi numerals, the numbers 1 through 4 are written in counting-based forms: one is written as one horizontal line; 2 as two lines; 3 as three lines. Four is written as a pair of crossed lines, giving four quadrants. 5 through 9 are written using sanskrit characters: their “original” form had nothing to do with counting angles or lines.

The real history of numerical notations is really interesting. It crosses through many different cultures, and the notations reform each time it migrates, keeping the same essential semantics, but making dramatic changes in the written forms of individual numerals. It’s so much more interesting – and the actual numeral forms are so much more beautiful – than you’d ever suspect from the nonsense of angle-counting.

# Silly φ and π crackpottery

Over time, I’ve come to really, really hate the number φ.

φ is the so-called golden ratio. It’s the number that is a solution for the equation (a+b)/a = (a/b). The reason that that’s interesting at all is because it’s got an interesting property when you draw it out: if you take a rectangle where the ratio of the length of the sides is 1:φ, then if you remove the largest possible square from it, you’ll get another rectangle whose sides have the ratio φ:1. If you take the largest square from that, you’ll get a rectangle whose sides have the ratio 1:φ. And so on.

The numeric value of it is (1+sqrt(5))/2, or about 1.618033988749895.

The problem with φ is that people are convinced that it’s some kind of incredibly profound thing, and find it all over the place. The problem is, virtually all of the places where people claim to find it are total rubbish. A number that’s just a tiny bit more that 1 1/2 is really easy to find if you go looking for it, and people go looking for it all over the place.

People claim it’s in all sorts of artwork. You can certainly find a ton of things in paintings whose size ratio is about 1 1/2, and people find it and insist that it was deliberately done to make it φ. People find it in musical scales, the diatonic and pentatonic scales, and the indian scales.

People claim it comes up all over the place in nature: in beehives, ant colonies, flowers, tree sizes, tree-limb positions, size of herds of animals, litters of young, body shapes, face shapes.

People claim it’s key to architecture.

And yet… it seems like if you actually take any of those and actually start to look at it in detail? The φ isn’t there. It’s just a number that’s kinda-sorta in the 1 1/2 range.

One example of that: there’s a common claim that human faces have proportions based on &phi. You can see a bunch of that nonsense here. The thing is, the “evidence” for the claim consists of rectangles drawn around photographs of faces – and if you look closely at those rectangles, what you find is that the placement of the corners isn’t consistent. When you define, say, “the distance between the eyes”, you can measure that as distances between inner-edges, or between pupils, or between outer edges. Most of these claims use outer edges. But where’s the outer edge of an eye? It’s not actually a well-defined point. You can pick a couple of different places in a photo as “the” edge. They’re all close together, so there’s not a huge amount of variation. But if you can fudge the width a little bit, and you can fudge other facial measurements just a little bit, you’ve got enough variation that if you’re looking for two measurements with a ratio close to φ, you’ll always find one.

Most of the φ nonsense is ultimately aesthetic: people claiming that the golden ratio has a fundamental beauty to it. They claim that facial features match it because it’s intrinsically beautiful, and so people whose faces have φ ratios are more beautiful, and that that led to sexual-selection which caused our faces to embody the ratio. I think that’s bunk, but it’s hard to make a mathematical argument against aesthetics.

But then, you get the real crackpots. There are people who think φ has amazing scientific properties. In the words of the crank I’m writing about today, understanding φ (and the “correct” value of π derived from it) will lead humanity to “enter into a veritable Space Age”.

I’m talking about a guy who calls himself “Jain 108”. I’m not quite sure what to call him. Mr. Jain? Mr. 108? Dr 108? Most of the time on his website, he just refers to himself as “Jain” (or sometimes “Jain of Oz”) so I’ll go with “Jain”).

Jain believes that φ is the key to mathematics, science, art, and human enlightenment. He’s a bit hard to pin down, because most of his website is an advertisement for his books and seminars: if you want to know “the truth”, you’ve got to throw Jain some cash. I’m not willing to give money to crackpots, so I’m stuck with just looking at what he’s willing to share for free. (But I do recommend browsing around his site. It’s an impressive combination of newage scammery, pomposity, and cluelessness.)

What you can read for free is more than enough to conclude that he’s a total idiot.

I’m going to focus my mockery on one page: “Is Pi a Lie?”.

On this page, Jain claims to be able to prove that the well-known value of π (3.14159265….) is wrong. In fact, that value is wrong, and the correct value of π is derived from φ! The correct value of π is $\frac{4}{\sqrt{\phi}}$, or about 3.144605511029693.

For reasons that will be soon explained, traditional Pi is deficient because historically it has awkwardly used logical straight lines to measure illogical curvature. Thus, by using the highest level of mathematics known as Intuitive Maths, the True Value of Pi must be a bit more than anticipated to compensate for the mysterious “Area Under The Curve”. When this is done, the value, currently known as JainPi, = 3.144… can be derived, by knowing the precise Height of the Cheops Pyramid which is based on the Divine Phi Proportion (1.618…). Instead of setting our diameter at 1 unit or 1 square, something magical happens when we set the diameter at the diagonal length of a Double Square = 2.236… which is the Square Root of 5 (meaning 2.236… x 2.236… = 5). This is the critical part of the formula that derives Phi $\frac{1+\sqrt{5}}{2}$, and was used by ancient vedic seers as their starting point to construct their most important diagram or ‘Yantra’ or power-art called the Sri Yantra. With a Root 5 diameter, the translation of the Phi’s formula into a geometric construct derives the royal Maltese Cross symbol, concluding that Phi is Pi, that Phi generates Pi, and that Pi must be derived with a knowledge of the Harmonics of Phi. When this is understood and utilized, we will collectively enter into a veritable Space Age.

