# How Computers Work: Arithmetic With Gates

In my last post, I promised that I’d explain how we can build interesting mathematical operations using logic gates. In this post, I’m going to try do that by walking through the design of a circuit that adds to multi-bit integers together.

As I said last time, most of the time, when we’re trying to figure out how to do something with gates, it’s useful to with boolean algebra to figure out what we want to build. If we have two bits, what does it mean, in terms of boolean logic to add them?

Each input can be either 0 or 1. If they’re both one, then the sum is 0. If either, but not both, is one, then the sum is 1. If both are one, then the sum is 2. So there’s three possible outputs: 0, 1, and 2.

This brings us to the first new thing: we’re building something that operates on single bits as inputs, but it needs to have more than one bit of output! A single boolean output can only have two possible values, but we need to have three.

The way that we usually describe it is that a single bit adder takes two inputs, X and Y, and produces two outputs, SUM and CARRY. Sum is called the “low order” bit, and carry is the “high order” bit – meaning that if you interpret the output as a binary number, you put the higher order bits to the left of the low order bits. (Don’t worry, this will get clearer in a moment!)

Let’s look at the truth table for a single bit adder, with a couple of extra columns to help understand how we intepret the outputs:

If we look at the SUM bit, it’s an XOR – that is, it outputs 1 if exactly one, but not both, of its inputs is 1; otherwise, it outputs 0. And if we look at the carry bit, it’s an AND. Our definition of one-bit addition is thus:

• $SUM = X \oplus Y$
• $CARRY = X \land Y$

We can easily build that with gates:

This little thing is called a half-adder. That may seem confusing at first, because it is adding two one-bit values. But we don’t really care about adding single bits. We want to add numbers, which consist of multiple bits, and for adding pairs of bits from multibit numbers, a half-adder only does half the work.

That sounds confusing, so let’s break it down a bit with an example.

• Imagine that we’ve got two two bit numbers, 1 and 3 that we want to add together.
• In binary 1 is 01, and 3 is 11.
• If we used the one-bit half-adders for the 0 bit (that is, the lowest order bit – in computer science, we always start counting with 0), we’d get 1+1=0, with a carry of 1; and for the 1 bit, we’d get 1+0=1 with a carry of 0. So our sum would be 10, which is 2.
• That’s wrong, because we didn’t do anything with the carry output from bit 0. We need to include that as an input to the sum of bit 1.

We could try starting with the truth table. That’s always a worthwile thing to do. But it gets really complicated really quickly.

This is a nice illustration of why designing CPUs is so hard, and why even massively analyzed and tested CPUs still have bugs! We’re looking at one of the simplest operations to implement; and we’re only looking at it for 2 bits of input. But already, it’s hard to decide what to include in the table, and to read the table to understand what’s going on. We’re not really going to be able to do much reasoning here directly in boolean logic using the table. But it’s still good practice, both because it helps us make sure we understand what outputs we want, and because it gives us a model to test against once we’ve build the network of gates.

And there’s still some insight to be gained here: Look at the row for 1 + 3. In two bit binary, that’s 01 + 11. The sum for bit 0 is 0 – there’s no extra input to worry about,
but it does generate a carry out. The sum of the input bits for bit
one is 1+1=10 – so 0 with a carry bit. But we have the carry from bit
0 – that needs to get added to the sum for bit1. If we do that – if we do another add step to add the carry bit from bit 0 to the sum from bit 1, then we’ll get the right result!

The resulting gate network for two-bit addition looks like:

The adder for bit 1, which is called a full adder, adds the input bits X1 and Y1, and then adds the sum of those (produced by that first adder) to the carry bit from bit0. With this gate network, the output from the second adder for bit 1 is the correct value for bit 1 of the sum, but we’ve got two different carry outputs – the carry from the first adder for bit 1, and the carry from the second adder. We need to combine those somehow – and the way to do it is an OR gate.

Why an OR gate? The second adder will only produce a carry if the first adder produced a 1 as its output. But there’s no way that adding two bits can produce both a 1 as its sum output and a 1 as its carry output. So the carry bit from the second adder will only ever be 1 if the output of the first adder is 0; and the carry output from the first adder will only ever be 1 if the sum output from the first carry is 0. Only one of the two carries will ever be true, but if either of them is true, we should produce a 1 as the carry output. Thus, the or-gate.

Our full adder, therefore, takes 3 inputs: a carry from the next lower bit, and the two bits to sum; and it outputs two bits: a sum and a carry. Inside, it’s just two adders chained together, so that first we add the two sum inputs, and then we add the sum of that to the incoming carry.

For more than two bits, we just keep chaining full adders together. For example,

This way of implementing sum is called a ripple carry adder – because the carry bits ripple up through the gates. It’s not the most efficient way of producing a sum – each higher order bit of the inputs can’t be added together until the next lower bit is done, so the carry ripples through the network as each bit finishes, and the total time required is proportional to the number of bits to be summed. More bits means that the ripple-carry adder gets slower. But this works, and it’s pretty easy to understand.

There are faster ways to build multibit adders, by making the gate network more complicated in order to remove the ripple delay. You can imagine, for example, that instead of waiting for the carry from bit 0, you could just build the circuit for bit 1 so that it inputs X0 and Y0; and similarly, for bit 2, you could include X0, X1, Y0, and Y1 as additional inputs. You can imagine how this gets complicated quickly, and there are some timing issues that come up as the network gets more complicated, which I’m really not competent to explain.

Hopefully this post successfully explained a bit of how interesting operations like arithmetic can be implemented in hardware, using addition as an example. There are similar gate networks for subtraction, multiplication, etc.

These kinds of gate networks for specific operations are parts of real CPUs. They’re called functional units. In the simplest design, a CPU has one functional unit for each basic arithmetic operation. In practice, it’s a lot more complicated than that, because there are common parts shared by many arithmetic operations, and you can get rid of duplication by creating functional units that do several different things. We might look at how that works in a future post, if people are interested. (Let me know – either in the comments, or email, or mastodon, if you’d like me to brush up on that and write about it.)

# How Computers Work: Logic Gates

At this point, we’ve gotten through a very basic introduction to how the electronic components of a computer work. The next step is understanding how a computer can compute anything.

There are a bunch of parts to this.

1. How do single operations work? That is, if you’ve got a couple of numbers represented as high/low electrical signals, how can you string together transistors in a way that produces something like the sum of those two numbers?
2. How can you store values? And once they’re stored, how can you read them back?
3. How does the whole thing run? It’s a load of transistors strung together – how does that turn into something that can do things in sequence? How can it “read” a value from memory, interpret that as an instruction to perform an operation, and then select the right operation?

In this post, we’ll start looking at the first of those: how are individual operations implemented using transistors?

## Boolean Algebra and Logic Gates

The mathematical basis is something called boolean algebra. Boolean algebra is a simple mathematical system with two values: true and false (or 0 and 1, or high and low, or A and B… it doesn’t really matter, as long as there are two, and only two, distinct values).

Boolean algebra looks at the ways that you can combine those true and false values. For example, if you’ve got exactly one value (a bit) that’s either true or false, there are four operations you can perform on it.

1. Yes: this operation ignores the input, and always outputs True.
2. No: like Yes, this ignores its input, but in No, it always outputs False.
3. Id: this outputs the same value as its input. So if its input is true, then it will output true; if its input is false, then it will output false.
4. Not: this reads its input, and outputs the opposite value. So if the input is true, it will output false; and if the input is false, it will output True.

The beauty of boolean algebra is that it can be physically realized by transistor circuits. Any simple, atomic operation that can be described in boolean algebra can be turned into a simple transistor circuit called a gate. For most of understanding how a computer works, once we understand gates, we can almost ignore the fact that there are transistors behind the scenes: the gates become our building blocks.

