Hello, and welcome to the Physics World Stories
podcast. I'm Andre Glesser. And in this episode,
we're gonna be exploring the physics of crowds.
Specifically,
or at least to begin with, looking at
the physics of sheep movement.
And whilst this is a slightly wooly topic,
and you will find the odd sheep based
joke in here,
there is also some really intriguing physics
not just a value to those who are
animal herders but anyone interested in animal or
human behavior
later in the podcast we'll hear from someone
who won for their work on human crowds
the ignobel prize
the prize given for research that makes you
laugh and then think but on the physics
world website you'll find an article by Philip
Ball a science writer based in London entitled
field work, the physics of sheep from phase
transitions
to collective motion.
And through a window out of which I
often gaze, there is a flock of sheep.
I must admit
at no point when I've looked at these
sheep have I thought of physics. So I
wondered
quite why it had occurred to Philip Paul.
So gazing out of that same window at
that flock of sheep, I gave him a
call.
I've had a long interest in how ideas
from physics can be,
can be used to understand
systems that don't sound at all like physics.
And
animal motion is one of them, actually, including
the motion of people.
So actually, years ago, 20 years ago, I
wrote a book about,
the this topic of how we could use
ideas from physics to understand
aspects of human society.
And, you know, as I say in the
piece, compared to trying to do that,
trying to understand sheep might seem like,
you know, an easy thing to do because,
they have somewhat less, sophistication
than, than humans.
But
it it's actually it's a very interesting problem
to understand how sheep herd, but also there's
this added twist that sheep are herded by
dogs.
You know? And that seems like a completely
kind of
idiosyncratic thing that is miles away from anything
physics could handle, but it turns out that
it isn't.
And these models that are used, they're ones
that have,
that sort of have their roots in work
on
particularly on more obviously
sort of collective flocking motions like, flocking of
birds, like the schooling of fish. Things that,
you know, when we look at them,
there seems we we just kind of intuit
that there has to be some
deeper principle going on that gives them this
kind of coherence,
that, you know, doesn't seem to come from
from individual
animals.
And in fact, I'd seen before I even
encountered these papers, I'd seen,
just, you know, video footage on on the
web of
sheep
fluids. So it's got the kind of these
aerial photos, probably drone photos of sheep,
her you know, wandering around in fields, speeded
up
and kind of going through gates and, you
know, going around obstacles and so on. And
it's really weird to see it because it
really does look like the flow of some
kind of, you know, slightly granular fluid.
So there too, if we see it if
we look at it on the right sort
of time scale and from the right perspective,
you know, again, there's this sort of intuitive
feeling that there's something there's some grand principle,
there's some physics in this system. That's what
this work that I was writing about is
trying to explore. So I say I'm looking
out the window, and there are a flock
of sheep. You know, there's walls,
there's
hedges,
There's occasionally the odd sheepdog. And at this
time of year, when the tourists have gone,
the sheep are sort of,
yeah, just left to their own devices. When
there are tourists here walking their dogs, the
sheep are scared all over the place. But
it's it's I've to be perfectly honest, as
a bit of a nerd, I have enjoyed
watching the sheep,
more at this time of year where they're
just left to their own devices, as I
say, and they have this kind of well,
yeah, they are a flock, aren't they?
Yeah. It is. And, you know, what so
so what does that mean? It, you know,
it means there's a a load of them,
but it means something more than that. It
means that they are
somehow interacting with an with each other
to stay coherent.
And that's something that
pretty much all,
groups of animals do or groups of living
organisms. Actually, even cells do that. You can
see it with bacteria. There are some people
who develop models like this to try to
understand bacteria and how they sort of flow
around, and they too can kind of sense
each other's presence.
And, you know, I I I kind of
we we if we think of how we
move around in space, we're clearly doing that
as well. We like to think, you know,
we're all individuals. But if we're moving down
a crowded pavement or something, then we are
interacting with others. We're certainly, we're trying to
avoid collisions if we're not staring at our
phones. We're trying to avoid bumping into,
each other.
But also, occasionally, we might be moving with
a group of friends or with family. And
so there's a kind of attraction, a kind
of cohesion there. We're trying to to stick
together.
So, you know, there are these these principles
that
aren't totally
unlike the way inanimate
particles
might interact
through forces of attraction
and repulsion, repulsion being this tendency to avoid
collisions.
And and this was the kind of idea
that, you know, lies behind all of these
attempts to try to understand group motion.
Is it is it something that we can
describe
just in terms of forces of attraction and
repulsion between the individuals,
who
each of which, you know, have their own
kind of agenda to some extent, but it
may be a fairly simple agenda. I mean,
even for us, if we're walking down a
pavement, we're generally trying to get from one
place to another, you know, probably as as
as quickly as possible
while navigating
obstacles and while, you know, avoiding
collisions.
So
that's
that's a, in principle, that's a fairly simple
situation
to to try to model and, you know,
that's kind of what you're probably seeing sheep
do, except that there's, you know, there's also
something else that I imagine you're kind of
seeing them doing, which is, you know, they're
not just trying to get from a to
b. On the whole, what they tend to
be doing is is wanting to to eat.
They spend their most of their lives doing
that, you know,
chewing on on grass.
So, you know, that's another aspect of the
problem that's specific to grazing animals that they're
not just trying to move. They will move
occasionally, and in particular, they'll move to try
to find more food, more grass.
But there's that, there there's that sort of
complex, you know, interplay between
just trying to, you know, be left alone
to graze, to eat,
and moving around together.
And
with sheep in particular,
as you say, they're nervous
of other animals, particularly of dogs, but also
of humans. So there's also that aspect. They're
kind of looking out for what they might,
you know, imagine are predators.