How did we get the wrong value? It’s based on the “fact” that the computation of π is based on the use of “logical” straight lines to measure “illogical” curvurature. (From just that one sentence, we can already conclude that Jain knows nothing about logic, except what he learned from Mr. Spock on Star Trek.) More precisely, according to Jain:

In all due good respects, we must first honour Archimedes of Syracuse 2,225 years ago, who gave the world his system on how to calculate Pi, approximated to 22÷7, by cutting the circle into say 16 slices of a pizza, and measuring the 16 edge lengths of these 16 triangular polygons (fig 3), to get a good estimate for the circumference of a circle. The idea was that if we kept making the slices of pizza smaller and smaller, by subsequently cutting the circle into 32 slices, then 64, then 128 then 256 slices, we would get a better and more accurate representation for the circumference. The Fundamental Flawed Logic or Error with Archimede’s Increasing Polygon Method was that he failed to measure The Area Under The Curve. In fact, he assumed that The Area Under The Curve, just magically disappeared. Even in his time, Archimedes admitted that his value was a mere estimate!

This explanation does a beautiful job of demonstrating how utterly ignorant Jain is of math. Archimedes may have been the first person from the western tradition to have worked out a mechanism to compute a value for π – and his mechanism was a good one. But it’s far from the only one. But let’s ignore that for a moment. Jain’s supposed critique, if true, would mean that modern calculus doesn’t work. The wedge-based computation of π is a forerunner of the common methods of calculus. In reality, when we compute the value of almost any integral using calculus, our methods are based on the concept of drawing rectangles under the curve, and narrowing those rectangles until they’re infinitely small, at which point the “area under the curve” missed by the rectangles becomes zero. If the wedge computation of π is wrong because it misses are under the curve, then so will every computation using integral calculus.

Gosh, think we would have noticed that by now?

Let’s skip past that for a moment, and come back to the many ways that π comes into reality. π is the ratio of the diameter of a circle to its radius. Because circles are such a basic thing, there are many ways of deriving the value of π that come from its fundamental nature. Many of these have no relation to the wedge-method that Jain attributes to Archimedes.

For example, there is Viete’s product:

$\frac{2}{\pi} = \left(\frac{\sqrt{2}}{2}\right)\left(\frac{\sqrt{2 + \sqrt{2}}}{2}\right)\left(\frac{\sqrt{2 + \sqrt{2 + \sqrt{2}}}}{2}\right)(...)$

Or there’s the Gregory-Leibniz series:

$\frac{\pi}{4} = 1 - \frac{1}{3} + \frac{1}{5} - \frac{1}{7} + \frac{1}{9} - ...$

These have no relation to the wedge-method – they’re derived from the fundamental nature of π. And all of them produce the same value – and it’s got no connection at all to φ.

As supportive evidence for the incorrectness of π, Jain gives to apocryphal stories about NASA and the moon landings. First, he claims that the first moon landing was off by 20 kilometers, and that the cause of this was an incorrect value of π: that the value of π used in computing trajectories was off by 0.003:

NASA admitted that when the original Mooncraft landing occurred, the targeted spot was missed by about 20km?
What could have been wrong with the Calculations?
NASA subsequently adjusted their traditional mathematical value for Pi (3.141592…) by increasing it in the 3rd decimal by .003!

Let’s take just a moment, and consider that.

It’s a bit difficult to figure out how to address that, because he’s not mentioning what part of the trajectory was messed up. Was it the earth-to-moon transit of the full apollo system? Or was it the orbit-to-ground flight of the lunar lander? Since he doesn’t bother to tell us, we’ll look at both.

π does matter when computing the trajectory of the earth-to-moon trip – because it involves the intersection of two approximate circles – the orbit of the earth around the sun, and the orbit of the moon around the earth. (Both of these are approximations, but they’re quite useful ones; the apollo trajectory computations did rely on a value for π.

Let’s look at earth-to-moon. I’m going to oversimplify ridiculously – but I’m just trying to give us a ballpark order-of-magnitude guess as just how much of a difference Mr. Jain’s supposed error would cause. THe distance from the earth to the moon is about 384,000 kilometers. If we assume that π is a linear factor in the computation, then a difference in the value of pi of around 1 part in 1000 would cause a difference in distance computations of around 384 kilometers. Mr. Jain is alleging that the error only caused a difference of 20 kilometers. He’s off by a factor of 15. We can hand-wave this away, and say that the error that caused the lander to land in the “wrong” place wasn’t in the earth-moon trajectory computation – but we’re still talking about the apollo unit being in the wrong place by hundreds of kilometers – and no one noticing.

What if the problem was in the computation of the trajectory the lander took from the capsule to the surface of the moon? The orbit was a nearly circular one at about 110 kilometers above the lunar surface. How much of an error would the alleged π difference cause? About 0.1 kilometer – that is, about 100 meters. Less than what Jain claims by a factor of 200.

The numbers don’t work. These aren’t precise calculations by any stretch, but they’re ballpark. Without Jain providing more information about the alleged error, they’re the best we can do, and they don’t make sense.

Jain claims that in space work, scientists now use an adjusted value of π to cover the error. This piece I can refute by direct knowledge. My father was a physicist who worked on missiles, satellites, and space probes. (He was part of the Galileo team.) They used good old standard 3.14159 π. In fact, he explained how the value of π actually didn’t need to be that precise. In satellite work, you’re stuck with the measurement problems of reality. In even the highest precision satellite work, they didn’t use more that 4 significant digits of precision, because the manufacturing and measurement of components was only precise to that scale. Beyond that, it was always a matter of measure and adjust. Knowing that π was 3.14159265356979323 was irrelevant in practice, because anything beyond “about 3.1416” was smaller that the errors in measurement.