## The Not Gate

We’ll start with the simplest gate: a not gate. A not gate implements the Not operation from boolean algebra that we described above. In a physical circuit, we’ll interpret a voltage on a wire (“high”) as a 1, and no voltage on the wire (“low”) as a 0. So a not gate should output low (no voltage) when its input is high, and it should output high (a voltage) when its input is low. We usually write that as something called a truth table, which shows all of the possible inputs, and all of the possible outputs. In the truth table, we usually write 0s and 1s: 0 for low (or no current), and 1 for high. For the NOT gate, the truth table has one input column, and one output column.

I’ve got a sketch of a not gate in the image to the side. It consists of two transistors: a standard (default-off) transistor, which is labelled “B”, and a complementary transistor (default-on) labeled A. A power supply is provided on the the emitter of transistor A, and then the collector of A is connected to the emitter of B, and the collector of B is connected to ground. Finally, the input is split and connected to the bases of both transistors, and the output is connected to the wire that connects the collector of A and the emitter of B.

That all sounds complicated, but the way that it works is simple. In an electric circuit, the current will always follow the easiest path. If there’s a short path to ground, the current will always follow that path. And ground is always low (off/0). Knowing that, let’s look at what this will do with its inputs.

Suppose that the input is 0 (low). In that case, transistor A will be on, and transistor B will be off. Since B is off, there’s no path from the power to ground; and since A is on, cif there’s any voltage at the input, then current will flow through A to the output.

Now suppose that the input is 1 (high). In that case, A turns off, and B turns on. Since A is off, there’s no path from the power line to the output. And since B is on, the circuit has connected the output to ground, making it low.

Our not gate is, basically, a switch. If its input is high, then the switch attaches the output to ground; if its input is low, then the switch attaches the output to power.

## The NAND gate

Let’s try moving on to something more interesting: a NAND gate. A NAND gate takes two inputs, and outputs high when any of its inputs is low. Engineers love NAND gates, because you can create any boolean operation by combining NAND gates. We’ll look at that in a bit more detail later.

Here’s a diagram of a NAND gate. Since there’s a lots of wires running around and crossing each other, I’ve labeled the transistors, and made each of the wires a different color:

• Connections from the power source are drawn in green.
• Connections from input X are drawn in blue.
• Connections from input Y are drown in red.
• The complementary transistors are labelled C1 and C2.
• The output of the two complementary transistors is labelled “cout”, and drawn in purple.
• The two default-off transistors are labelled T1 and T2.
• The output from the gate is drawn in brown.
• Connections to ground are drawn in black.

Let’s break down how this works:

• In the top section, we’ve got the two complimentary (default-on) transistors. If either of the inputs is 0 (low), then they’ll stay on, and pass a 1 to the cout line. There’s no connection to ground, and there is a connection to power via one (or both) on transistors, so the output of the circuit will be 1 (high).
• If neither of the inputs is low, then both C1 and C2 turn off. Cout is then not getting any voltage, and it’s 0. You might think that this is enough – but we want to force the output all the way to 0, and there could be some residual electrons in C1 and C2 from the last time they were active. So we need to provide a path to drain that, instead of allowing it to possibly affect the output of the gate. That’s what T and T2 are for on the bottom. If both X and Y are high, then both T1 and T2 will be on – and that will provide an open path to ground, draining the system, so that the output is 0 (low).

## Combining Gates

There are ways of building gates for each of the other basic binary operators in boolean algebra: AND, OR, NOR, XOR, and XNOR. But in fact, we don’t need to know how to do those – because in practice,all we need is a NAND gate. You can combine NAND gates to produce any other gate that you want. (Similarly, you can do the same with NOR gates. NAND and NOR are called universal gates for this reason.)

Let’s look at how that works. First, we need to know how to draw gates in a schematic form, and what each of the basic operations do. So here’s a chart of each operation, its name, its standard drawing in a schematic, and its truth table.

Just like we did with the basic gates above, we’ll start with NOT. Using boolean logic identities, we can easily derive that $\lnot A = A \lnot\land A$; or in english, “not A” is the same thing as “not(A nand A)”. In gates, that’s easy to build: it’s a NAND gate with both of its inputs coming from the same place:

For a more interesting one, let’s look at AND, and see how we can build that using just NAND gates. We can go right back to boolean algebra, and play with identities. We want $A \land B$. It’s pretty straightforward in terms of logic: “$A \and B$” is the same as $\lnot (A \lnot\land B)$.

That’s just two NAND gates strung together, like this:

We can do the same basic thing with all of the other basic boolean operations. We start with boolean algebra to figure out equivalences, and then translate those into chains of gates.

With that, we’ve got the basics of boolean algebra working in transistors. But we still aren’t doing interesting computations. The next step is building up: combining collections of gates together to do more complicated things. In the next post, we’ll look at an example of that, by building an adder: a network of gates that performs addition!

# How Computers Work, Part 2: Transistors

From my previous post, we understand what a diode is, and how it works. In this post, we’re going to move on to the transistor. Once we’ve covered that, then we’ll start to move up a layer, and stop looking at the behavior of individual subcomponents, and focus on the more complicated (and interesting) components that are built using the basic building blocks of transistors.

By a fortuitous coincidence, today is actually the anniversary of the first transistor, which was first successfully tested on December 16th 1947, at Bell Labs.

Transistors are amazing devices. It’s hard to really grasp just how important they are. Virtually everything you interact with in 21st century live depends on transistors. Just to give you a sense, here’s a sampling of my day so far.

• Get woken up by the alarm on my phone. The phone has billions of transistors. But even if I wasn’t using my phone, any bedside clock that I’ve had in my lifetime has been built on transistors.
• Brush my teeth and shave. Both my electric razor and toothbrush use transistors for a variety of purposes.
• Put on my glasses. These days, I wear lenses with a prismatic correction. The prism in the lens is actually a microscoping set of grids, carved into the lens by an ultraprecise CNC machine. Aka, a computer – a ton of transistors, controlling a robotic cutting tool by using transistors as switches.
• Come downstairs and have breakfast. The refrigerator where I get my milk uses transistors for managing the temperature, turning the compressor for the cooling on and off.
• Grind some coffee. Again, transistor based electronics controlling the grinder.
• Boil water, in a digitally controlled gooseneck kettle. Again, transistors.

There’s barely a step of my day that doesn’t involve something with transistors. But most of us have next to no idea what they actually do, much less how. That’s what we’re going to look at in this post. This very much builds on the discussion of diodes in last weeks post, so if you haven’t read that, now would be a good time to jump back.

What is a transistor? At the simplest, it’s an electronic component that can be used in two main ways. It’s works like an on-off switch, or like a volume control – but without any moving parts. In a computer, it’s almost always used as a switch – but, crucially, a switch that doesn’t need to move to turn on or off. The way that it works is a bit tricky, but if we don’t really get too deep and do the math, it’s not too difficult to understand.

We’ll start with the one my father explained to me first, because he thought it was the simplest kind of transistor to understand. It’s called a junction transistor. A junction transistor is, effectively, two diodes stack back to back. There’s two kinds – the PNP and the NPN. We’ll look an the NPN type, which acts like a switch that’s off by default.

Each diode consists of a piece of N type silicon connected to a piece of P type silicon. By joining them back to back, we get a piece of P type silicon in the middle, with N type silicon on either side. That means that on the left, we’ve got an NP boundary – a diode that wants to flow right-to-left, and block current left-to-right; and on the right, we’ve got a PN boundary – a diode that wants to let current flow from right to left, and block left to right.

If we only have the two outer contacts, we’ve got something that simply won’t conduct electricity. But if we add a third contact to the P region in the middle, then suddenly things change!

Let’s give things some names – it’ll help with making the explanation easier to follow. We’ll call the left-hand contact of the transistor the emitter, and the right hand the collector The contact that we added to the middle, we’ll call the base.

(Quick aside: these names are very misleading, but sadly they’re so standardized that we’re stuck with them. Electrical engineering got started before we really knew which charges were moving in a circuit. By convention, circuits were computed as if it was the positive charges that move. So the names of the transistor come from that “conventional” current flow tradition. The “collector” recieves positive charges, and the emitter emits them. Yech.)

