Welcome to the Conversion Science and Instrument Formed podcast on collaboration.
I'm Paul Verschurek, and together with my colleague Julia Lug, we speak with Dr.
Sybrandt de Jong, Professor of High Energy Physics at Radboud University,
where he's also the Dean of the Faculty of Science and Technology.
Sybrandt is an expert on elementary particle and astroparticle physics,
and he's a member of the CERN Council.
Having conducted high energy experiments at large accelerator laboratories such
as CERN, Ferney Lab, and the Pierre Auger Observatory, he reflects on how large-scale
scientific collaboration is carried out. Sebrant, welcome to our podcast.
Hello, good afternoon. And Sebrant, before we really delve into the questions around collaboration,
it would be very helpful if you could situate us a little bit in your background
and in your career path that brought us together today.
Okay, well, I'm not completely sure what brought us together today,
but I can certainly sketch my career path.
So by building, I'm a physicist.
So I studied physics, but also I have a keen interest in mathematics,
computer science and astronomy.
So I also picked up parts of that in my study.
After graduating at the University of Amsterdam,
I did my PhD research with NICEF, already on a large experimental collaboration
in Hamburg, on the Hierar Ring at the Dacey Laboratory.
Of course, there I was a very junior member of that collaboration.
From that, after obtaining my PhD, I moved on to CERN.
I've been for eight years at CERN as a CERN fellow and a CERN associate,
really working on another large experiment, the OPAL experiment at the lab ring.
And then after my stint at CERN, I came to Nijmegen.
Somehow I called it coming back to Nijmegen, but I'm not really from Nijmegen,
as many people will hear.
I'm, of course, from the area of Amsterdam. But ever since I've been in Nijmegen,
well, it first was called the Catholic University Nijmegen, and now it's called
the Radboud University.
And there I served in several managerial tasks.
So I've been a department head, I've been the director of the School of Physics from 2000 to 2004.
After that, I was the founding director of the EMAP Research Institute for mathematics,
astrophysics and particle physics.
After that, I founded the Radboud Pre-University College of Science,
so an interface to high schools.
And then I moved on to serve on the CERN Council and the last three years as
the CERN Council President.
And of course, meanwhile, I did many things in both together,
always simultaneously in research and in governance and management.
So far, I've been able to always combine it.
So from the 1st of December on, I will be the Dean of the Faculty of Science.
And now for the first time in my life, I won't be able to combine that anymore
with doing really science myself.
So I had to give that up and I'm still sort of in the process of getting accustomed
to that. Right. Understood.
Well, that's an amazing career up to this point.
But then in the context of that experience, how do you define collaboration and what is it good for?
Well, collaboration already is a very interesting word for those who are not
Dutch listening to this podcast.
Collaboratie, collaboration in the Netherlands means quite the opposite,
I guess, from what it means anywhere else.
So in Dutch, it's like siding with the enemy, which is, of course,
more or less the opposite of collaboration.
I've always been working in large experimental collaborations.
So typically from ranging from, well, my very first collaboration I worked in
as a master's student was only like 60 people.
And then it scaled up from hundreds to thousands.
So for me, sort of a natural way of dealing with, say, large and complex situations.
So, in that sense, it's sort of my habitat, my natural habitat is working with large collaborations.
And these collaborations I do, of course, there's the common goal,
which is very unifying normally.
But of course, there's also a lot of fighting, a lot of infighting,
a lot of fighting between colleagues.
Because these collaborations are usually formed from like, well,
I've been in collaborations with over a thousand full professors.
And of course, they're all sort of king of their own empire.
But then in a much larger context, they have to work together as equals.
Okay. And okay, depending on, especially depending on nationality,
that gives more or less problems.
So, but then how does that work? So if you look at these larger,
the ones you just described, the really challenging ones, why would it work and why does it fail?
Could you could you describe that or give an example the foremost thing why
it it can work is to have a recognized common goal um typically the cycle in
a large experiment is that.