So those are the kind of components
of the behavior that need to go into
trying to model this situation.
As we've been talking,
the sheep have gone from
lying down
behind
a sort of hillock on the hillside, if
you see what I mean. It's it's it's
a a mound on the hillside, which is
protecting them from the wind. That's why they're
all there as far as I can see.
Then the sun's come out, and they've all
spread out across the field. Physics doesn't explain
that, does it? I mean, apart from the
sun coming out.
Well, it it it
I could see how it could
in that, you know, if you were wanting
to,
to to include in a model like this,
their wish to be in sunshine,
then
the you know, there's an attraction there. There's
an attraction to a particular kind of, you
know, you can model it as a as
a force, as a field.
I mean,
there's literally a field as well.
So you can see how you can, you
know, build aspects like that into it. The
other, interesting thing about sheep and about, you
know, larger animals like that, which actually doesn't
apply so much to us. You know, sheep
are elongated. So from above, we're kind of,
you know, circular blobs, and you can model
us as circular particles.
Sheep are kind of more like ellipsoidal
particles.
And so there's an orientational
aspect to that. It's a bit like the
contrast between,
you know, simple atoms or or, you know,
globular molecules and liquid crystals,
which also have that tendency to kind of
align, to respond to one another's presence by
sort of aligning their their axis.
And so, you know, that's a question we
might ask and and the researchers have asked
about sheep.
Is there a a tendency to align? And
if there is, then that looks a little
bit like
the way magnetic spins
align in magnetic materials,
and you can build that force into it
as well. A force that,
will make one sheep have a kind of
tendency to align its body,
with the direction of what its neighbors are
doing. Why?
Well, I I I guess I mean, certainly,
if if you're if we're thinking about the
way sheep move,
you know, they seem to
the the flocks,
seem to occasionally to sort of wander as
groups in the same direction. That's something that
you just observe happening. So sheep, you know,
have this tendency to follow one another.
And if
that's the case,
they're clearly gonna have to be aligned in
order to do that. So they're not, you
know, literally, they're they're trying to align in
the walking direction.
So while they're moving, you know, they'll be
trying to align. But also, you know, if
they were in a dense
flock and they were being, for example, if
they were being shepherded by a dog, then
again, they're they're one they're they're not gonna
be wanting to bump into each other.
So, you know, there's probably there too going
to be a tendency to kind of line
themselves up.
So, you know, that it's really,
just either a matter of, doing that in
order to, coordinate their motions or doing it
so that they can pack more efficiently in
a small space.
I do there's a a behavior that I
observe here. I don't know if you've seen
an answer to this, but,
very regularly in the summer months, there's,
you know, a tourist dog who should be
on a lead isn't on a lead, but
we'll we'll sketch over that particular issue.
But one of the sheep gets isolated from
the rest of the flock.
Then the dog goes away and that sheep
the isolated sheep then calls out and tries
to find its way back to the to
the flock. It well, it seems that they
do. I mean, a lot of animals do,
particularly if there's a predator around because it's
the kind of safety in numbers thing, you
know, that that actually a sheep on its
own is gonna feel much more exposed than
a sheep in a flock and particularly a
sheep in the center of the flock. So,
you know, there's clearly a, there's a sort
of attraction if you like there for the
sheep to a sort of cohesive force that
will keep them together for safety.
And, you know, more than that, that what
you actually see in,
sheep flocks and in other sometimes other groups
of animals that are exposed to predators
is they they all wanna be in the
middle, which kind of makes sense, you know,
that that's,
that's the safest place.
And so there's this kind of constant churn,
you know, going on.
And in fact, it's,
some, biologists have, you know, talked about this
in terms of this kind of selfish flock
theory,
where, you know, everyone wants to be in
the middle. And so what what kind of
dynamics,
result from that?
So, yeah, that that that, you know, if
if if, an individual or small groups get
isolated, then there is this sort of attraction
to bring them back into the flock
for the the the purposes of safety. In
the selfish flock, my understanding is that at
least it's the way it's talked about, I
think, in the march of the penguins.
They they take it in turns, and that's
less selfish and more,
altruistic, more part of the group kind of
looking after each other. Is that just a
kind of a nice way of looking at
it? Well, that you know, that's a good
question because it's quite possible,
and I don't know if this is the
case for penguins. I think it's more probably
the case for for sheep, certainly.
It's quite possible that if you have a
model where there's the the the the individuals
are moving around, they have this tendency to
try to get into the center. That actually
spontaneously
out of that will come this kind of
churn, this kind of turnover of who's on
the edge. So it's not as though the
individuals on the edge are kind of at
any time consciously
thinking, okay. I've had my turn now. You
know, I've done my my my part to
be on the edge now. Let me back
in the middle. I don't think, it seem
it seems unlikely that anything like that is
going on. It seems more probable
that something like this kind of behavior would
emerge spontaneously
simply from those movement and from those kind
of psychological
rules.
And the other thing that seems to be
spontaneous from what I can see is the
who the leader is at any moment with
the sheep in terms of the the leader
of the movement, who takes
this the flock in a particular direction or
parts of the flock in a particular direction?
Yeah. Sure. I mean, that that is something,
that that is observed, and it's something that
was looked at,
in particular in one of the models that
I discussed in the piece, work done by
Fernando Peruana and colleagues,
at one of the universities in Paris,
where they they were watching. So they started
off empirically. They were just watching small groups,
sometimes really tiny groups of of sheep, you
know, move around in a field grazing. And
they found that there was this kind of
tendency
to occasionally
develop to adopt to kind of, you know,
moving mode where they all walk in some
single file to a different part of the
field.