Mr. Jain’s next claim is far worse.

Also, an ex-Engineer from NASA, “Smokey” admitted (via email) that when he was making metal cylinders for this same Mooncraft, finished parts just did not fit perfectly, so an adjusted value for Pi was also implemented. At the time, he thought nothing about it, but after reading an internet article called The True Value of Pi, by Jain 108, he made contact.

This is very, very simple to refute by direct experience. This morning, I got up, shaved with an electric razor (3 metal rotors), made myself iced coffee using a moka pot (three round parts, tight fitted, with circular-spiral threading). After breakfast, I packed my backpack and got in my car to drive to the train. (4 metal cylinders with 4 precisely-fitted pistons in the engine, running on four wheels with metal rims, precisely fitted to circular tires, and brakes clamping on circular disks.) I drove to the train station, and got on an electric train (around 200 electric motors on the full train, with circular turbines, driving circular wheels).

All those circles. According to Jain, every one of those circles isn’t the size we think it is. And yet they all fit together perfectly. According to Jain, every one of those circular parts is larger that we think it should be. To focus on one thing, every car engine’s pistons – every one of the millions of pistons created every year by companies around the world – requires more metal to produce than we’d expect. And somehow, in all that time, no one has ever noticed. Or if they’ve noticed, every single person who ever noticed it has never mentioned it!

It’s ludicrous.

Jain also claims that the value of e is wrong, and comes up with a cranky new formula for computing it. Of course, the problem with e is the same as the problem wiht π: in Jain’s world, it’s really based on φ.

In Jain’s world, everything is based on φ. And there’s a huge, elaborate conspiracy to keep it secret. Any Jain will share the secret with you, showing you how everything you think you know is wrong. You just need to buy his books ($77 for a hard-copy, or$44 for an ebook.) Or you could pay for him to travel to you and give you a seminar. But he doesn’t list a price for that – you need to send him mail to inquire.

# The Bad Logic of Good People Can’t be Sexists

One of my constant off-topic rants around here is about racism and sexism. This is going to be a nice little post that straddles the line. It’s one of those off-topic-ish rants about sexism in our society, but it’s built around a core of bad logic – so there is a tiny little bit of on-topicness.

We live in a culture that embodies a basic conflict. On one hand, racism and sexism are a deeply integrated part of our worldview. But on the other wand, we’ve come to believe that racism and sexism are bad. This puts us into an awkward situation. We don’t want to admit to saying or doing racist things. But there’s so much of it embedded in every facet of our society that it takes a lot of effort and awareness to even begin to avoid saying and doing racist things.

The problem there is that we can’t stop being racist/sexist until we admit that we are. We can’t stop doing sexist and racist things until we admit that we do sexist and racist things.

And here’s where we hit the logic piece. The problem is easiest to explain by looking at it in formal logical terms. We’ll look at it from the viewpoint of sexism, but the same argument applies for racism.

1. We’ll say $\text{Sexist}(x)$ to mean that “x” is sexist.
2. We’ll say $\text{Bad}(x)$ to mean that x is bad, and $\text{Good}(x)$ to mean that x is good.
3. We’ll have an axiom that bad and good are logical opposites: $\text{Bad}(x) \Leftrightarrow \lnot \text{Good}(x)$.
4. We’ll have another axiom that sexism is bad: $\forall x: \text{Sexist}(x) \Rightarrow \text{Bad}(x)$.
5. We’ll say $\text{Does}(p, x)$ means that person $p$ does an action $x$.

The key statement that I want to get to is: We believe that people who do bad things are bad people: $\forall p, x: \text{Does}(p, x) \land \text{Bad}(x) \Rightarrow \text{Bad}(p)$.

That means that if you do something sexist, you are a bad person:

• $s$ is a sexist action: $\text{Sexist}(s)$.
• I do something sexist: $\text{Does}(\textbf{markcc}, s)$.
• By rule 5 above, that means that I am sexist.
• If I am sexist, then by rule 4 above, I am bad.

We know that we aren’t bad people: I’m a good person, right? So we reject that conclusion. I’m not bad; therefore, I can’t be sexist, therefore whatever I did couldn’t have been sexist.

This looks shallow and silly on the surface. Surely mature adults, mature educated adults couldn’t be quite that foolish!

If his crime was to use the phrase “boys with toys”, and that is your threshold for sexism worthy of some of the abusive responses above, then ok – stop reading now.

My problem is that I have known Shri for many years, and I don’t believe that he’s even remotely sexist. But in 2015 can one defend someone who’s been labeled sexist without a social media storm?

Are people open to the possibility that actually Kulkarni might be very honourable in his dealings with women?

In an interview a week or so ago, Professor Shri Kulkarni said something stupid and sexist. The author of that piece believes that Professor Kulkarni couldn’t have said something sexist, because he knows him, and he knows that he’s not sexist, because he’s a good guy who treats women well.

The thing is, that doesn’t matter. He messed up, and said something sexist. It’s not a big deal; we all do stupid things from time to time. He’s not a bad person because he said something sexist. He just messed up. People are, correctly, pointing out that he messed up: you can’t fix a problem until you acknowledge that it exists! When you say something stupid, you should expect to get called on it, and when you do, you should accept it, apologize, and move on with your life, using that experience to improve yourself, and not make the mistake again.

The thing about racism and sexism is that we’re immersed in it, day in and day out. It’s part of the background of our lives – it’s inescapable. Living in that society means means that we’ve all absorbed a lot of racism and exism without meaning to. We don’t have to like that, but it’s true. In order to make things better, we need to first acklowledge the reality of the world that we live in, and the influence that it has on us.

In mathematical terms, the problem is that good and bad, sexist and not sexist, are absolutes. When we render them into pure two-valued logic, we’re taking shades of gray, and turning them into black and white.