If we don’t do anything with the base, but we attach the emitter to the negative side of a battery, and the collector to the positive, what happens is nothing. The two diodes that make up the transistor block any current from flowing. It’s still exactly the same as in the diodes – there’s a depletion region around the NP and PN boundaries. So while current could flow from the emitter to the base, it can’t flow into the collector and out of the circuit; and the base isn’t connected to anything. So all that it can do is increase the size of the depletion zone around the PN boundary on the right.

What if we apply some voltage to the base?

Then we’ve got a negative charge coming into the P type silicon from the base contact, filling holes and creating a negative charge in the P-type silicon away from the NP boundary. This creates an electric field that pushes electrons out of the holes along the NP-boundary, essentially breaking down the depletion zone. By applying a voltage to the base, we’ve opened a connection between the emitter and the collector, and current will flow through the transistor.

Another way of thinking about this is in terms of electrons and holes. If you have a solid field of free electrons, and you apply a voltage, then current will flow. But if you have electron holes, then the voltage will push some electrons into holes, creating a negatively charged region without free electrons that effectively blocks any current from flowing. By adding a voltage at the base, we’re attracting holes to the base, which means that they’re not blocking current from flowing from the emitter to the collector!

The transistor is acting like a switch controlled by the base. If there’s a voltage at the base, the switch is on, and current can flow through the transistor; if there’s no voltage at the base, then the switch is off, and current won’t flow.

I said before that it can also act like a volume control, or an amplifier. That’s because it’s not strictly binary. The amount of voltage at the base matters. If you apply a small voltage, it will allow a small current to flow from the emitter to the collector. As you increase the voltage at the base, the amount of current that flows through the transistor also increases. You can amplify things by putting a high voltage at the emitter, and then the signal you want to amplify at the base. When the signal is high, the amount of voltage passing will be high. If the voltage at the emitter is significantly higher than the voltage of the signal, then what comes out of the collector is the same signal, but at a higher voltage. So it’s an amplifier!

There’s a bunch of other types of transistors – I’m not going to go through all of them. But I will look at one more, because it’s just so important. It’s called a MOSFET – metal oxide semiconductor field effect transistor. Pretty much all of our computers are built on an advanced version of MOSFET called CMOS.

Just to be annoying, the terminology changes for the names of the contacts on a MOSFET. In a MOSFET, the negative terminal is called the source, and the positive terminal is the drain. The control terminal is still called the gate. In theory, there’s a fourth terminal called the body, but in practice, that’s usually connected to the source.

The way a field effect transistor works is similar to a junction transistor – but the big difference is, no current ever flows through the base, because it’s not actually electrically connected to the P-type silicon of the body. It’s shielded by a metal oxide layer (thus the “metal oxide” part of MOSFET).

In a MOSFET, we’ve got a bulky section of P-type silicon, called the substrate. On top of it, we’ve got two small N-type regions for the source and the drain. Between the source and the drain, on the surface of the MOSFET, there’s a thin layer of non-conductive metal oxide (or, sometimes, silicon dioxide – aka glass), and then on top of that metal oxide shield is the base terminal. Underneath the gate is P-type silicon, in an area called the channel region.

Normally, if there’s a voltage at the drain, but no voltage on the gate, it behaves like a junction transistor. Along the boundaries of the N-type terminals, You get electrons moving from the N-type terminals to the P-type holes, creating a depletion region. The current can’t flow – the negatively charged P-side of the depletion region blocks electrons from flowing in to fill more holes, and the open holes in the rest of the P-type region prevent electrons from flowing through.

If we apply a positive voltage (that is, a positive charge) at the gate, then you start to build up a (relative) positive charge near the gate and a negative charge near the body terminal. The resulting field pushes the positively charged holes away from the gate, and pulls free electrons towards the gate. If the voltage is large enough, it eventually creates what’s called an inversion region – a region which has effectively become N-type silicon because the holes have been pushed away, and free electrons have been pulled in. Now there’s a path of free electrons from source to drain, and current can flow across the transistor.

That’s what we call an N-type MOSFET transistor, because the source and drain are N-type silicon. There’s also a version where the source and drain are P-type, and the body is N type, called a P-type transistor. A P-type MOSFET transistor conducts current when there is no voltage on the base, and stops doing so when voltage is applied.

There’s an advanced variant of MOSFET called CMOS – complementary metal oxide semiconductor. It’s an amazing idea that pairs P-type and N-type transistors together to produce a circuit that doesn’t draw power when it isn’t being switched. I’m not going to go into depth about it here – I may write something about it later. You can see an article about it at the computer history museum.

On a personal note, in that article, you’ll see how “RCA Research Laboratories and the Somerville manufacturing operation pioneered the production of CMOS technology (under the trade name COS/MOS) for very low-power integrated circuits, first in aerospace and later in commercial applications.” One of the team at RCA Somerville semiconductor manufacturing center who worked on the original CMOS manufacturing process was my father. He was a semiconductor physicist who worked on manufacturing processes for aerospace systems.

While doing that, my father met William Shockley. Shockley was the lead of the team at Bell Labs that developed the first transistor. He was, without doubt, one of the most brilliant physisists of the 20th century. He was also a total asshole of absolutely epic proportions. Based on his interactions with Shockley, my dad developed his own theory of human intelligence: “Roughly speaking, everyone is equally intelligent. If you’re a genius in one field, that means that you must be an idiot in all others”. I think of all the people he met in his life, my dad thought Shockley was, by far, the worst.

If you don’t know about Shockley, well… Like I said, the guy was a stunningly brilliant physisist and a stunningly awful person. He was a coinventor of the transistor, and pretty much created silicon valley. But he also regularly told anyone who’d listen about how his children were “genetic regressions” on his intellectual quality (due of course, he would happily explain, to the genetic inferiority of his first wife). After collecting his Nobel prize for the invention of the transistor, he dedicated the rest of his life to promoting eugenics and the idea that non-white people are genetically inferior to whites. You can read more about his turn to pseudo-scientific racism and eugenics in this article by the SPLC.)

## References

A few of the sources I looked at while writing this. (As usual, I’m a bit scattered, so I’m sure there were other places I looked, but these are the links I remembered.)

Since I used to work at twitter, a few folks have asked what I think of what’s going on with Twitter now that Elon Musk bought it.

The answer is a little bit complicated. I worked for Twitter back in 2013, so it’s quite a while ago, and an awful lot has changed in that time.

I went to Twitter out of idealism. I believed it was a platform that would change the world in a good way. At the time, there was a lot going on to back up that idea. Twitter was being used in the Arab Spring uprisings, it was giving underrepresented voices like Black Lives Matter a platform, and it really looked like it was going to democratize communication in a wonderful way. I was so thrilled to be able to become a part of that.

But what I found working there was a really dysfunctional company. The company seemed to be driven by a really odd kind of fear. Even though it seemed (at least from my perspective) like no one ever got penalized, everyone in management was terrified of making a decision that might fail. Failure was a constant spectre, which haunted everything, but which was never acknowledged, because if anyone admitted that something had failed, they might be held responsible for the failure. I’ve never seen anything like it anywhere else I worked, but people at Twitter were just terrified of making decisions that they could be held responsible for.

A concrete example of that was something called Highline. When I got to Twitter, the company was working on a new interface, based on the idea that a user could have multiple timelines. You’d go to twitter and look at your News timeline to find out what was going on in your world, and all you’d see was news. Then you’d switch to your music timeline to read what your favorite artists were talking about. Or your Friends timeline to chat with your friends.

At first, HighLine was everything. Every team at the company was being asked to contribute. It was the only topic at the first two monthly company-wide all-hands. It was the future of twitter.

And then it disappeared, like it had never happened. Suddenly there was no highline, no new interface in the plans. No one ever said it was cancelled. It was just gone.

At the next company all-hands, Dick Costolo, the CEO, gave his usual spiel about the stuff we’d been working on, and never mentioned highline. In fact, he talked about the work of the last few months, and talked about everything except highline. If I hadn’t been there, and didn’t know about it, I would never have guessed that nearly everyone in that room had been working heads-down on a huge high-profile effort for months. It was just gone, disappeared down the memory hole. It hadn’t failed, and no one was responsible, because it had never happened.