But what they observed was that there so
there's a leader there,
by Natesti if there's a line, but they
they found that the sheep seem to take
it in turns. So there's no,
single leader who decides that in the flock
each time.
It seems to happen more or less at
random. So pretty much each of the sheep
gets a turn,
at being the leader.
And, again, you know, that sounds like that's
all very kind of egalitarian
and, you know, it's you could imagine the
sheep saying, okay. Now it's my turn to
be leader. But actually, you know, it seems
like it doesn't happen or at least we
don't need to assume that. If we simply
assume
that whoever,
becomes the leader, that that is, you know,
something that just happens
randomly, that there is a particular chance
at any moment that any individual will become
the leader of one of those moving groups,
that that's enough
to account for for what we see.
So, yeah, there there absolutely is. And the
you you see the the advantage there's an
advantage in in having that kind of, if
you like, that kind of cheap algorithm,
you know,
inherent in in their behaviors,
relative to their always being the same leader
because,
it means that there's more pooling of the
information
that all of the individuals have.
So, you know, it may be that one
of them knows or has has
realized that one part of the field seems
to be more attractive or less, you know,
chewed on than another. And
the ideal is that the flock has access
to the information of all the individuals.
You know, this is something that we might
also see in in in flocking birds or
in the other sort of flocking animals where
it might be that 1 or 2 individuals
spot a predator where the rest of the
the group hasn't. And so you want to
make use of that. You want to pool
that information that everyone has.
So this idea with sheep, this idea of
constantly changing the the leader in these sort
of episodes of walking,
It's it's an it's an efficient way of
working,
in the sense that it's efficient in information
sharing. I I can't help wondering if it's
not that, efficient if you're trying to run
a political party or a government to keep
changing your computer.
Well, you know, I mean, even there, that's
although that's true, you know, there is a
discussion about whether there are more sort of
egalitarian, you know, whether it would be
more sensible, for example, instead of having a
House of Lords to have, you know, people
randomly,
you know, chosen from the population
to lead a body like that and that
there's a constant turnover there. And maybe that
would be more efficient in terms of being
able to make better use of the information
that everyone has rather than that a few
privileged individuals have for life. But wouldn't the
concern there be that you could get someone
that was sort of, you know, a a
conspiracy theorist running
an important part of the world. I mean,
absolutely. You know, questions like that, they're they're
they're complex ones, but I suppose the the
the principle there is, you know, you could
equally get them and you could equally get
that in the house. And when you do
get that in the house of lords now,
and they're there for life.
So, you know, as long as you have
that constant sort of turnover,
the the the idea would be that, you
know, on average, you're going to,
get a better sort of representation and a
fairer representation
of all the information and all the views
in the population. There's that kind of angle
to it, but I wonder, is this actually
useful for the farmer who's herding the sheep
out there? Well, I I think I mean,
first of all, I guess the question is,
can a model like this even reproduce what
we see in terms of herding? And, you
know, that's the thing I found quite remarkable
that it can.
That without any
assumptions and here I'm talking about a model,
that's being developed by,
peep researchers at Harvard,
and others to try to look at shepherding
behavior. And, you know, here, as as I
said initially, it just seems like, how on
earth do you make a physics based model
of a sheepdog actually trying to herd, you
know, a
flock of sheep. But it seems that you
can, that it's an optimization problem is how
they present it. So you're you know, the
the the challenge is to try to get
the flock
from a to b as quickly as possible
without losing any sheep on the way.
So
what what is the optimum strategy for a
dog to, to undertake
to encourage that motion?
Assuming that there's this repulsion between the dog
and the sheep so that that's what's going
to kind of move the the flock along.
And there's there's this attraction
between the sheep themselves and that's what's going
to get to keep the flock coherent. So
given those constraints, you know, what's the most
efficient way of doing it? And they come
up with three possibilities
that do the job.
One is that the dog just sort of
runs from side to side of the flock,
keeping it, you know, in order but gradually
kind of getting it to move, in a
single direction.
Another is that the dog runs in circles
around the flock, but they're kind of corkscrewing
circles so that it's gradually getting somewhere.
Both of those strategies are ones that are
seen, literally in the field.
And then there's a third strategy, which isn't
seen as far as the researchers are aware,
which is that the dog actually it almost
kind of burrows into the flock.
But, because the flock is coherent, it doesn't
just scatter. It sort of stays together, but,
you know, it it sort of draws back
from the dog within it. And so by
the by the dog moving, the flock, it
kind of moves with it as if the
dog is like a driver in a car,
and so they call this strategy driving. So
it seems to be one way that the
the sheepdog could work, but but doesn't. And
it may be that
the the flocks just aren't really sort of
cohesive enough to make that a viable strategy.
We we, you know, we don't know.
But, you know, it does seem to capture
what actual sheepdog do. And so, you know,
that's a kind of it seems like that's
a kind of validation of the model itself.
And so if you've got something like that
that works, then you can start to think,
okay. What are there ways that,
you know, are there better ways
of of of doing this or are there
ways that we could improve the efficiency with
which the the dogs work? Or in particular,
are there ways that we might automate this
process using, for example, drones or using, you
know, robotic,
you know, entities to to do the herding
instead.
And I, you know, I I loved it
that,
what some of the researchers involved in this
work said, well, if you if you speak
to farmers about that possibility of using drones
or whatever, they just laugh,
because, you know, the technology just isn't ready.
But in particular,
it seems that sheepdog you know, sheepdog are
really smart dogs.
That's why they hear that they're they're so
good at doing this job.
So they have a kind of an instinct
it seems
to be able to respond on the fly
to what the sheep are doing. If you've
got one that wanders off, you know, the
sheepdog can respond to it in ways that
it's probably gonna be very hard to program
into robots or drones.