There are people who are profoundly sexist or racist, and that makes them bad people. Just look at the MRAs involved in Gamergate: they’re utterly disgusting human beings, and the thing that makes them so despicably awful is the awfulness of their sexism. Look at a KKKer, and you find a terrible person, and the thing that makes them so terrible is their racism.

But most people aren’t that extreme. We’ve just absorbed a whole lot of racism and sexism from the world we’ve lived our lives in, and that influences us. We’re a little bit racist, and that makes us a little bit bad – we have room for improvement. But we’re still, mostly, good people. The two-valued logic creates an apparent conflict where none really exists.

Where do these sexist/racist attitudes come from? Picture a scientist. What do you see in your minds eye? It’s almost certainly a white guy. It is for me. Why is that?

1. In school, from the time that I got into a grade where we had a dedicated science teacher, every science teacher that I had was a white guy. I can still name ’em: Mr. Fidele, Mr. Schwartz, Mr. Remoli, Mr. Laurie, Dr. Braun, Mr. Hicken, etc.
2. On into college, in my undergrad days, where I took a ton of physics and chemistry (I started out as an EE major), every science professor that I had was a white guy.
3. My brother and I used to watch a ton of trashy sci-fi movie some free movie apps from the internet. In those movies, every time there was a character who was a scientist, he was a white guy.
4. My father was a physicist working in semiconductor manufacturing for satellites and military applications. From the time I was a little kid until they day he retired, he had exactly one coworker who wasn’t a white man. (And everyone on his team complained bitterly that the black guy wasn’t any good, that he only got and kept the job because he was black, and if they tried to fire him, he’d sue them. I really don’t believe that my dad was a terrible racist person; I think he was a wonderful guy, the person who is a role model for so much of my life. But looking back at this? He didn’t mean to be racist, but I think that he was.)

In short, in all of my exposure to science, from kindergarten to graduate school, scientists were white men. (For some reason, I encountered a lot of women in math and comp sci, but not in the traditional sciences.) So when I picture a scientist, it’s just natural that I picture a man. There’s a similar story for most of us who’ve grown up in the current American culture.

When you consider that, it’s both an explanation of why we’ve got such a deeply embedded sexist sense about who can be a scientist, and an explanation how, despite the fact that we’re not deliberately being sexist, our subconscious sexism has a real impact.

I’ve told this story a thousand times, but during the time I worked at IBM, I ran the intership program for my department one summer. We had a deparmental quota of how many interns each department could pay for. But we had a second program that paid for interns that were women or minority – so they didn’t count against the quota. The first choice intern candidate of everyone in the department was a guy. When we ran out of slots, the guy across the hall from me ranted and raved about how unfair it was. We were discriminating against male candidates! It was reverse sexism! On and on. But the budget was what the budget was. Two days later, he showed up with a resume for a young woman, all excited – he’d found a candidate who was a woman, and she was even better than the guy he’d originally wanted to hire. We hired her, and she was brilliant, and did a great job that summer.

The question that I asked my office-neighbor afterwards was: Why didn’t he find the woman the first time through the resumes? He went through the resumes of all of the candidates before picking the original guy. The woman that he eventually hired had a resume that was clearly better than the guy. Why’d he pass her resume to settle on the guy? He didn’t know.

That little story demonstrates two things. One, it demonstrates the kind of subconscious bias we have. We don’t have to be mustache-twirling black-hatted villains to be sexists or racists. We just have to be human. Two, it demonstrates the way that these low-level biases actually harm people. Without our secondary budget for women/minority hires, that brilliant young woman would never have gotten an internship at IBM; without that internship, she probably wouldn’t have gotten a permanent job at IBM after graduation.

Professor Kulkarni said something silly. He knew he was saying something he shouldn’t have, but he went ahead and did it anyway, because it was normal and funny and harmless.

It’s not harmless. It reinforces that constant flood of experience that says that all scientists are men. If we want to change the culture of science to get rid of the sexism, we have to start with changing the deep attitudes that we aren’t even really aware of, but that influence our thoughts and decisions. That means that when someone says we did something sexist or racist, we need to be called on it. And when we get called on it, we need to admit that we did something wrong, apologize, and try not to make the same mistake again.

We can’t let the black and white reasoning blind us. Good people can be sexists or racists. Good people can do bad things without meaning to. We can’t allow our belief in our essential goodness prevent us from recognizing it when we do wrong, and making the choices that will allow us to become better people.

When a friend asks me to write about something, I try do it. Yesterday, a friend of mine from my Google days, Daniel Martin, sent me a link, and asked to write about it. Daniel isn’t just a former coworker of mine, but he’s a math geek with the same sort of warped sense of humor as me. He knew my blog before we worked at Google, and on my first Halloween at Google, he came to introduce himself to me. He was wearing a purple shirt with his train ticket on a cord around his neck. For those who know any abstract algebra, get ready to groan: he was purple, and he commuted. He was dressed as an Abelian grape.

The real subject of the article involves a recent twitter-storm around a professor at Boston University. This professor tweeted some about racism and history, and she did it in very blunt, not-entirely-professional terms. The details of what she did isn’t something I want to discuss here. (Briefly, I think it wasn’t a smart thing to tweet like that, but plenty of white people get away with worse every day; the only reason that she’s getting as much grief as she is is because she dared to be a black woman saying bad things about white people, and the assholes at Breitbart used that to fuel the insatiable anger and hatred of their followers.)

But I don’t want to go into the details of that here. Lots of people have written interesting things about it, from all sides. Just by posting about this, I’m probably opening myself up to yet another wave of abuse, but I’d prefer to avoid and much of that as I can. Instead, I’m just going to rip out the introduction to this article, because it makes a kind of incredibly stupid mathematical argument that requires correction. Here are the first and second paragraphs:

There aren’t too many African Americans in higher education.