There was more nonsense like that – rewriting history to erase things that no one wanted to be responsible for, and avoiding making any actual decisions about much of anything. The only things that ever got decided where things where we were copying facebook, because you couldn’t be blamed for doing the same thing as the most successful social media company, right?

After about a year, we got (another) new head of engineering, who wanted to get rid of distributed teams. I was the sole NYer in an SF based team. I couldn’t move to SF, so I left.

I came away from it feeling really depressed about the company and its future. A company that can’t make decisions, that can’t take decisive actions, that can’t own up to and learn from its mistakes isn’t a company with a bright future. The whole abuse situation had also grown dramatically during my year there, and it was clear that the company had no idea what to do about it, and were terrified of trying anything that might hurt the user numbers. So it really seemed like the company was heading into trouble, both in terms of the business, and the platform.

Looking at the Musk acquisition: I’ll be up front before I get into it. I think Elon is a jackass. I’ll say more about why, but being clear, I think he’s an idiot with no idea of what he’s gotten himself into.

That said: the company really needed to be shaken up. They hired far too many people – largely because of that same old indecisiveness. You can’t move existing staff off of what they’re doing and on to something new unless you’re willing to actually cancel the thing that they were working on. But cancelling a stream of in-progress work takes some responsibility, and you have to account for the now wasted sunk cost of what they were doing. So instead of making sure that everyone was working on something that was really important and valuable to the company, they just hired more people to do new things. As a result, they were really bloated.

So trimming down was something they really needed. Twitter is a company that, run well, could probably chug along making a reasonable profit, but it was never going to be a massive advertising juggernaut like Google or Facebook. So keeping tons of people on the staff when they aren’t really contributing to the bottom line just doesn’t work. Twitter couldn’t afford to pay that many people given the amount of money it was bringing in, but no one wanted to be responsible for deciding who to cut. So as sad as it is to see so many smart, hard-working people lose their jobs, it was pretty inevitable that it would happen eventually.

But The way that Elon did it was absolutely mind-numbingly stupid.

He started by creating a bullshit metric for measuring productivity. Then he stack-ranked engineers based on that bullshit metric, and fired everyone below a threshold. Obviously a brilliant way to do it, right? After all, metrics are based on data, and data is the basis of good decisions. So deciding who to fire and who to keep based on a metric will work out well!

Sadly, his metric didn’t include trivial, unimportant things like whether the people he was firing were essential to running the business. Because he chose the metric before he understood anything about what the different engineers did. An SRE might not commit as many lines of code to git, but god help your company if your service discovery system goes down, and you don’t have anyone who knows how to spin it back up without causing a massive DDOS across your infrastructure.

Then came the now-infamous loyalty email. Without telling any of the employees what he was planning, he demanded that they commit themselves to massive uncompensated increase in their workload. If they wouldn’t commit to it, the company would take that as a resignation. (And just to add insult to injury, he did it with a Google forms email.)

Dumb, dumb, dumb.

Particularly because, again, it didn’t bother to consider that people aren’t interchangable. There are some people that are really necessary, that the company can’t do without. What if they decide to leave rather than sign? (Which is exactly what happened, leading to managers making desperate phone calls begging people to come back.)

So dumb. As my friend Mike would say, “Oh fuck-a-duck”.

Elon is supposed to be a smart guy. So why is he doing such dumb stuff at twitter?

Easy. He’s not really that smart.

This is something I’ve learned from working in tech, where you deal with a lot of people who became very wealthy. Really wealthy people tend to lose touch with the reality of what’s going on around them. They get surrounded by sycophants who tell them how brilliant, how wonderful, how kind and generous they are. They say these things not because they’re true, but because it’s their job to say them.

But if you’re a person who hears nothing, from dawn to dusk, but how brilliant you are, you’ll start to believe it. I’ve seen, several times, how this can change people. People who used to be reasonable and down to earth, but after a few years of being surrounded by yes-men and sycophants completely lose touch.

Elon is an extreme case of that. He grew up in a rich family, and from the time he was a tiny child, he’s been surrounded by people whose job it is to tell him how smart, how brilliant, how insightful, how wonderful he is. And he genuinely believes that. In his head, he’s a combination of Einstein, Plato, and Ghandi – he’s one of the finest fruits of the entire human race. Anyone who dares to say otherwise gets fired and kicked out of the great presence of the mighty Musk.

Remember that this is a guy who went to Stanford, and dropped out, because he didn’t like dealing with Professors who thought they knew more than he did. He’s a guy who believes that he’s a brilliant rocket scientist, but who stumbles every time he tries to talk about it. But he believes that he’s an expert – because (a) he’s the smartest guy in the world, and (b) all of the engineers in his company tell him how brilliant he is and how much he’s contributing to their work. He’d never even consider the possibility that they say that because he’dfire them if he didn’t. And, besides, as I’m sure his fanboys are going to say: If he’s not so smart, then why does he have so much more money than me?

Take that kind of clueless arrogance, and you see exactly why he’s making the kinds of decisions that he is at Twitter. Why the stupid heavy-handed layoffs? He’s the smartest guy in the world, and he invented a metric which is obviously correct for picking which engineers to keep. Why pull the stupid “promise me your children or you’re fired” scam? Because they should feel privileged to work for the Great Elon. They don’t need to know what he’s planning: they should just know that because it’s Elon, it’s going to be the most brilliant plan they can imagine, and they should be willing to put their entire lives on the line for the privilege of fulfilling it.

It’s the stupidity of arrogance from start to finish. Hell, just look at the whole acquisition process.

He started off with the whole acquisition as a dick-swinging move: “How dare you flag my posts and point out that I’m lying? Do you know

Then he lined up some financing, and picked a ridiculously over-valued price for his buyout offer based on a shitty pot joke. (Seriously, that’s how he decided how much to pay for Twitter: he wanted the per-share price to be a pot joke.)

Once the deal was set, he realized how ridiculous it was, so he tried to back out. Only the deal that he demanded, and then agreed to, didn’t leave him any way to back out! He’d locked himself in to paying this ridiculous price for a shitty company. But he went to court to try to get out of it, by making up a bullshit story about how Twitter had lied to him.

When it became clear that the courts were going to rule against his attempt to back out, he reversed course, and claimed that he really did want to buy Twitter, despite the fact that he’d just spent months trying to back out. It had nothing to do with the fact that he was going to lose the case – perish the thought! Elon never loses! He just changed his mind, because he’s such a wonderful person, and he believes twitter is important and only he can save it.

It’s such stupid shit from start to finish.

So I don’t hold out much hope for the future of Twitter. It’s being run by a thin-skinned, egotistical asshole who doesn’t understand the business that he bought. I’m guessing that he’s got some scheme where he’ll come out of it with more money than he started with, while leaving the investors who backed his acquisition holding the bag. That’s how money guys like Elon roll. But Twitter is probably going to be burned to the ground along the way.

But hey, he bought all of my Twitter stock for way more than it was worth, so that was nice.

# How Do Computers Really Work? Part 1: Basics

It’s been a long time since I updated this blog. I keep saying I want to get back to it, but every time I post anything, it leads to a stream of abuse from trolls and crackpots, and the frustration drives me away. But I shouldn’t let a pack of bullying assholes do that! So I’m going to give it another try. I’m going to be a bit more aggressive about policing the comments, and probably write more longer, but less frequent posts, instead of trying to keep a pace of 3 or 4 per week like I used to.

To get started, I’m going to do some writing about computers.

Last week, my son was saying that he loves programming, and understands on the code level how a computer works, but he has no idea of how a bunch of electrons moving around actually make that happen.

My father was a semiconductor physicist, and when I was a kid, he spent a lot of time teaching me about math and physics, and he did a great job explaining how computers worked to me. I don’t remember exactly the way that he taught it, but I remember a lot of it, so I thought I’d try to pull it together with other material, and see if I could rebuild what he taught me.