But, you know, if you're ever going to
do that, then models like this are precisely
the kind of thing you'd need to figure
out what are the basic rules of behavior
that a an automated
shepherd or sheepdog,
would have to follow in order to do
the job efficiently and reliably. I can't help
but wonder why on earth you'd want to
replace sheepdogs, though. I mean, what would be
the reason? Yeah. Well, they require a lot
of training,
and, you know, they if if and they
have their limitations.
They can only go at a certain you
know, they can't go too fast.
And, you know,
it it could be that particularly
if you're
working with bigger flocks
that, you know, sheepdogs would a single sheepdog
would struggle to, to to contain,
you know, might it be then that you'd
have to use something more systematic,
and with great a greater range of capabilities
in order to do the job.
So, you know, I suppose you could imagine
situations where where that's,
where where that's the case.
You know, drones never tire, drones never need
feeding.
But, I mean, you know, I I suspect
that behind your question is the same feeling
for me that actually it's amazing and lovely
that sheepdog are able to do this. And,
you know, what a shame it would be
if they were replaced by something automatic.
I know. I think the only
possible reason I can think for it is
if you had a sort of robotic dog
that had
some form of artificial intelligence, and it worked
with the sheep
so much that eventually you could ask it
whether androids dream of electric sheep.
Brilliant. Of course, you would need to get
that in there. I guess, you know, I
I mean, I I think the other,
answer to that might be, well, you know,
it may be that you don't wanna do
this with sheep, but it may be that
you wanna do it with other systems, including
maybe,
you know, sort of nanoscopic inorganic systems where
you've got a load
of what are are called active active particles
that can move under their own steam, and
you want to get them somewhere and you
want some other, you know, active particle or
nanorobot or something to, you know, be able
to move them or other animal systems or
possibly even cells. You know, if you want
to marshal cells in a particular direction, can
you do that with some kind of artificial
entity that is programmed in certain way?
So, you know, these
these, questions of how to shepherd a load
of,
you know, moving
possibly quite idiosyncratic particles,
there's a broader applicability
to it than just she. Can we apply
this to to people? Can we is there
some way we can help with crowding at,
I don't know, concerts or football matches or
something by looking at this? It's it's already
being done. Models like this to try to
understand crowd behavior
have been used to try to plan things
like events like Notting Hill Carnival
where you can, you know, literally map out
the real route that the carnival takes and,
you know, get some sense of what the
crowd motions might be like, where you might
have pressure points and, you know, difficulties that
might need extra policing or extra guidance or
so on.
It's also been
applied models like this have been applied to
the,
the Muslim pilgrimage of the the Hajj,
which has
had problems,
you know, some years because so many people
come to it. It's this massive crowd event
that has, you know, the real danger of
there being sort of buildups of pressure in
the crowd. And there have been,
disasters really that have happened in the past
because of this. And so this sort of
modeling has been applied also to to events
like that to see if you can create
better safety measures.
And just sort of crowd panic in general,
you know, is is a really difficult problem,
that,
you know,
models like this can can can inform
at least.
So, you know, it's not necessarily that you'd,
you'd be thinking of a a sort of
shepherding problem, but that you'd be anticipating
problems that might arise in these sort of
mass crowd movements
that are possibly quite predictable ones just as
they are for road traffic, and then think
about the kind of safety measures that might
alleviate that. You mentioned your book that you
wrote a a few years ago, but haven't
you got a new one that's sort of
looking in this area? Oh, yeah. How life
works. That's looking more at the kind of
molecular details of, of of that question. But
it does, you know, it it it does
then sort of, open out into
looking at higher levels of the system.
So it's actually
yeah. I mean, in the sense that the
that book is looking at
questions of emergence in biological systems, you know,
that's what we're talking about. It's absolutely what
we're talking about with these collective motions of
animals. I mean, you know, the murmurations
of of birds are one of the classic
examples of of emergence but we absolutely see
in biological systems in in all sorts of
ways how you get these higher levels of
organization
that are
somehow somehow seem to be autonomous
from the really lower level details of what
individual molecules are doing and what genes are
switched on. So that broader question
of how you get
emergent sort of coherence and robustness
in the face of
microscopic
details
that might be quite sort of random and
idiosyncratic.
That's absolutely the same kind of question,
that we we see in, you know, in
these cases. And in fact, it's actually you
could say the same kind of problem
that, you know, in a sense,
that emergence in biology continues beyond the level
of the organism to the ecosystem
and for us to the society.
And
there are similar kind of principles
involved in the sense of how do you
get a high level system
that is reliable and robust
and isn't going to break down as soon
as something microscopic
goes awry.
A walk among the sheep. They they don't
worry about me walking amongst them. They just
stay where they are. But it the movements
that they have, as you say, you know,
sort of that bit of field looks
tastier or whatever it is,
It it it doesn't seem to make sense.
It seems to be
just sort of
I'm going to move now. It doesn't seem
to be any more to it than that.
Right? It just seems like, yeah. I'm moving
moving over there and then the others follow
because they, oh, maybe they found something. It
doesn't seem to be kind of, you know,
in any way,
decisive based on evidence
about just sort of going somewhere. The information
bit of it, that's really lovely. Right? That
that they're sharing that information, but maybe the
information is dodgy. It isn't better.
Mhmm. Well, I mean, okay. So maybe the
first thing to say about that is that,
and this is something that I heard in
in writing this article that, you know, farmers,
will say, actually, sheep,
they're more intelligent than you think they are.