In fact, black folks only make up about 4 percent of all full time tenured college faculty in America. To put that in context, only 14 out of the 321—that’s about 4 percent—of U.S. astronauts have been African American. So in America, if you’re black, you’ve got about as good a chance of being shot into space as you do getting a job as a college professor.

Statistics and probability can be a difficult field of study. But… a lot of its everyday uses are really quite easy. If you’re going to open your mouth and make public statements involving probabilities, you probably should make sure that you at least understand the first chapter of “probability for dummies”.

This author doesn’t appear to have done that.

The most basic fact of understanding how to compare pretty much anything numeric in the real world is that you can only compare quantities that have the same units. You can’t compare 4 kilograms to 5 pounds, and conclude that 5 pounds is bigger than 4 kilograms because 5 is bigger than four.

That principle applies to probabilities and statistics: you need to make sure that you’re comparing apples to apples. If you compare an apple to a grapefruit, you’re not going to get a meaningful result.

The proportion of astronauts who are black is 14/321, or a bit over 4%. That means that out of every 100 astronauts, you’d expect to find four black ones.

The proportion of college professors who are black is also a bit over 4%. That means that out of every 100 randomly selected college professors, you’d expect 4 to be black.

So far, so good.

But from there, our intrepid author takes a leap, and says “if you’re black, you’ve got about as good a chance of being shot into space as you do getting a job as a college professor”.

Nothing in the quoted statistic in any way tells us anything about anyone’s chances to become an astronaut. Nothing at all.

This is a classic statistical error which is very easy to avoid. It’s a unit error: he’s comparing two things with different units. The short version of the problem is: he’s comparing black/astronaut with astronaut/black.

You can’t derive anything about the probability of a black person becoming an astronaut from the ratio of black astronauts to astronauts.

Let’s pull out some numbers to demonstrate the problem. These are completely made up, to make the calculations easy – I’m not using real data here.

Suppose that:

• the US population is 300,000,000;
• black people are 40% of the population, which means that there are are 120,000,000 black people.
• there are 1000 universities in America, and there are 50 faculty per university, so there are 50,000 university professors.
• there are 50 astronauts in the US.
• If 4% of astronauts and 4% of college professors are black, that means that there are 2,000 black college professors, and 2 black astronauts.

In this scenario, as in reality, the percentage of black college professors and the percentage of black astronauts are equal. What about the probability of a given black person being a professor or an astronaut?

The probability of a black person being a professor is 2,000/120,000,000 – or 1 in 60,000. The probability of a black person becoming an astronaut is just 2/120,000,000 – or 1 in 60 million. Even though the probability of a random astronaut being black is the same as a the probability of a random college professor being black, the probability of a given black person becoming a college professor is 10,000 times higher that the probability of a given black person becoming an astronaut.

This kind of thing isn’t rocket science. My 11 year old son has done enough statistics in school to understand this problem! It’s simple: you need to compare like to like. If you can’t understand that, if you can’t understand your statistics enough to understand their units, you should probably try to avoid making public statements about statistics. Otherwise, you’ll wind up doing something stupid, and make yourself look like an idiot.

(In the interests of disclosure: an earlier version of this post used the comparison of apples to watermelons. But given the racial issues discussed in the post, that had unfortunate unintended connotations. When someone pointed that out to me, I changed it. To anyone who was offended: I am sorry. I did not intend to say anything associated with the racist slurs; I simply never thought of it. I should have, and I shouldn’t have needed someone to point it out to me. I’ll try to be more careful in the future.)

# A failed attempt to prove P == NP

In computer science, we have one really gigantic open question about complexity. In the lingo, we ask “Does P == NP?”. (I’ll explain what that means below.) On March 9th, a guy named Michael LaPlante posted a paper to ArXiv that purports to prove, once and for all, that P == NP. If this were the case, if Mr. LaPlante (I’m assuming Mr.; if someone knows differently, ie. that it should be Doctor, or Miss, please let me know!) had in fact proved that P==NP, it would be one of the most amazing events in computer science history. And it wouldn’t only be a theoretical triumph – it would have real, significant practical results! I can’t think of any mathematical proof that would be more exciting to me: I really, really wish that this would happen. But Mr. LaPlante’s proof is, sadly, wrong. Trivially wrong, in fact.

In order to understand what all of this means, why it matters, and where he went wrong, we need to take a step back, and briefly look at computational complexity, what P and NP mean, and what are the implications of P == NP? (Some parts of the discussion that follows are re-edited versions of sections of a very old post from 2007.)

Before we can get to the meat of this, which is talking about P versus NP, we need to talk about computational complexity. P and NP are complexity classes of problems – that is, groups of problems that have similar bounds on their performance.

When we look at a computation, one of the things we want to know is: “How long will this take?”. A specific concrete answer to that depends on all sorts of factors – the speed of your computer, the particular programming language you use to run the program, etc. But independent of those, there’s a basic factor that describes something important about how long a computation will take – the algorithm itself fundamental requires some minimum number of operations. Computational complexity is an abstract method of describing how many operations a computation will take, expressed in terms of the size or magnitude of the input.

For example: let’s take a look at insertion sort. Here’s some pseudocode for insertion sort.

def insertion_sort(lst):
result = []
for i in lst:
for j in result:
if i < j:
insert i into result before j
if i wasn't inserted, add it to the end of result
return result


This is, perhaps, the simplest sorting algorithm to understand - most of us figured it out on our own in school, when we had an assignment to alphebetize a list of words. You take the elements of the list to be sorted one at a time; then you figure out where in the list they belong, and insert them.