As usual, we need to start with some basics. To be clear, a lot of what I’m going to say is an extreme simplification. This isn’t a simple area – the real, full depth of it involves pretty deep quantum physics, which I don’t pretend to understand. And even for professionals, there’s a lot of craziness and inaccuracy in how things are explained and taught.

## Conduction

The most basic thing to understand is: what is an electrical circuit? What does it mean for something to conduct electricity? From there, we can start building up our understanding of basic components like resistors and diodes and transistors, and then how to put those together into the building blocks of computers.

We all know from school that matter is made up of atoms. The atom has a core called the nucleus which is made of a collection of positively charged protons, and neutral (chargeless) neutrons. Whizzing around the nucleus are a bunch of tiny negatively charged particles called electrons. (Remember what I said about simplifications? This is very much one of them.)

The electrons aren’t just whizzing about willy nilly – there’s a structure to their behavior, which is described by some of the most introductory parts of quantum physics. Electrons fall into specific states around a nucleus. The states are divided into shells, where the first shell is closest to the nucleus; the second shell is a little further, and so on. Every electron associated with an atom is associated with one shell. An electron can only be in a shell – it’s impossible to be in between shells; that’s what the quantum of quantum physics means: there are discrete states with nothing between them.

Each of the shells can contain a certain number of electrons. The first shell can contain at most two electrons; the second shell can contain eight electrons; the third can contain 18 electrons.

Once a given shell is complete, it becomes very stable. For example, helium is an atom with two protons, and two electrons. It’s only got one shell of electrons, and that shell is complete (full). That means that it’s very difficult to add or remove electrons from a helium atom, and helium won’t form compounds with other atoms.

The inner, complete shells of an atom are almost inert – you can’t get the electrons in them to move around between different atoms, or to be bound to multiple atoms in a chemical bond. Only the outmost shell – called the valence shell really interacts.

Now we get to another of those simplifications. There’s a whole area of theory here that I’m just handwaving to keep this comprehensible.

In addition to the valence shell, there’s something called the conduction band. The conduction band is the next electron shell out from the valence shell. When energy is added to an atom, it can push electrons upwards by a shell level in a process called excitement. When an excited electron is in the conduction band, it’s got enough energy to move around between different atoms.

The conductivity of a material is determined by the difference between the energy band of its valence electron shell and its conduction band. When the valence shell and the conduction band overlap, the material conducts electricity well, and it’s called a conductor. All metals are conductors. When there’s a significant separation between the valence shell and conduction band, it’s a non-conductor. Most non-metals fit into this category. Finally, there’s a third group of materials that fits in between – where its valence shell and its conduction band are close, but not overlapping. These are the semiconductors. We’re going to talk a lot about them, but we’ll come back to that in a little bit. Before we can really talk about them, we need to understand how an electrical circuit works.

So let’s think about a simple circuit – a battery connected to a light bulb. We’ve got two terminals – a positive terminal, and a negative terminal, with different electrical fields. If there’s a path that electrons can move through between the terminals, then there will be an electromotive force produced by the field that pushes the electrons from the negative to the positive until the fields are equalized. In between, we have the light bulb, which contains something that isn’t a very good conductor – but there’s that electromotive force pushing the electrons. If it’s strong enough to push through the weak conductor, it will heat it up, and cause it to glow, consuming some of the energy that’s contained in the difference between the fields of the battery terminals. So electrons get pushed from the negative terminal through the wire, through the bulb, and all the way to the positive terminal. In this circuit, we call the strength of the difference between the electrical fields of the terminals voltage, and we call the number of electrons being moved current.

We tend to think of it as if electrons are moving from the negative terminal to the positive, but that’s not really true. Electrons don’t move very far at all. But the force propagates through the cloud of electrons in a conductor at nearly the speed of light, and since individual electrons are indistinguishable, by convention we just say that an electron moves through the circuit, when what actually moves is something more like a wave of force produced by the differing electrical fields.

## Semiconductors

The behavior of semiconductors is at the heart of a lot of modern electronics. But we don’t really use them in their pure state. What makes semiconductors particularly valuable is that we can change their properties and conductivity using a process called doping. Doping is just impregnating the crystal structure of a semiconductor with another atom (callod a dopant) that changes the way it behaves. The crystal structure of semiconductors provide two ways of doping that are relevant to computers: N-doping, and P-doping.

Doping converts a semiconductor to a conductor – but a conductor with particular properties that depend on the dopant.

Let’s look at a model of a silicon crystal. This isn’t really how it looks – it’s a 2D model to be readable (but this is the standard way that it’s illustrated in textbooks.) Each silicon atom has 4 valence electrons. So it forms into a crystal where each silicon atom is surrounded by 4 other silicon atoms, and each atom of silicon shares one of its electrons with each of its neighbors – so that the crystal behaves almost as if the valence shells were complete.

When we N-dope silicon (producing an N-type semiconductor), we add something that changes the way that electrons get shared in the crystal structure so that some of the electrons are more mobile. For example, a really common N-dopant is Phosphorus. Phosphorus has 5 valence electrons. When it gets infused into a silicon crystal, each atom of phosporus is surrounded by four atoms of silicon. The atoms share electrons in the same way as in a pure silicon atom – each atom shares an electron pair with each of its four neighbors. But the phosphorus atom has an extra valence electron which isn’t paired. That unpaired electron can easily jump around – with it and the other semi-free electrons around phosphorus atoms in the crystal lattice behaving almost like the cloud electrons in a metal. When you apply a force using an electric field, the free electrons will move in response to that force – the semiconductor is now a peculiar conductor.

When we P-dope silicon (producing, you guessed it! – a P-type semiconductor, we’re doing something similar – we’re creating a “free” charge unit that can move around in the crystal, but we’re doing in in a sort-of opposite direction. We introduce something which doesn’t have quite enough electrons to fit perfectly into the silicon lattice. For example, Boron is commonly used as a P-dopant. Boron only has 3 valence electrons. So when it’s integrated into a silicon crystal, it shares a valence electron with three of its neighbors – but there’s one missing – a hole where an electron can fit. We can think of that hole as a pseudo-particle with a positive charge. In the same way that the “free” electron of the N-doped silicon can move around the crystal, this free electron hole can move around the crystal – but it moves in the opposite direction than an electron would.

An important thing to understand here is that doped silicon is still electrically neutral. A piece of N-doped silicon doesn’t have a negative charge, and P-doped silicon doesn’t have a positive charge. Doping the silicon hasn’t changed the fact that the charges are balanced – the silicon is has a neutral charge both before and after doping! Instead, N-doping has converted a semiconductor to a conductor that allows electrons to move through the crystalline lattice; and P-doping has converted it to a conductor that allows positive charges to flow through the lattice.

N-type and P-type silicon by themselves aren’t all that interesting. They are semiconductors that have been chemically altered to conduct electricity better, but they’re not as good at conducting as a nice piece of metal. What makes them interesting is what happens when you start to put them together.

The simplest example (or, at least, the simplest one that I understand!) example is a diode, which is just a piece of N-doped silicon connected to a piece of P-doped silicon.

Where they contact, a bunch of electrons from the N-type silicon will jump across the gap to fill holes in the P-type silicon. This creates a region along the boundary that doesn’t have free electrons or holes, This region is called the depletion zone. Some of the holes on the P-side are filled by electrons from the N-side, so that now, on the P side, we’ve actually got a slight negative charge, and on the N side, we’ve got a slight positive charge. This creates an electric field that prevents more electrons on the N side from crossing the depletion zone to fill holes on the P side.

What happens if we apply a voltage to the negative side? This pushes electrons to move – and with enough force, they’ll start to jump the gap created by the depletion zone. It takes some voltage to get it started, because of the electrical field around the depletion zone, but with some force (typically a bit less than a volt), the electrons will start moving, almost as if you had an open circuit.

But what happens if you reverse the terminals? Then you have a force pushing electrons in the opposite direction. We’ve got an electrical field trying to push electrons into the P side of the diode. Electrons start filling holes in the P-type side, which just increases the size of the depletion zone, making it even harder to push a current across that gap. The more voltage we apply, the larger the depletion zone gets, and the more voltage we would need to apply to get a current across.