They're and they're more individual
than you think they are. So for example,
it seems that sometimes,
sheep flocks
respond
to
what the farmer has told the sheepdog to
do. So the farmer can, you know, has
whistles or whatever to instruct the sheepdog to
do particular things. The sheep get to to
recognize
those calls
and start behaving, you know, as the dog
would want them to behave before the dog
has really done anything, you know. So then
they are picking up on these things. But
also it seems like there's quite a lot
of variability
in the way they behave. Some sheep just
look at the sheepdog and think whatever and
ignore it. Whereas others think, oh my god,
and run like mad the moment they see
it. So that, you know, that's, that variability
you need to build into,
the into into these models. But it also
undermines the the, you know, the the kind
of meme like stereotype really is, you know,
sheep are just they just all do the
same thing. Well, they really don't.
But the question of, you know, why they're
doing this in the first place, it's it's
it's all about foraging
strategies, and each animal has, you know, has
some. And
the this has been, a very sort of
rich area of research of how do animals
decide what strategy to follow, what's what's efficient.
Some of them seem to,
undergo what are called levy flights where, you
know, the idea is you go to one
location and you forage around a little bit
around there with random walk, and then you
make a big jump to somewhere else and
then you forage a little bit around there.
That's something that you could describe using a
particular kind of statistics.
You know, other animal foragers. I mean, if
we think the classic example really is bees.
You know, that's that's extraordinary, the amount of
cognition that's involved there that they will you
know, they'll go out,
they'll, you know, they'll find some,
some sort of source of food,
come back to the hive, and tell the
others where it is but with this incredible
waggle dance and the others will understand and
off they'll go. And sometimes they won't. Sometimes
they'll think, you know what? I'm it doesn't
I you know, I've been there and it's
not I mean, I'm you know, this isn't
what the bees are thinking, but it's how
they seem to behave. They don't all just
sort of respond in this programmed way. So
even in the case of a bee with
this kind of, you know, brain the size
of a grain of salt,
let alone a sheep, there's an amazing amount
of cognition, of subtle cognition going on there.
So,
you know, I I I think
although,
I mean in a way that's what these
models are kind of showing us that, you
know, we can be described using these particle
based models in certain situations
and yet we like to think that we
have this extraordinary amount of cognition that, you
know, we're making use of in other contexts.
And so we should probably not underestimate
the amount of cognition that other animals are
capable of,
and the the way that the,
the the way that that is
harvested collectively.
You know, that's the key thing that the
these creatures aren't making decisions
just by themselves.
They're actually that that these strategies have this
sort of collective ability
to pool information and to make effective
use of the group.
So
it's it's a much richer
environment
than you might think understandably
by going up to a sheep and thinking,
there's not a lot going on in there.
Actually, you know, there may or there may
not be, but what comes out of it
is really quite rich.
With no offense to any sheep or farmers
listening,
I wanted to know more about
the human interaction in crowds.
Someone who won the Ignat Nobel Prize
for the research
into crowd movement
is Alessandro
Corveto.
I am an assistant professor at the Endoben
University of Technology
in the department of applied physics.
I have here my own group that is
called the AI for complex flows and traffic.
And, what we do here is,
well, essentially 2 things. So first,
use
tools
of physics to explore,
let's say, new physical systems, specifically traffic and,
most importantly,
crowd traffic. And the second thing is
we use AI
tools to try to crack problems in in
complex fluids and complex flows that were that
are open for long.
And, and therefore AI is giving new opportunities
in this direction.
We won the 2021,
Iqnoebel Prize for a research that we started
somewhere around 2013
and, that is, that is a very big
part of my work,
today as well.
So we
try to understand,
whether we can establish some basic physical understanding
of
of the way pedestrian crowds moves. Okay?
And
and this means try to establish
a physical model that reproduce, that explain,
what we see as we look at crowds.
And
let's say crowds are
very, let's say, as you can as you
can imagine, a very random, a very stochastic
system. Right? So imagine that you are yourself
in a in a train station.
What you do today is different from what
you'll do tomorrow, and what another person does
today is different from what they will do
tomorrow and what you will do today.
So it's an extremely stochastic system. And the
the key idea
of of our work was to explore this
system, collecting as much data as possible, in
this case, in a train station. Now imagine
tracking,
all the people passing in this train station
for, like, 1 year.
Now we we speak about, 100,000 people per
day,
roughly speaking. So you you multiply this by
360 days. You are immediately in the
the millions
scale.
Well, many millions of scale.
And then,
the endeavor was or the endeavor still is
to find
universal pattern or universal physical features that emerge
as we look at this,
dynamical system at this,
this large scale.
And
let's say
you need to start simple. Right? So this
is a this is a extremely complex system.
So we start simple. And,
so first first step is to look at
how individual particles, let's say, individual pedestrian move,
what is their statistical
fingerprint. So how can you describe this with
the with the stochastic differential equation?
And, specifically,
the ignoble was
centered around the, let's say, the volume 2
that is, when you move from a single
particle to 2 particles interacting.
So we did,
something,
let's say, in some sense inspired by by
by impact parameter studies in in scattering physics.
That is imagine that now you have 2
particles that that
are,
walking or are moving in opposite direction. And
then, I mean,
obviously,
you don't collide typically with with the particle
coming in in in the opposite direction. And
so there is, there is a whole avoidance
mechanism that is that is triggered.
And we characterize this avoidance mechanism
looking, again in the in the hundreds of
thousands of trajectories.
And, and we wrote the differential equation that
reproduce the same statistical fingerprint. So what happens
So what are the the the the common
way in which this process,
happens?
The fact that every now and then there
are indeed collisions and
how, let's say, the interaction decays in space
and and so on. Okay. Okay. So, like,
for example, one one time, I was walking
along Tottenham Court Road in London. A taxi
pulled up. I wasn't concentrating.