In the worst possible case, how long does this take?

1. Inserting the first element requires 0 comparisons: just stick it into the list.
2. Inserting the second element takes exactly one comparison: it needs to be compared to the one element in the result list, to determine whether it goes before or after it.
3. Inserting the third element could take either one or two comparisons. (If it's smaller than the first element of the result list, then it can be inserted in front without any more comparisons; otherwise, it needs to be compared against the second element of the result list. So in the worst case, it takes 2 comparisons.
4. In general, for the Nth element of the list, it will take at most n-1 comparisons.

So, in the worst case, it's going to take 0 + 1 + 2 + ... + n-1 comparisons to produce a sorted list of N elements. There's a nice shorthand for computing that series: $\frac{(n-1)(n-2)}{2}$, which simplifies to \frac{n^2 -3n + 2}{2}, which is O(n2).

So while we can't say "computing a list of 100 elements will take 2.3 seconds" (because that depends on a ton of factors - the specific implementation of the code, the programming language, the machine it's running on, etc.), we can say that the time it takes to run increase roughly proportionally to the square of the size of the input - which is what it means when we say that insertion sort is O(n2).

That's the complexity of the insert sort algorithm. When we talk about complexity, we can talk about two different kinds of complexity: the complexity of an algorithm, and the complexity of a problem. The complexity of an algorithm is a measure of how many steps the algorithm takes to execute on an input of a particular size. It's specific to the algorithm, that is, the specific method used to solve the the problem. The complexity of the problem is a bound that bounds the best case of the complexity of any possible algorithm that can solve that problem.

For example, when you look at sort, you can say that there's a minimum number of steps that's needed to compute the correct sorted order of the list. In fact, you can prove that to sort a list of elements, you absolutely require $n lg n$ bits of information: there's no possible way to be sure you have the list in sorted order with less information that that. If you're using an algorithm that puts things into sorted order by comparing values, that means that you absolutely must do O(n lg n) comparisons, because each comparison gives you one bit of information. That means that sorting is an O(n log n) problem. We don't need to know which algorithm you're thinking about - it doesn't matter. There is no possible comparison-based sorting algorithm that takes less than $O(n \log n)$ steps. (It's worth noting that there's some weasel-words in there: there are some theoretical algorithms that can sort in less than O(n lg n), but they do it by using algorithms that aren't based on binary comparisons that yield one bit of information.)

We like to describe problems by their complexity in that way when we can. But it's very difficult. We're very good at finding upper bounds: that is, we can in general come up with ways of saying "the execution time will be less than O(something)", but we are very bad at finding ways to prove that "the minimum amount of time needed to solve this problem is O(something)". That distinction, between the upper bound (maximum time needed to solve a problem), and lower bound (minimum time needed to solve a problem) is the basic root of the P == NP question.

When we're talking about the complexity of problems, we can categorize them into complexity classes. There are problems that are O(1), which means that they're constant time, independent of the size of the input. There are linear time problems, which can be solved in time proportional to the size of the input. More broadly, there are two basic categories that we care about: P and NP.

P is the collection of problems that can be solved in polynomial time. That means that in the big-O notation for the complexity, the expression inside the parens is a polynomial: the exponents are all fixed values. Speaking very roughly, the problems in P are the problems that we can at least hope to solve with a program running on a real computer.

NP is the collection of problems that can be solved in non-deterministic polynomial time. We'll just gloss over the "non-deterministic" part, and say that for a problem in NP, we don't know of a polynomial time algorithm for producing a solution, but given a solution, we can check if it's correct in polynomial time. For problems in NP, the best solutions we know of have worst-case bounds that are exponential - that is, the expression inside of the parens of the O(...) has an exponent containing the size of the problem.

NP problems are things that we can't solve perfectly with a real computer. The real solutions take an amount of time that's exponential in the size of their inputs. Tripling the size of the problem increases its execution time by a factor of 27; quadrupling the input size increases execution time by at least a factor of 256; increasing the input by a factor of 10 increases execution time by at least a factor of 10,000,000,000. For NP problems, we're currently stuck using heuristics - shortcuts that will quickly produce a good guess at the real solution, but which will sometimes be wrong.

NP problems are, sadly, very common in the real world. For one example, there's a classic problem called the travelling salesman. Suppose you've got a door-to-door vacuum cleaner salesman. His territory has 15 cities. You want to find the best route from his house to those 15 cities, and back to his house. Finding that solution isn't just important from a theoretical point of view: the time that the salesman spends driving has a real-world cost! We don't know how to quickly produce the ideal path.

The big problem with NP is that we don't know lower bounds for anything in it. That means that while we know of slow algorithms for finding the solution to problems in NP, we don't know if those algorithms are actually the best. It's possible that there's a fast solution - a solution in polynomial time which will give the correct answer. Many people who study computational complexity believe that if you can check a solution in polynomial time, then computing a solution should also be polynomial time with a higher-order polynomial. (That is, they believe that there should be some sort of bound like "the time to find a solution is no more than the cube of the time to check a solution".) But so far, no one has been able to actually prove a relationship like that.

When you look at NP problems, some of them have a special, amazing property called NP completeness. If you could come up with a polynomial time solution for any single NP-complete problem, then you'd also discover exactly how to come up with a polynomial time solution for every other problem in NP..

In Mr. LaPlante's paper, he claims to have implemented a polynomial time solution to a problem called the maximum clique problem. Maximum clique is NP complete - so if you could find a P-time solution to it, you'd have proven that P == NP, and that there are polynomial time solutions to all NP problems.