The diode conducts electricity from N to P, but not from P to N. By combining N and P type silicon, we’re able to create a one-way valve – a component which only conducts current in one direction!

This is the first step towards a computer: control how electrons move through a circuit, preventing them from following certain paths.

Next time we’ll look at transistors, which are where things start getting dynamic! The transistor is a switch without moving parts, and it’s the heart of modern electronics.

## References

I’ve been looking at a lot of places to help put this together. Here’s some good links that I used. I’m sure I missed some, so I apologize in advance!

# Deceptive Statistics and Elections

I don’t mean to turn by blog into political nonsense central, but I just can’t pass on some of these insane arguments.

This morning, the state of Texas sued four other states to overturn the results of our presidential election. As part of their suit, they included an "expert analysis" that claims that the odds of the election results in the state of Georgia being legitimate are worse that 1 in one quadrillion. So naturally, I had to take a look.

Here’s the meat of the argument that they’re claiming "proves" that the election results were fraudulent.

I tested the hypothesis that the performance of the two Democrat candidates were statistically similar by comparing Clinton to Biden. I use a Z-statistic or score, which measures the number of standard deviations the observation is above the mean value of the comparison being made. I compare the total votes of each candidate, in two elections and test the hypothesis that other things being the same they would have an equal number of votes.
I estimate the variance by multiplying the mean times the probability of the candidate not getting a vote. The hypothesis is tested using a Z-score which is the difference between the two candidates’ mean values divided by the square root of the sum of their respective variances. I use the calculated Z-score to determine the p-value, which is the probability of finding a test result at least as extreme as the actual results observed. First, I determine the Z-score comparing the number of votes Clinton received in 2016 to the number of votes Biden received in 2020. The Z-score is 396.3. This value corresponds to a confidence that I can reject the hypothesis many times more than one in a quadrillion times that the two outcomes were similar.

This is, to put it mildly, a truly trashy argument. I’d be incredibly ashamed if a high school student taking statistics turned this in.

What’s going on here?

Well, to start, this is deliberately bad writing. It’s using a whole lot of repetitive words in confusing ways in order to make it sound complicated and scientific.

I can simplify the writing, and that will make it very clear what’s going on.

I tested the hypothesis that the performance of the two Democrat candidates were statistically similar by comparing Clinton to Biden. I started by assuming that the population of eligible voters, the rates at which they cast votes, and their voting preferences, were identical for the two elections. I further assumed that the counted votes were a valid random sampling of the total population of voters. Then I computed the probability that in two identical populations, a random sampling could produce results as different as the observed results of the two elections.

As you can see from the rewrite, the "analysis" assumes that the voting population is unchanged, and the preferences of the voters are unchanged. He assumes that the only thing that changed is the specific sampling of voters from the population of eligible voters – and in both elections, he assumes that the set of people who actually vote is a valid random sample of that population.

In other words, if you assume that:

1. No one ever changes their mind and votes for different parties candidates in two sequential elections;
2. The population and its preferences never changes – people don’t move in and out of the state, and new people don’t register to vote;
3. The specific people who vote in an election is completely random.

Then you can say that this election result is impossible and clearly indicates fraud.

The problem is, none of those assumptions are anywhere close to correct or reasonable. We know that people’s voting preference change. We know that the voting population changes. We know that who turns out to vote changes. None of these things are fixed constants – and any analysis that assumes any of these things is nothing but garbage.

But I’m going to zoom in a bit on one of those: the one about the set of voters being a random sample.

When it comes to statistics, the selection of a sample is one of the most important, fundamental concerns. If your sample isn’t random, then it’s not random. You can’t compare results for two samples as if they’re equivalent if they aren’t equivalent.

Elections aren’t random statistical samples of the population. They’re not even intended to be random statistical samples. They’re deliberately performed as counts of motivated individual who choose to come out and cast their votes. In statistical terms, they’re a self-selected, motivated sample. Self-selected samples are neither random nor representative in a statistical sense. There’s nothing wrong with that: an election isn’t intended to be a random sample. But it does mean that when you do statistical analysis, you cannot treat the set of voters as a random sampling of the population of elegible voters; and you cannot make any assumptions about uniformity when you’re comparing the results of two different elections.

If you could – if the set of voters was a valid random statistical sample of an unchanging population of eligible voters, then there’d be no reason to even have elections on an ongoing basis. Just have one election, take its results as the eternal truth, and just assume that every election in the future would be exactly the same!

But that’s not how it works. And the people behind this lawsuit, and particularly the "expert" who wrote this so-called statistical analysis, know that. This analysis is pure garbage, put together to deceive. They’re hoping to fool someone into believing that they actually prove something that they couldn’t prove.

And that’s despicable.

# Herd Immunity

With COVID running rampant throughout the US, I’ve seen a bunch of discussions about herd immunity, and questions about what it means. There’s a simple mathematical concept behind it, so I decided to spend a bit of time explaining.

The basic concept is pretty simple. Let’s put together a simple model of an infectious disease. This will be an extremely simple model – we won’t consider things like variable infectivity, population age distributions, population density – we’re just building a simple model to illustrate the point.

To start, we need to model the infectivity of the disease. This is typically done using the name $R_0$. $R_0$ is the average number of susceptible people that will be infected by each person with the disease.

$R_0$ is the purest measure of infectivity – it’s the infectivity of the disease in ideal circumstances. In practice, we look for a value $R$, which is the actual infectivity. $R$ includes the effects of social behaviors, population density, etc.

The state of an infectious disease is based on the expected number of new infections that will be produced by each infected individual. We compute that by using a number S, which is the proportion of the population that is susceptible to the disease.

• If R S < 1, then the disease dies out without spreading throughout the population. More people can get sick, but each wave of infection will be smaller than the last.
• If R S = 1, then the disease is said to be endemic. It continues as a steady state in the population. It never spreads dramatically, but it never dies out, either.
• If R S > 1, then the disease is pandemic. Each wave of infection spreads the disease to a larger subsequent wave. The higher the value of $R$ in a pandemic, the faster the disease will spread, and the more people will end up sick.

There are two keys to managing the spread of an infectious disease

1. Reduce the effective value of $R$. The value of $R$ can be affected by various attributes of the population, including behavioral ones. In the case of COVID-19, an infected person wearing a mask will spread the disease to fewer others; and if other people are also wearing masks, then it will spread even less.
2. Reduce the value of $S$. If there are fewer susceptible people in the population, then even with a high value of $R$, the disease can’t spread as quickly.

The latter is the key concept behind herd immunity. If you can get the value of $S$ to be small enough, then you can get $R * S$ to the sub-endemic level – you can prevent the disease from spreading. You’re effectively denying the disease access to enough susceptible people to be able to spread.

Let’s look at a somewhat concrete example. The $R_0$ for measles is somewhere around 15, which is insanely infectious. If 50% of the population is susceptible, and no one is doing anything to avoid the infection, then each person infected with measles will infect 7 or 8 other people – and they’ll each infect 7 or 8 others – and so on, which means you’ll have epidemic spread.

Now, let’s say that we get 95% of the population vaccinated, and they’re immune to measles. Now $R * S = 15 * 0.05 = 0.75$. The disease isn’t able to spread. If you had an initial outbreak of 5 infected, then they’ll infect around 3 people, who’ll infect around 2 people, who’ll infect one person, and soon, there’s no more infections.

In this case, we say that the population has herd immunity to the measles. There aren’t enough susceptible people in the population to sustain the spread of the disease – so if the disease is introduced to the population, it will rapidly die out. Even if there are individuals who are still susceptible, they probably won’t get infected, because there aren’t enough other susceptible people to carry it to them.

There are very few diseases that are as infectious as measles. But even with a disease that is that infectious, you can get to herd immunity relatively easily with vaccination.