It was before I had a mobile phone,
so I wasn't looking at that. I don't
know. I was probably looking at a a
Lego shop or something. And, the,
the door opened of the of the taxi
without me noticing, and somebody stepped out, and
I bumped into,
that person. And that person was Billy Piper,
who at the time was in doctor who,
which was terribly exciting for me.
I imagine it wasn't terribly exciting for her.
So that kind of collision has had a
different reaction with the 2 different particles.
So one of those particles had a, you
know,
reaction of boredom and annoyance, and the other
one had a reaction
of excitement
and can't wait to tell its other particle
nerd friends
about the interaction.
Let's say this is, I think,
connected with with the other Nobel Prize that
has been that has been won the same
year by the group in Japan of of
Claudio Feliciani and the others.
And, yeah, they they started, let's say, the
the case in which
which people are not paying attention because they
are distracted by by mobile phones. And they
are examined
essentially
how
this interaction mechanism gets penalized as as people,
don't pay attention. What does it tell us
about
ourselves,
crowds,
or physics in more widely? Bumping or perhaps
not bumping
is,
is
the basic interaction mechanism that there is in
this type
of system. Let's say that that, you know,
as physicists or as applied mathematician,
what mathematicians, what we'd like to do is
start from something complex. Right? We have many
agents, many people, and so on and so
forth, and we try to
reduce it using, the language of of mathematical
physics. Right? And so immediately,
you you would reduce a system of interacting
pedestrians into a system of particles that, in
a way or another,
have,
interactions that are in some sense similar to
gravity, but probably
anti gravity. Right? So you repel each other
instead of,
attracting,
each other. And, and,
the interesting thing,
is that
considering a a particle system
with with this very basic,
repulsion mechanism,
is enough to recover
some of the
qualitative features
of this system. So you might be aware
of of of the following.
This is a very, very much used case
to explain some of this emergent behavior that
you have in this in this crowd system.
Right? So,
okay. Okay. Maybe let let me add something
that,
one of the the connection point between between
crowd dynamics and and physics is in the
direction of complex system. Right? So complex system
is about,
how, let's say, complex pattern or complex structure
emerge as
basic
entities interact with simple rules. So the fact
that you can get macroscopically nontrivial patterns out
of basic
rules between many, many agents. And in crowds,
the very basic,
emergent behavior that is considered is the following.
So imagine that you have
a crowd of people, let's say, with with
red shirts that are moving along 1 one
one street from one side to another and
another crowd of people with blue shirts that
are moving on the same street but in
the opposite direction. Okay? So now you have
the 2 big crowds, and they're facing each
other and walking
in in opposite direction. And then the very
basic,
emergent behavior that this system has is that
particles,
or or people that have
that are going in the same direction, which
means that they they come with a a
shirt of the same color,
would align in stripes. So now imagine yourself
looking up. Now these 2 crowds
walked, and now they are crossing each other.
But then you wouldn't observe that, the system
is completely disordered, so you wouldn't observe completely
random distribution of red, of red and blue
shirts.
You would rather observe
the fact that the system has ordered
in in in stripes
of shirts of the same color, meaning of
stripes of people going in the same same
direction. And the the fascinating thing is that
it's
extremely
well, it's surprisingly simple to regenerate or to
to,
explain, to reproduce
this emerging behavior
by just
having particle moving in opposite direction that
know only a single rule beside the the
the need of moving that is the need
of avoiding. So the need of not entering
into very string stronger contact. Right? So so
the mechanism
of not bumping into each other is already
enough to explain quite interesting
emergent,
emergent behavior. This is only one. Right? There
are there are others.
And,
Yeah. Yeah. It took a very long road
here. Brilliant. So when you are you're doing
all of this research into into crowds, what
what's your goal? There are multiple goals. So,
let's say, as a physicist, there is the
challenge of of explaining
something that is
extremely complex, extremely stochastic. So there is the
the the underlying question, can we put down
physical rules that that regulate this system? And
here, one needs to go deep enough because,
obviously, you cannot expect to have a mathematical
model that reproduces what one person does because
this is obviously,
I mean, meaningless. So this is completely
senseless because it's just unpredictable. But then,
in in our case, going deep enough means
having enough
data
of this system,
in such a way that that statistical fingerprint
emerged. Like, the fact that the average behavior
of the system is this. The fluctuation the
typical fluctuation is that. Events that happen 1
in 1,000 are these and these and these.
Events that happen 1 in 10000 are these
and these and these. And I think at
this point, it becomes
not so surprising the fact that about this
behavior, one can write,
can write physical,
equations.
And, and, and this aligns with, again, topics
such as, physics of active matter that is,
the physics that study all sort
of matter that in a way or another
is capable of of turning internal energy into
motion spontaneously.
Right? So we go from,
bacteria.
We go from, we consider, I don't know,
flocks of birds,
and so in general animal behavior and so
on. It aligns with the physics of complexity
that is, how,
as I said, how
nontrivial pattern emerged from simple inter from simple
interaction.
And this this case also, especially when we
go in a high density regime, it can
align with with, some condensed matter structure. So
but in general, there are there are very
many connections with different fields of physics.
And then, of course, there is also the
the the societal
slash engineering component that is,
now we get to have a model that
that is capable of reproducing the the stochastic
dynamics of the system. How about we use
it to
in design phase perhaps of of a facility?
Or we use this to do real time
control, which are things that actually are happening.
So let's say you observe your system at
a very large scale, and then you you
have a model that tries to predict what's
most likely going to happen in the next
5, 10 minutes, and then you use this
to to nudge. Right? So to to exert
some action in the system in such a
way that that it behaves as it, I
mean, to increase safety, for instance, or or
or comfort. And this, this is this might
sound science fiction, but this
is active research
on one hand, and already there are many
attempts in this direction already on the other.