The problem that Mr. LaPlante looked at is the maximal clique problem:

• Given:
1. a set of $V$ atomic objects called vertices;
2. a set of $E$ of objects called edges, where each edge is an unordered pair $(x, y)$, where $x$ and $y$ are vertices.
• Find:
• The largest set of vertices C=$\{v_1, ..., v_n\}$ where for any $v_i$, there is an edge between $v_i$ to every other vertex in $C$.

Less formally: given a bunch of dots, where some of the dots are connected by lines, find the largest set of dots where every dot in the set is connected to every other dot in the set.

The author claims to have come up with a simple P-time solution to that.

The catch? He's wrong. His solution isn't P-time. It's sloppy work.

His algorithm is pretty easy to understand. Each vertex has a finite set of edges connecting it to its neighbors. You have each node in the graph send its list of its neighbors to its neighbors. With that information, each node knows what 3-cliques its a part of. Every clique of size larger than 3 is made up of overlapping 3-cliques - so you can have the cliques merge themselves into ever larger cliques.

If you look at this, it's still basically considering every possible clique. But His "analysis" of the complexity of his algorithm is so shallow and vague that it's easy to get things wrong. It's a pretty typical example of a sloppy analysis. Complexity analysis is hard, and it's very easy to get wrong. I don't want to be too hard on Mr. LaPlante, because it's an extremely easy mistake to make. Analyzing algorithmic complexity needs to be done in a careful, exacting, meticulous way - and while Mr. LaPlante didn't do that, most people who are professional programmers could easily make a similar mistake! But the ultimate sloppiness of it is that he never bothers to finish computing the complexity. He makes vague hand-wavy motions at showing the complexity of certain phases of his algorithm, but he never even bothers to combine them and come up with an estimate of the full upper-bound of his algorithm!

I'm not going to go into great detail about this. Instead, I'll refer you to a really excellent paper by Patrick Prosser, which looks at a series of algorithms that compute exact solutions to the maximum clique problem, and how they're analyzed. Compare their analysis to Mr. LaPlante's, and you'll see quite clearly how sloppy LaPlante was. I'll give you a hint about one thing LaPlante got wrong: he's taking some steps that take significant work, and treating them as if they were constant time.

But we don't even really need to look at the analysis. Mr. LaPlante provides an implementation of his supposedly P-time algorithm. He should be able to show us execution times for various randomly generated graphs, and show how that time grows as the size of the graph grows, right? I mean, if you're making claims about something like this, and you've got real code, you'll show your experimental verification as well as your theoretical analysis, right?

Nope. He doesn't. And I consider that to be a really, really serious problem. He's claiming to have reduced an NP-complete problem to a small-polynomial complexity: where are the numbers?

I'll give you a good guess about the answer: the algorithm doesn't complete in a reasonable amount of time for moderately large graphs. You could argue that even if it's polynomial time, you're looking at exponents that are no smaller than 3 (exactly what he claims the bound to be is hard to determine, since he never bothers to finish the analysis!) - a cubic algorithm on a large graph takes a very long time. But... not bothering to show any runtime data? Nothing at all? That's ridiculous. If you look at the Prosser paper above, he manages to give actual concrete measurements of the exponential time algorithms. LaPlante didn't bother to do that. And I can only conclude that he couldn't gather actual numbers to support his idea.

# Big Bang Bogosity

One of my long-time mantras on this blog has been “The worst math is no math”. Today, I’m going to show you yet another example of that: a recent post on Boing-Boing called “The Big Bang is Going Down”, by a self-proclaimed genius named Rick Rosner.

First postulated in 1931, the Big Bang has been the standard theory of the origin and structure of the universe for 50 years. In my opinion, (the opinion of a TV comedy writer, stripper and bar bouncer who does physics on the side) the Big Bang is about to collapse catastrophically, and that’s a good thing.

According to Big Bang theory, the universe exploded into existence from basically nothing 13.7-something billion years ago. But we’re at the beginning of a wave of discoveries of stuff that’s older than 13.7 billion years.

We’re constantly learning more about our universe, how it works, and how it started. New information isn’t necessarily a catastrophe for our existing theories; it’s just more data. There’s constantly new data coming in – and as yet, none of it comes close to causing the big bang theory to catastrophically collapse.

The two specific examples cited in the article are:

1. one quasar that appears to be younger than we might expect – it existed just 900 million years after the current estimate of when the big bang occurred. That’s very surprising, and very exciting. But even in existing models of the big bang, it’s surprising, but not impossible. (No link, because the link in the original article doesn’t work.)
2. an ancient galaxy – a galaxy that existed only 700 million years after the big bang occurred – contains dust. Cosmic dust is made of atoms much larger than hydrogen – like carbon, silicon, and iron, which are (per current theories) the product of supernovas. Supernovas generally don’t happen to stars younger than a couple of billion years – so finding dust in a galaxy less than a billion years after the universe began is quite surprising. But again: impossible under the big bang? No.

The problem with both of these arguments against the big bang is: they’re vague. They’re both handwavy arguments made about crude statements about what “should” be possible or impossible according to the bing bang theory. But neither comes close to the kind of precision that an actual scientific argument requires.

Scientists don’t use math because they like to be obscure, or because they think all of the pretty symbols look cool. Math is a tool used by scientists, because it’s useful. Real theories in physics need to be precise. They need to make predictions, and those predictions need to match reality to the limits of our ability to measure them. Without that kind of precision, we can’t test theories – we can’t check how well they model reality. And precise modelling of reality is the whole point.

