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The BONKERS Physics of Animal Swarms (Not Clickbait) - Video học tiếng Anh
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The BONKERS Physics of Animal Swarms (Not Clickbait)
The BONKERS Physics of Animal Swarms (Not Clickbait)
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0:00
When animals get together in large numbers, they often move in a very weird way.
0:04
Starlings might be the most famous for it.
0:07
Their individual swoops translate to massive undulations across the entire flock,
0:11
making the whole thing look choreographed and hypnotic.
0:15
But birds aren’t the only animals that move in this way.
0:18
Schools of fish, swarms of insects,
0:20
and dense crowds of people can display impressive coordination, too.
0:25
Which makes these mesmerizing patterns a tantalizing topic for the scientists
0:29
trying to figure out exactly how they work.
0:32
And not just so they can explain why animal swarms look like that,
0:35
or help design buildings with safer layouts.
0:38
Some of this research has even wound up in a famous Hollywood movie!
0:42
[♪ INTRO]
0:46
When starlings move in those weird flocks, it’s called a murmuration.
0:50
And murmurations are just one example of an emergent behavior.
0:53
Where each individual in a flock acts according to its own motivations, there’s also an entirely
0:59
different collective behavior that arises in response to all those individual actions.
1:05
You're an emergent system, turns out,
1:07
just a bunch of cells working together… and you have “thoughts”...
1:10
“but I have to pee”
1:12
“and are stressed”
1:14
"I have to get up tomorrow."
1:15
Now, since the movements of any one bird are fairly predictable, you might think scientists
1:19
can just extrapolate to predict what shape the overall murmuration will be at any given moment.
1:24
But no.
1:25
There’s a bunch of unpredictable chaos mixed in with all that determinism.
1:30
As such, physicists have become obsessed with emergent behavior,
1:34
and have created an entire subfield devoted to it, called active matter.
1:38
It’s clear that individual birds are communicating with each other somehow to coordinate the motion.
1:45
Although exactly how is left for the biologists to figure out, so we’ll set that aside for now.
1:49
Don’t worry, bio fans, we will be back for you.
1:52
Kind of like a like a game of Telephone,
1:53
the starlings seem like they only communicate with their direct neighbors.
1:57
But unlike you experienced back in grade school,
1:59
the message they’re passing along can make it across the entire flock without getting garbled.
2:05
But what kind of messages are being sent, and how far can they travel between individual birds?
2:10
It’s time to break out the mathematical models.
2:12
So, let’s start with some basic behavioral rules based on real-world observations:
2:17
First, individual birds avoid crowding their neighbors.
2:21
And second, an individual doesn’t want to be separated from its flock.
2:25
And third, individuals tend to end up facing the same direction as their neighbors.
2:30
Each bird also has a limited range of visibility,
2:33
so let’s assume each individual in the flock can only see its neighbors within
2:37
a couple of body-lengths of itself, and within a specific angular range.
2:42
Back in the 1980s, a computer scientist named Craig Reynolds used a similar
2:46
set of rules to model what he called bird-oid objects…or “boids” for short.
2:52
Although technically, it wasn’t just meant to model birds.
2:54
I love this, boids…
2:56
They’re boids!
2:57
The resulting algorithm was initially developed for use in computer animation, and by 1992, it was
3:03
ready for its silver screen debut: modeling flocks of penguins and bats in the movie Batman Returns.
3:09
I told you it’s a famous hollywood movie.
3:12
The boids algorithm has been pretty successful at replicating emergent behavior in flocks,
3:16
such as how small flocks of boids merge to form larger flocks,
3:20
and how a flock can split to avoid an obstacle like a tree or a predator.
3:24
And for more accuracy, you can even program your boids with a migratory urge, an ingrained
3:29
preferred direction inspired by a real life bird’s biological urge to fly south for the winter.
3:34
And I’m going to tell you more about this but much
3:36
like a basic cable showing of Batman Returns, we do have to take a break.
3:42
This SciShow video is supported by Zocdoc
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4:55
There’s a lot the boids algorithm still can’t account for.
4:58
It’s a purposefully oversimplified version of reality.