So Yeah. Do you do you have any
examples of where it's been?
Well,
I can I can connect, with with my
research?
For instance, we're investigating,
the usage of of illumination
to nudge,
people into,
let's say, actually,
into distributing in space to maximize
some some property.
A typical application would be, for instance, in
museums. Right? So you would like
floor usage, to be uniform,
and not, that,
you know, your crowd is fully concentrated in
one room and then all the rest of
the museum is,
empty.
We've been working with, with with sound with
positional sound as well. Let's say you,
only in certain location,
you can hear some sound.
And, only for instance, if you have a
complete track this is something that we we
published recently that, let's say,
only if you are walking a specific trajectory,
you could hear a full, let's say, melody.
Right?
And,
and if you steer out from this, let's
say, target trajectory, you stop hearing the melody.
And so there we show that actually we
could have people following the the, let's say,
the target trajectory
due to the fact that there was, an
expectation by people of keep on hearing the
melody. You know? So it's like every step
you hear a different
tone. Right? And then people would be,
I mean, spontaneously,
you would like to hear what you expect
to be the next tone. Right? So imagine
that there is a there is a simple
melody, and and this enable us to to
push people to to follow,
certain trajectories. But but there is more. I
mean, there are
things that are very unexpected. And,
I mean, let's say at times, you are
maybe in a train station and and and
you are annoyed by the fact that your
mobile phone
is is is not connecting to the Internet.
And this is not because there was no
Internet there, but because the signal is locally
jammed
to make sure to make sure that people
don't clog in a
in a given, location.
Let's say we don't want people to stand
here because maybe the the the flow would
would would clog. Right? Imagine,
there is a staircase perhaps, and then people
would would climb the staircase and just wait
there. But obviously
and let's say that this this staircase leads
to to the platform of a train station.
Yeah? And, obviously, you don't want people just
to to climb the stairs and and wait
there. You would like them to distribute on
the platform.
And, so how do you ensure that?
Then you would locally jam the mobile phone
signal in front of the staircase.
And now and now, you know, the person
would would climb the stairs and then they
would just stand there and get out their
mobile phone and then they figure, oh,
it's not working.
Right? And then I move around to to
get it to work. But, yeah,
it was not it wasn't working not because
there was no signal. It was on purpose.
You know, you know and then it was
not the efficiency of the mobile phone company.
It was rather by design that you were
not,
That's amazing. Is that something that's going to
happen?
No. This is something that is happening already.
Really? Tell you this. Yes. Genuinely.
People are using people are blocking that. So,
basically, at halftime in the football, right, I
I sit there and try and check the
scores from elsewhere,
and it it you just can't because everybody's
doing exactly the same thing. Happily, in my
own,
football club that I support, they provide Wi
Fi, which does the job quite nicely. And
and they put the scores on the screen.
But there it does happen that it's blocked
because there's a number of people in it
as well. Right? Just so people are listening
and not thinking every time it's blocked, it's
some mobile phone company trying to move them
on.
Well, no. No. No. I mean, like, yeah.
Of course of course, there you have a
you have a network capacity issue. I I
think these these are different things. Here, I
mean, there is a possibility of doing very
local jamming. Right? The the the within a
within a very limited area, mobile phone is
is
reception is not is not good. But, again,
this is this is already in in place.
And, that's extraordinary.
That's extraordinary. Well, can you tell me just
going back to the light bit, the illumination
bit in the museum? Right.
How does that would we are you sort
of eliminating particular bits to draw people in?
Are you dimming bits? Yeah. We we we
ran a couple of of big experiments,
around here. Is,
is the question,
can we use light
to
to to provide
information
that is,
to convey, let's say
I I don't want to use the word
subtle, but to convey,
let's say, more and more implicit
information. And it works,
or at least the experiment that we did,
were based on on on dimming.
And,
we showed indeed the following
that,
imagine you have you have this big crowd.
We did this, in in this very experiment
that I'm referring to was that was run
during a big festival that we have here
in Andover.
So it's it's,
city walk throughout the entire city, let's say.
So, again,
tens of thousands of people.
And now imagine these these people all entering
into a corridor, then,
we wanted them to go, let's say, to
choose either the left or the right exit.
And,
in our experiment, we compared our effective it
is to show people, for instance, a normal
signage, so big arrow that says, okay. Go
left rather than go right.
Or
rather to change the illumination level between the
two ends or the two exits. Yeah. And
then we show that that,
within some,
let's say, crowd density limit,
you can have more or less the same
effectiveness
in,
in
light and signage have more or less the
same effectiveness.
And then it happens that as cloud density,
grows,
as as you can expect,
there is no indication that is really followed.
It doesn't matter whether it is based on
light or signage. It's it's like,
I mean, people at certain point when there
are these many in a confined space, they
just need to get out. So it doesn't
matter whether there is an indication that points
there them somewhere.
But but, let's say, within
operational,
density level that you can find
in museums and so on and so forth.
So in our experiment, we could show that
that you can convey the same type of,
signal or you can convey a signal that
that has the same effect on people,
either by using
variable light intensity or or,
signage. Could you tell me a bit more
as well about the the the audio version?
Do do you have the melody? In the
paper, you could find the 4 the 4
tunes.
It was it was,
really
piano chords. Yeah. Oh, okay. I'm literally sitting
at a piano right now. So Okay. So
we could see. Yeah. I I I yeah.