Without vaccination, it’s still possible to develop herd immunity. It’s just extremely painful. If you’re dealing with a disease that can kill, getting to herd immunity means letting the disease spread until enough people have gotten sick and recovered that the disease can’t spread any more. What that means is letting a huge number of people get sick and suffer – and let some portion of those people die.

Getting back to COVID-19: it’s got an $R_0$ that’s much lower. It’s somewhere between 1.4 and 2.5. Of those who get sick, even with good medical care, somewhere between 1 and 2% of the infected end up dying. Based on that $R_0$, herd immunity for COVID-19 (the value of S required to make R*S<1) is somewhere around 50% of the population. Without a vaccine, that means that we’d need to have 150 million people in the US get sick, and of those, around 2 million would die.

(UPDATE: Ok, so I blew it here. The papers that I found in a quick search appear to have a really bad estimate. The current CDC estimate of $R_0$ is around 5.7 – so the S needed for herd immunity is significantly higher – upward of 80%, and so the would the number of deaths.)

A strategy for dealing with an infection disease that accepts the needless death of 2 million people is not exactly a good strategy.

# Election Fraud? Nope, just bad math

My old ScienceBlogs friend Mike Dunford has been tweeting his way through the latest lawsuit that’s attempting to overturn the results of our presidential election. The lawsuit is an amazingly shoddy piece of work. But one bit of it stuck out to me, because it falls into my area. Part of their argument tries to make the case that, based on "mathematical analysis", the reported vote counts couldn’t possibly make any sense.

The attached affidavit of Eric Quinell, Ph.D. ("Dr. Quinell Report) analyzez the extraordinary increase in turnout from 2016 to 2020 in a relatively small subset of townships and precincts outside of Detroit in Wayne County and Oakland county, and more importantly how nearly 100% or more of all "new" voters from 2016 to 2020 voted for Biden. See Exh. 102. Using publicly available information from Wayne County andOakland County, Dr. Quinell found that for the votes received up to the 2016 turnout levels, the 2020 vote Democrat vs Republican two-ways distributions (i.e. excluding third parties) tracked the 2016 Democrat vs. Republican distribution very closely…

This is very bad statistical analysis – it’s doing something which is absolutely never correct, which is guaranteed to produce a result that looks odd, and then pretending that the fact that you deliberately did something that will produce a certain result means that there’s something weird going on.

Let’s just make up a scenario with some numbers up to demonstrate. Let’s imagine a voting district in Cosine city. Cosine city has 1 million residents that are registered to vote.

In the 2016 election, let’s say that the election was dominated by two parties: the Radians, and the Degrees. The radians won 52% of the vote, and the Degrees won 48%. The voter turnout was low – just 45%.

Now, 2020 comes, and it’s a rematch of the Radians and the Degrees. But this time, the turnout was 50% of registered votes. The Degrees won, with 51% of the vote.

So let’s break that down into numbers for the two elections:

• In 2016:
• A total of 450,000 voters actually cast ballots.
• The Degrees got 216,000 votes.
• In 2020:
• A total of 500,000 voters actually cast ballots.</li>
• The Degrees got 255,000 votes.</li>

Let’s do what Dr. Quinell did. Let’s look at the 2020 election numbers, and take out 450,000 votes which match the distribution from 2016. What we’re left with is:

• 39,000 new votes for the Degrees.

There was a 3 percent shift in the vote, combined with an increase in voter turnout. Neither of those is unusual or radically surprising. But when you extract things in a statistically invalid way, we end up with a result that in a voting district which the vote for the two parties usually varies by no more than 4%, the "new votes" in this election went nearly 4:1 for one party.

If we reduced the increase in voter turnout, that ratio becomes significant worse. If the election turnout was 46%, then the numbers would be 460,000 total votes; 225,400 for the Radians and 234,600 for the Degrees. With Dr. Quinell’s analysis, that would give us: -9,000 votes for the Radians, and +18,000 votes for the Degrees. Or since negative votes don’t make sense, we can just stop at 225,400, and say that all of the remaining votes, every single new vote beyond what the Radians won last time, was taken by the Degrees. Clearly impossible, it must be fraud!

So what’s the problem here? What caused this reasonable result to suddenly look incredibly unlikely?

The votes are one big pool of numbers. You don’t know which data points came from which voters. You don’t know which voters are new versus old. What happened here is that the bozo doing the analysis baked in an invalid assumption. He assumed that all of the voters who voted in 2016 voted the same way in 2020.

"For the votes received up to the turnout level" isn’t something that’s actually measurable in the data. It’s an assertion of something without evidence. You can’t break out subgroups within a population, unless the subgroups were actually deliberately and carefully measured when the data was gathered. And in the case of an election, the data that he’s purportedly analyzing doesn’t actually contain the information needed to separate out that group.

You can’t do that. Or rather you can, but the results are, at best, meaningless.

# The Futility of Pi Denialism

To me, the strangest crackpots I’ve encountered through this blog are the π denialists.

When people have trouble with Cantor and differently sized infinities, I get it. It defies our intuitions. It doesn’t seem to make sense.

When you look at Gödel incompleteness theorem – it’s really hard to wrap your head around. It doesn’t seem to make sense. I get it.

When you talk about things like indescribable numbers, it’s crazy. How could it possibly be true? I get it.

But π?

It’s a pretty simple number: the ratio of the diameter of the circle and the circle’s circumference. There’s nothing all that difficult about what it means. And there are so many different ways of calculating it! We can use the nature of a circle, and derive series that compute it. We can write simple programs, do tactile demos, measure actual physical phenomena. And yet, there are people who fervently believe that it’s all a sham: that the value of π isn’t what we say it is. It’s exactly 4. Or it’s exactly 22/7. Or it’s exactly $\frac{4}{\phi^{\frac{1}{2}}}$. Or it’s not a number, it’s an acceleration.

It’s amazing. I constantly get mail – mostly from fans of either Jain (the author of the &phi;-based &pi; mentioned above), or from followers of Miles Mathis (he of “&pi; isn’t a ratio, it’s an acceleration” fame), insisting that I’m part of the great mathematical conspiracy to deny the true factual value of &pi;.

And yet… It’s so simple to demonstrate how wrong that is.

My favorite version is a simple program.

Here’s the idea, followed by the code.

• Take the unit square – the region of the graph from (0, 0) to (1, 1), and inside of it, an arc of the circle of radius 1 around (0,0).
• Pick a random point, (x, y), anywhere inside of that square.
• If the distance from the origin ($x^2 + y^2$) is less than one, then the point is inside the circle. If it isn’t, then it’s outside of the circle.
• The probability, $p$, of any given random point being inside that circle is equal to the ratio of the area of the circle to the area of the square. The area of that region of the circle is: $\pi*1^2/4$, and the area of the the square is $1^2$. So the probability is $(1/4)\pi/1$, or $\pi/4$.
• So take a ton of random points, and count how many are inside the circle.
• The ratio of points inside the circle to total random points is $\pi/4$. The more random points you do this with, the closer you get to π.