5:01
You can tune the rules and parameters within the algorithm, like the visibility range,
5:05
or “repulsion” and “attraction” forces between the individual boids.
5:09
Eventually, you’ll get simulation results that
5:11
mimic different animal swarms in the real world and their emergent behavior.
5:15
And by tweaking the model properties, scientists can inch toward figuring out
5:19
how individuals might sense and respond to their flockmates and environment.
5:24
But at the end of the day, they’re really just inferring rules that seem to work and
5:28
recreate the emergent properties of a real flock.
5:31
There could be lots of other parameters physicists aren’t accounting for, and there are
5:34
often multiple combinations of input parameters that lead to really similar simulation outputs.
5:40
But let’s give the physicists some credit: they aren’t only guessing.
5:43
They’ve taken measurements of birds, fish, and insects to see how individuals influence each
5:49
other, allowing them to identify and tune flocking simulation parameters.
5:53
For example, analyses of starling murmurations revealed that while the birds only interact
5:57
with their nearby neighbors, information still makes it across the entire flock.
6:01
So if simulated flocks can also communicate across the whole group,
6:05
then the model is on the right track.
6:06
Meanwhile, studies on groups of three fish versus shoals of 30 fish have found that the fish likely
6:12
receive signals from their closest neighbors and their flockmates that are farther away.
6:18
So adding more complicated terms to flocking
6:21
models like the boids algorithm could improve simulation results.
6:24
But despite all these insights,
6:25
they’re still just simplified rules that describe and reproduce flock phenomena.
6:30
They can’t account for all the complexities of animal behavior,
6:34
like individual motivations or decision making.
6:36
And we do have to be careful not to anthropomorphize these animals by assigning
6:40
them “goals” or “hopes” or “dreams”...unless they’re the ones in Batman Returns.
6:45
Clearly, did have goals...
6:47
So on the other side of the emergent behavior problem,
6:49
biologists are considering the neurobiology of how animals navigate.
6:53
Instead of defining “rules” that the animals follow, biologists have successfully simulated
6:58
flocking behaviors by modeling how animals encode spatial information in their brains.
7:03
Physics models like the boids algorithm assume
7:06
actions depend on where an individual is positioned relative to its neighbors.
7:10
That’s considered an egocentric way to navigate.
7:13
But biology studies have shown that, basically across the board, animals
7:17
also navigate in allocentric ways, meaning guided by external things in the landscape.
7:22
Egocentric and allocentric navigation are stored differently inside the brain.
7:27
And the allocentric flocking model suggests that when some animals swarm,
7:31
they are rapidly switching between egocentric and allocentric methods.
7:36
This means animals can probably sense where their flockmates are
7:39
relative to themselves and relative to the landscape and any nearby predators.
7:43
This combination of self-centered and landmark-oriented navigation
7:47
could be the biological key to producing emergent swarm behaviors.
7:52
One paper from 2025 has shown that models of this random,
7:55
rapid neurobiological perspective-switching can successfully recreate swarming behaviors
8:00
without needing to infer rules, like what happens in the boids algorithm.
8:05
These two different perspectives have brought us closer to understanding animal swarms,
8:09
but physicists and biologists will need to keep working together to understand active matter.
8:15
Because animal swarms aren’t the only systems governed by active matter principles.
8:20
Self-propelled robots, though not alive, act individually according to fixed rules.
8:25
And, much like starlings, they can display completely different collective behavior.
8:30
For example, these tiny hexbug robots that can turn a gear!
8:33
On the living side of things, cells collectively
8:36
migrate during biological processes, like embryonic development or wound healing.
8:41
This emergent motion is completely different from the motion of individual cells.
8:46
And don’t forget, humans are animals, too, but with even more complicated decisions to make.
8:51
Like whether or not to go see the Batman movie marathon at your local theater.
8:54
So active matter has also been useful for modeling human crowds.
8:58
Using similar rules as the boids algorithm,
9:00
physicists have created models to describe how people cross a crosswalk, form a mosh pit,
9:06
move through an exit, and create dangerous crowd crushes during moments of panic.
9:10
If we can design around that, we can prevent unnecessary injuries or worse.
9:16
Turns out those boids are pretty flocking useful.
9:19
[♪ OUTRO]
