Name: Simulating the Evolution of Sacrificing for Family
Uploaded: 2025-11-19T01:58:34.000Z
Duration: 20 min 49 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

The basic idea of natural selection is that if a gene does an above average job at helping its host creatures survive and reproduce, it'll tend to become more common as creatures compete with each other over the generations.

Basic passive voice

This tendency to survive and reproduce is called fitness, and

you'll often hear natural selection summarized as survival of the fittest.

But one day, a clever, newly mutated gene realized another strategy was possible.

If copies of the gene exist in multiple different creatures, the gene can do better by causing the creatures to help each other rather than mercilessly competing with each other.

This concept is known as inclusive fitness.

The only problem is the copies of a gene are hidden inside the creatures and they can't see each other.

So if the copies of the gene want to help each other, they have to gamble, sometimes helping the competing genes by accident.

So the question is, how can a gene strategize around inclusive fitness without the whole plan backfiring?

The condition for making inclusive fitness gambling work is called Hamilton's Rule, and

It's a pretty simple looking inequality, and

if you've learned about Hamilton's rule before, you might be wondering why the heck this video is so long.

Well, it turns out that the simplicity of Hamilton's rule is a bit deceptive, or at least it deceived me at first.

So this video is going to go slowly, starting with the general requirement for gambling to be beneficial and then applying that idea to simulations of increasingly complex biological systems.

And when it does, you might not realize it.

It'll get fixed before the end of the video, but

consider it a challenge to follow closely enough to see what's going wrong before I tell you about it.

Let's let this blob play for a bit while we try to predict what will happen.

Okay, so there are six possible outcomes, and

we can summarize each with an equation where the payout minus the cost equals the net gain.

If the die lands on a number from 1 to 5, the payout is 0 and the cost is 1, leading to a net gain of negative 1 in each case, or a loss of 1, if you prefer to say it that way.

If the die lands on a six, though, the payout is seven.

So the net gain for that case is six on a fair die.

These six possible results are all equally likely.

So we can combine the equations together and divide everything by six to get the average payout cost and net gain.

One way to read this equation is to say you have a one in six chance to win the benefit of seven coins.

And it costs one coin to play, so you expect to win one sixth of the coin per time you play.

The coins only come in whole numbers, but the average is a fraction.

An average predicted result like this has a special name.

And since the expected value here is positive, the blob should gain money over time by continuing to play.

It looks like the blob is actually doing way better than 1/6 of a coin on average.

That's good for the blob, but not so good for our prediction.

It's only been a few rolls, though, so maybe the blob is just getting lucky.

If our prediction is good, it should start to look more accurate with a larger number of rolls.

So let's enlist some more blobs to see if that happens.

It can sometimes take surprisingly long for random events to even out, which is my favorite excuse whenever I'm getting my butt kicked in a board game.

We can use variables to write this out in a way that will apply to a broader set of situations.

The benefit of winning times the probability of winning minus the cost to play is the expected value of a gambling game.

If the expected value is positive, it's usually a good idea to gamble.

And when the expected value is negative, like.

Like in every single casino game, for example, you should expect to lose money.

You might notice that this looks basically the same as Hamilton's rule already.

So let's try applying it to a biology simulation to see how it works.

We'll start with the same basic structure from the video on Green Beard Altruism.

Each day, blobs will go out to the forest to eat some fruit.

Some of the trees have predators in them, though.

And if the blobs are unlucky enough to visit a tree with a predator in it, they'll think get eaten.

But if they don't get eaten, they'll head to one of the homes and reproduce, creating a puddle of three new identical blobs in their place.

And yes, a group of blob siblings is officially called a puddle.

And after the blobs reproduce, the cycle repeats.

Also, when it's time to go home, there might not be enough homes to go around.

In which case some randomly determined blobs won't get to go home and reproduce.

In case you've seen that Greenbeard video, I want to highlight the key difference here.

This time, the siblings always stick together in their puddle when they go out to a tree.

let's make this a bit bigger and let it run for a while to understand how the simulation works in this baseline case.

Alright, it turns out that the blobs thrive in this environment and the limiting factor ends up being the supply of houses.

The blobs should really figure out a way to build more housing, but

comments like that belong in the economic series.

Okay, now let's add the ability to be altruistic again.

This will be pretty similar to what we saw in the Greenbeard video.

When the blobs go to a tree with a predator in it, one of them will notice the predator and it'll have two options for how to react.

The first option is to just run away, leaving the other blobs to die.

The second option will be to make a bunch of noise to warn the other blobs, but

in the process attract the predator's attention and end up getting eaten.

Because these blobs are sacrificing themselves to help others.

The behavior will be controlled by a gene which has two versions or alleles.

The cowardly allele will be blue, and the altruism allele will be orange.

The blobs can't actually see the genes, but

we're going to use these colors to graph the frequency of each allele over time.

I mention this in basically every video, but I can't say enough how.

In real life, the cause of behaviors is very complex.

And the genetic part itself is a lot more complicated than this model we're using here, where just one gene controls this one kind of behavior.

We're just using a simple model to explore one small piece of the very large picture of behavior.

But with that said, let's run this simulation.

We'll start with equal numbers of each allele.

Before we hit go, though, what do you think will happen?

Try using that expected value idea we talked about earlier.

And keep in mind that the puddles stay together when they go to find food.

This graph shows the frequencies of each allele over time, stacked on top of each other at the beginning it's half and half.

But over time, the orange altruism allele drove the other allele to extinction.

Let's see if we can make sense of why the altruism allele did so well.

Just like before, we'll look at the benefit of the behavior times the probability of getting the benefit, minus the cost of the behavior to get the expected value.

But instead of coins, we're worried about copies of the altruism allele.