We can turn that into a simple Python program:

from random import random

def computePi(points):
inside = 0
for i in range(points):
x = random()
y = random()
if (x*x + y*y) < 1.0:
inside = inside + 1
return (inside*1.0)/points * 4.0

for i in range(30):
pi = computePi(2**i)
print(f"Pi at 2**{i} iterations = {pi}")


The exact value that you’ll get when you run this depends on the random number generator, and the initial seed value. If you don’t specify a seed, most random number libraries will use something like last 32 digits of the current system time in nanoseconds, so you’ll get slightly different results each time you run it. I just ran it, and got:

Pi at 2**0 iterations = 4.0
Pi at 2**1 iterations = 4.0
Pi at 2**2 iterations = 3.0
Pi at 2**3 iterations = 2.0
Pi at 2**4 iterations = 3.5
Pi at 2**5 iterations = 2.75
Pi at 2**6 iterations = 3.0625
Pi at 2**7 iterations = 3.125
Pi at 2**8 iterations = 3.109375
Pi at 2**9 iterations = 3.1875
Pi at 2**10 iterations = 3.171875
Pi at 2**11 iterations = 3.126953125
Pi at 2**12 iterations = 3.12109375
Pi at 2**13 iterations = 3.14013671875
Pi at 2**14 iterations = 3.169677734375
Pi at 2**15 iterations = 3.1324462890625
Pi at 2**16 iterations = 3.14453125
Pi at 2**17 iterations = 3.147247314453125
Pi at 2**18 iterations = 3.138519287109375
Pi at 2**19 iterations = 3.1364669799804688
Pi at 2**20 iterations = 3.1443214416503906
Pi at 2**21 iterations = 3.141223907470703
Pi at 2**22 iterations = 3.141301155090332
Pi at 2**23 iterations = 3.1419320106506348
Pi at 2**24 iterations = 3.1415367126464844
Pi at 2**25 iterations = 3.1421539783477783
Pi at 2**26 iterations = 3.1420511603355408
Pi at 2**27 iterations = 3.1415300369262695
Pi at 2**28 iterations = 3.141532242298126
Pi at 2**29 iterations = 3.1415965482592583


I suspect that I could do a lot better using a special number library to reduce or eliminate the floating point roundoff errors, but I don’t really think it’s worth the time. Just this much, using a really simple, obvious, intuitive method produces a better result than any of the numbers pushed by the crackpots.

To support that previous statement: the best crackpot value for π is the one based on the golden ratio. That version insists that the true value of π is 3.14460551103. But you can see – by using the simple metric of counting points inside and outside the circle – that the actual value is quite different from that.

That’s what makes this breed of denialism so stupid. π isn’t complicated: it’s a simple ratio. And it’s easy to test using simple concepts. Pi relates the diameter (or radius) of a circle to the circumference or area of that circle. So any test that works with circles can easily show you what π is. There’s nothing mysterious or counterintuitive or debatable about it. It is what it is, and you can test it yourself.

# Abusing Linear Regression to Make a Point

A bunch of people have been sending me links to a particularly sloppy article that (mis)uses linear regression to draw an incorrect conclusion from some data. So I guess I’ve got to got back to good-old linear regression, and talk about it a bit.

If you have a collection of data – typically data with one independent variable, and one dependent variable (that is, the first variable can vary any way it wants; changing it will change the second variable), then you’re probably interested in how the dependent variable relates to the independent. If you have reason to believe that they should have a linear relationship, then you’d like to know just what that linear relationship is.

If your data were perfect, then you’d just need to plot all of the data points on a graph, with the independent variable on the X axis, and the dependent on the Y, and then your graph would be a line, and you could get its slope and Y intercept, and thus completely capture the relationship.

But data is never perfect. There’s a lot of reasons for that, but no real set of collected data is ever perfect. No matter how perfect the real underlying linear relationship is, real measured data will always show some scatter. And that means that you can draw a lot of possible lines through the collected data. Which one of them represents the best fit?

Since that’s pretty abstract, I’m going to talk a bit about an example – the very example that was used to ignite my interest in math!

Back in 1974 or so, when I was a little kid in second grade, my father was working for RCA, as a physicist involved in manufacturing electronics for satellite systems. One of the important requirements for the products they were manufacturing was that they be radiation hard – meaning that they could be exposed to quite a bit of radiation before they would be damaged enough to stop working.

Their customers – NASA, JPL, and various groups from the U. S. Military, had very strong requirements. They had to show, for a manufacturing setup of a particular component, what the failure profile was.

The primary failure mode of these chips they were making was circuit trace failure. If a sufficiently energetic gamma ray hit one of the circuit traces, it was possible that the trace would burn out – breaking the circuit, and causing the chip to fail.

The test setup that that they used had a gamma ray emitter. So they’d make a manufacturing run to produce a batch of chips from the setup. Then they’d take those, and they’d expose them to increasing doses of radiation from the gamma emitter, and detect when they failed.

For trace failure, the probability of failure is linear in the size of the radiation dose that the chip is exposed to. So to satisfy the customer, they had to show them what the slope of the failure curve was. “Radiation hard” was defined as being able to sustain exposure to some dose of radiation with a specified probability of failure.

So, my dad had done a batch of tests, and he had a ton of little paper slips that described the test results, and he needed to computer the slop of that line – which would give the probability of failure as a multiple of the radiation dose.

I walked into the dining room, where he was set up doing this, and asked what he was doing. So he explained it to me. A lot like I just explained above – except that my dad was a much better teacher than me. I couldn’t explain this to a second or third grader the way that he did!

Anyway… The method that we use to compute the best line is called least squares. The intuition behind it is that you’re trying to find the line where the average distance of all of the datapoints from that line is the smallest. But a simple average doesn’t work well – because some of the data points are above the line, and some are below. Just because one point is, say, above a possible fit by 100, and another is below by 100 doesn’t mean that the two should cancel. So you take the distance between the data points and the line, and you square them – making them all positive. Then you find the line where that total is the smallest – and that’s the best fit.

So let’s look at a real-ish example.

For example, here’s a graph that I generated semi-randomly of data points. The distribution of the points isn’t really what you’d get from real observations, but it’s good enough for demonstration.

The way that we do that is: first we compute the means of $x$ and $y$, which we’ll call $\overline{x}$ and $\overline{y}$. Then using those, we compute the slope as:

$m = \frac{\Sigma_{i=1}^n (x-\hat{x})(y-\hat{y})}{\Sigma_{i=1}^{n} (x-\hat{x})^2}$

Then for the y intercept: $b = \hat{y} - m\hat{x}$.

In the case of this data: I set up the script so that the slope would be about 2.2 +/- 0.5. The slope in the figure is 2.54, and the y-intercept is 18.4.

Now, we want to check how good the linear relationship is. There’s several different ways of doing that. The simplest is called the correlation coefficient, or $r$.

$r = \frac{\left(\Sigma (x-\hat{x})\right) \left(\Sigma (y - \hat{y})\right)}{\sqrt{ \left(\Sigma (x-\hat{x})^2\right) \left(\Sigma (y - \hat{y})^2\right) }}$

If you look at this, it’s really a check of how well the variation between the measured values and the expected values (according to the regression) match. On the top, you’ve got a set of products; on the bottom, you’ve got the square root of the same thing squared. The bottom is, essentially, just stripping the signs away. The end result is that if the correlation is perfect – that is, if the dependent variable increases linearly with the independent, then the correlation will be 1. If the dependency variable decreases linearly in opposition to the dependent, then the correlation will be -1. If there’s no relationship, then the correlation will be 0.

For this particular set of data, I generated it with a linear equation with a little bit of random noise. The correlation coefficient is slighly greater than 0.95, which is exctly what you’d expect.

Ok, so that’s the basics of linear regression. Let’s get back to the bozo-brained article that started this.

They featured this graph:

You can see the scatter-plot of the points, and you can see the line that was fit to the points by linear regression. How does that fit look to you? I don’t have access to the original dataset, so I can’t check it, but I’m guessing that the correlation there is somewhere around 0.1 or 0.2 – also known as “no correlation”.

You see, the author fell into one of the classic traps of linear regression. Look back at the top of this article, where I started explaining it. I said that if you had reason to believe in a linear relationship, then you could try to find it. That’s the huge catch to linear regression: no matter what data you put in, you’ll always get a “best match” line out. If the dependent and independent variables don’t have a linear relation – or don’t have any actual relation at all – then the “best match” fit that you get back as a result is garbage.

That’s what the graph above shows: you’ve got a collection of data points that to all appearances has no linear relationship – and probably no direct relationship at all. The author is interpreting the fact that linear regression gave him an answer with a positive slope as if that positive slope is meaningful. But it’s only meaningful if there’s actually a relationship present.

But when you look at the data, you don’t see a linear relationship. You see what looks like a pretty random scatterplot. Without knowing the correlation coefficient, we don’t know for sure, but that line doesn’t look to me like a particularly good fit. And since the author doesn’t give us any evidence beyond the existence of that line to believe in the relationship that they’re arguing for, we really have no reason to believe them. All they’ve done is demonstrate that they don’t understand the math that they’re using.