I I think yeah. If you want, I
can I can look for it even right
now? But it's, let's say in this paper,
then then you would see that
we divided the space into
areas, and then each area, each small area,
something like maybe half a meter times half
a meter,
came with different,
piano
tune. Right? With a different,
and then this was played one after the
other should you be,
walking
along the right path. Yes. Otherwise, you you
wouldn't hear anything. And,
okay. I'm not a music person. Right? So
now I would say something that I hope
makes sense to you because it doesn't make
much sense to me.
The sequence was c4c5
and thene4e5
and then g4g5andc5c6.
So should you be following the right path,
you would be hearing this.
As when you're walking around in crowds, are
you sort of in a kind of Sherlock
mind palace with equations appearing in your peripheral
vision as you're walking, or are you just
kind of wondering how can we make this
better? Walking around is is mostly like,
about how to improve the models. So how
to include,
let's say, more factors in the models that,
that that we are using or that we
are making.
And I think,
yeah. I
mean so that there are very
there are a bunch of very interesting physical
directions that are let me make an example
that is more telling than others in in
in my view. That is, for instance, when
when a crowd walk, not necessarily
the interaction is cooperative.
Right? So imagine that that you would like
to to get, onto onto a train. Right?
And you would like to to be
inside the train first.
And this
eventually doesn't mean, for instance, that the people
that need to get into the train with
you,
altogether
will manage to get in in the most
efficient way.
Right? Because
typically,
the the behavior of of each particle here
is is competitive
instead of constructive. Right? So and then the
question arises,
so how to up deal with it, how
to model this? So what is the underlying
physics? How can we represent it? I think
this is, for instance, a very interesting,
a very interesting,
direction that is,
how to to consider this particle system in
which, which,
there is an interplay between between competition and
and and cooperation,
as the the context changes.
And so for instance, this is one of
the things that I'm asking myself lately. So
what are efficient way to deal with this
and then then the randomness in the crowds
and and and so on.
And about about the interest in general, I
think
I got into crowds because
I wanted to do I wanted to explore
a part of physics that had two characteristics.
And one that was,
close
to people, like, that was, that I could
communicate
easily about,
that was immediately impactful
in society.
That was new. Right?
Where new means that, that research,
let's say,
started not so long ago. I mean, for
crowds, I mean, obviously, I mean, crowds, you
know, have been I mean, people have been
trying to optimize, crowd flow from the times
of the Colosseum in Rome. Right? But nevertheless,
let's say physicists started to look very seriously
at crowds only since the beginning of nineties,
and the first experiments were since the beginning
of 2000. So I would say that this
is relatively new.
Wanted to do something something new and it's
also something that
being new enables to I mean, needs to
couple a lot of things. So how to
do experiment is still an open question. How
to model is an open question. How to
control is an open question. So it has
a very, very broad range of open questions.
And this gave me the
the opportunity to to to bridge many things
from the experimental part to the modeling part.
And this is,
yeah, also one of the reasons I enjoy
this.
I'm not sure whether I project the equations
in my mind, but
it goes in that direction for sure. I've
never spoken to anyone who's won the Ig
Nobel Prize before. How does that come about,
and how does it feel?
Well,
it works, in in the in the following
way that that you receive a mail from
some colleague
that you might or might not know that
tells.
Well,
well, typically, I think you you you don't
know the colleague and then, you receive this
email
this morning that says, hey. I read your
work.
It's very interesting. I'd like to ask you
some question. And then and then,
it goes more or less like, the famous
scene of the matrix, you know, the the
blue and red pill thing. So, okay, I'd
like to tell you that that, you got,
I mean, you have you won this, Nobel
Prize and then,
but you can talk with your colleagues and
decide whether or not to accept the price.
And then, you you, yeah, you contact again
this person that that wrote you in the
1st place and you go for the red
pill. And then it becomes
yeah, afterwards, it becomes obviously extremely interesting because
it's a
great opportunity
to to discuss with with very, very wide
audience from scientists
to to
popular science channel about about your work. Okay.
But so is that why you decided to
accept the prize because of that opportunity for
communicating about your work?
Well, this is definitely well, because it's a
prize. Right? So we're not. And
and, but also,
I mean, it's I think, it's an unprecedented
opportunity. I mean, I don't think,
yeah, I don't think, anyone,
could let it slip, honestly. Yeah. No. It's
a it's a it's a lovely thing. I
mean, my understanding of it, right, is is
that it's interesting research that also makes you
laugh. Right?
Yes. Yeah. I mean, the the I think
the line by by Mark Abrahams is,
research that makes you laugh and then think.
And, I think the beauty of this, global
prize is that,
every year there are there are discoveries
that, I mean, at first sight,
they they might look,
they might not look about science. Right? Because,
you know, maybe seen by the general public,
science needs to be
extremely complex,
very far from what we are doing in
our daily life, but, often it's not the
case.
And,
and, and, therefore, this is something that that
people would find surprising. Therefore, in many cases,
it can make you laugh. There are,
but then definitely, it's it's a channel for
having people thinking about surprising,
let's say, surprising emergence of science where they
wouldn't expect.
I think this is a growing field. I
mean, the cryodynamics
is a growing field
in physics,
engineering in general.
It's something that is definitely
challenging scientifically,
but also impactful in society as,
in in places that are very densely populated,
the Netherlands, UK, and so on and so
forth. It often happens that
you the the pressure on public infrastructure like
the train station grows, by the year, so
there are more people moving.
But often,
it's becoming impossible to extend
train stations
to to, cope with increased load. And so
now the only way
is is making the environment smarter,
And this is, one of the reason we
are doing this.
I'd like to thank Alessandro and Philip for
talking to me for this episode of the
Physics World Stories podcast.
And, of course, we'll post links to their
work on the Physics World website,
physics world.com.
Next month, we'll be looking at Pele's tears,
which have more to do with volcanoes
than football.
Until then, thank you very much for listening.