The big bang is an extremely successful theory. It makes a lot of predictions, which do a good job of matching observations. It’s evolved in significant ways over time – but it remains by far the best theory we have – and by “best”, I mean “most accurate and successfully predictive”. The catch to all of this is that when we talk about the big bang theory, we don’t mean “the universe started out as a dot, and blew up like a huge bomb, and everything we see is the remnants of that giant explosion”. That’s an informal description, but it’s not the theory. That informal description is so vague that a motivated person can interpret it in ways that are consistent, or inconsistent with almost any given piece of evidence. The real big bang theory isn’t a single english statement – it’s many different mathematical statements which, taken together, produce a description of an expansionary universe that looks like the one we live in. For a really, really small sample, you can take a look at a nice old post by Ethan Siegel over here.

If you really want to make an argument that it’s impossible according to the big bang theory, you need to show how it’s impossible. The argument by Mr. Rosner is that the atoms in the dust in that galaxy couldn’t exist according to the big bang, because there wasn’t time for supernovas to create it. To make that argument, he needs to show that that’s true: he needs to look at the math that describes how stars form and how they behave, and then using that math, show that the supernovas couldn’t have happened in that timeframe. He doesn’t do anything like that: he just asserts that it’s true.

In contrast, if you read the papers by the guys who discovered the dust-filled galaxy, you’ll notice that they don’t come anywhere close to saying that this is impossible, or inconsistent with the big bang. All they say is that it’s surprising, and that we made need to revise our understanding of the behavior of matter in the early stages of the universe. The reason that they say that is because there’s nothing there that fundamentally conflicts with our current understanding of the big bang.

But Mr. Rosner can get away with the argument, because he’s being vague where the scientists are being precise. A scientist isn’t going to say “Yes, we know that it’s possible according to the big bang theory”, because the scientist doesn’t have the math to show it’s possible. At the moment, we don’t have sufficient precise math either way to come to a conclusion; we don’t know. But what we do know is that millions of other observations in different contexts, different locations, observed by different methods by different people, are all consistent with the predictions of the big bang. Given that we don’t have any evidence to support the idea that this couldn’t happen under the big bang, we continue to say that the big bang is the theory most consistent with our observations, that it makes better predictions than anything else, and so we assume (until we have evidence to the contrary) that this isn’t inconsistent. We don’t have any reason to discard the big bang theory on the basis of this!

Mr. Rosner, though, goes even further, proposing what he believes will be the replacement for the big bang.

The theory which replaces the Big Bang will treat the universe as an information processor. The universe is made of information and uses that information to define itself. Quantum mechanics and relativity pertain to the interactions of information, and the theory which finally unifies them will be information-based.

The Big Bang doesn’t describe an information-processing universe. Information processors don’t blow up after one calculation. You don’t toss your smart phone after just one text. The real universe – a non-Big Bang universe – recycles itself in a series of little bangs, lighting up old, burned-out galaxies which function as memory as needed.

In rolling cycles of universal computation, old, collapsed, neutron-rich galaxies are lit up again, being hosed down by neutrinos (which have probably been channeled along cosmic filaments), turning some of their neutrons to protons, which provides fuel for stellar fusion. Each calculation takes a few tens of billions of years as newly lit-up galaxies burn their proton fuel in stars, sharing information and forming new associations in the active center of the universe before burning out again. This is ultra-deep time, with what looks like a Big Bang universe being only a long moment in a vast string of such moments across trillions or quadrillions of giga-years.

This is not a novel idea. There are a ton of variations of the “universe as computation” that have been proposed over the years. Just off the top of my head, I can rattle off variations that I’ve read (in decreasing order of interest) by Minsky (can’t find the paper at the moment; I read it back when I was in grad school), by Fredkin, by Wolfram, and by Langan.

All of these theories assert in one form or another that our universe is either a massive computer or a massive computation, and that everything we can observe is part of a computational process. It’s a fascinating idea, and there are aspects of it that are really compelling.

For example, the Minsky model has an interesting explanation for the speed of light as an absolute limit, and for time dilation. Minksy’s model says that the universe is a giant cellular automaton. Each minimum quanta of space is a cell in the automaton. When a particle is located in a particular cell, that cell is “running” the computation that describes that particle. For a particle to move, the data describing it needs to get moved from its current location to its new location at the next time quanta. That takes some amount of computation, and the cell can only perform a finite amount of computation per quanta. The faster the particle moves, the more of its time quantum are dedicated to motion, and the less it has for anything else. The speed of light, in this theory, is the speed where the full quanta for computing a particle’s behavior is dedicated to nothing but moving it to its next location.

It’s very pretty. Intuitively, it works. That makes it an interesting idea. But the problem is, no one has come up with an actual working model. We’ve got real observations of the behavior of the physical universe that no one has been able to describe using the cellular automaton model.

That’s the problem with all of the computational hypotheses so far. They look really good in the abstract, but none of them come close to actually working in practice.

A lot of people nowadays like to mock string theory, because it’s a theory that looks really ogood, but has no testable predictions. String theory can describe the behavior of the universe that we see. The problem with it isn’t that there’s things we observe in the universe that it can’t predict, but because it can predict just about anything. There are a ton of parameters in the theory that can be shifted, and depending on their values, almost anything that we could observe can be fit by string theory. The problem with it is twofold: we don’t have any way (yet) of figuring out what values those parameters need to have to fit our universe, and we don’t have any way (yet) of performing an experiment that tests a prediction of string theory that’s different from the predictions of other theories.

As much as we enjoy mocking string theory for its lack of predictive value, the computational hypotheses are far worse! So far, no one has been able to come up with one that can come close to explaining all of the things that we’ve already observed, much less to making predictions that are better than our current theories.

But just like he did with his “criticism” of the big bang, Mr. Rosner makes predictions, but doesn’t bother to make them precise. There’s no math to his prediction, because there’s no content to his prediction. It doesn’t mean anything. It’s empty prose, proclaiming victory for an ill-defined idea on the basis of hand-waving and hype.

Boing-Boing should be ashamed for giving this bozo a platform.