People dream of doing something spectacular
and then they they they they
find out okay i can i can't do it alone okay so i
need companions through this and then they round
up a couple of friends and then the circle gets larger and larger
and then at some point you have like the the
volume to to jump into the big
enterprise uh of course at
that point everybody is very enthusiastic about the common goal and
then usually things like letters of intent
are written which are of course a letter
of intent it sounds like a letter okay but usually it's
like a few hundred page book um with quite
detailed uh prognosis of what you want to
do and studies on how you're going to do it etc so at
that point there's still like one community then um
the trouble actually starts when these things get approved because
then suddenly like the first hurdle the first goal has been taken the approval
is there okay so what do we do now then of course typically there's years between
a new collaboration approved and a large apparatus really being built.
And so there's lots of designs in between. And this design phase is usually like a huge fight.
It's like a huge cage fight because you know you're condemned to one another,
yet you want to kill the enemy. You want your idea to prevail.
So it's really like a cage, an enormous big cage fight.
And then all sorts of things come in so not only the scientific argument of
what is best but also in the end my ideas may be second best but I pay for it
so it's my idea that's going to be built,
and actually this is one of the better arguments there are worse arguments that make people win,
which I won't go into but.
Okay and then sort of at some point okay this fighting takes usually a lot of
time and a lot of energy people do this actually typically for years in these large collaborations,
and at some point I realized like oh gee gee, we should have started building
half a year or a year ago to make it for the deadline of, say,
the accelerator to be finished or the promised start of the project.
And then sort of there's usually there's first a panic.
Then, of course, there's a lot of blaming and shaming on who's guilty of this.
And then people realize like, okay, this doesn't help to actually pull it together.
We have to really work together again so then
there's a there's usually a very rapid stage of
convergence uh typically like in
months where people realized okay we we just
and then and then um people just
tend to be more soft on giving up some of their own ideas etc like okay anything
okay as long as it's been built and so then there's this phase of of building
where people are pretty much unified and then you at the point where the whole thing is going to be,
commissioned uh there's usually like a great group spirit okay but things tend to work together.
Sometimes miraculously there's also great satisfaction and group identity and
big parties sometimes parties of thousands of people you i mean you build belief
in corona times um and uh And then,
of course, you have to start the experiment.
And usually the first few measurements are, again, really like the core business.
So everybody's unified, big success.
And then you get into the stage where you have to accumulate more data and to
either supersede the first publications or to do something entirely new.
You just need so much data that you need to wait for years.
Years, and then people start to fight again, because there's one analysis against
the other, because they're basically waiting for data, they get a little bit
bored, okay, the initial successes are over, and the goal becomes sort of fussy.
So my conclusion from all of this is that it's all in the goal, okay?
You can get people working together. It was a long story, but...
No, no, no, it's very good. The conclusion is relatively simple.
But, Saber, if we focus on the goal as the unifier, is that really enough?
Because in some sense, it's also, at some point, also these phases that you
described, right, of infighting, coalescing again, and so on.
In some sense, now you also have a limited resource that you know you have to
share in order to achieve your selfish objectives.
So that is our new element that comes in. So is the goal and actually still the same?
Yeah, but that's okay. So again, okay, the resources can play both parts.
Okay. So of course, when the project is defined, everybody realizes that,
okay, I cannot pay for this on my own.
So we will have to share it. And then everybody, of course,
immediately agrees that, yeah, of course, if we have to to share resources
we also have to share responsibility and then
share tasks and and okay fine
no problem but then especially in this design phase okay when people have already
committed basically their resources then it's like yeah but you give like a
thousand and i give ten thousand so i can tell you what you should do so there
the resources are abused again
okay and everything has to be built usually yes
yes before we go to that stage
i want to back up again to the goal because you're
talking about recognizing a common goal but how is that common goal constructed
in other words if you have a collaboration of eventually a thousand people are
a thousand people involved in constructing the common goal who constructs that common goal okay well,
again okay so usually of course there's like a couple of people that really pull the cart okay that.
Really express the the goal and and
usually those are the people that can sort of make good
presentations present it in a in a in a
in a very nice way so that it's convincing but
usually these goals um that are
then defined by individuals already i like living
in the community for quite some time so
um and that's where people then recognize it
say yeah yeah i'll i thought the same
and and then they join this collaboration uh of
course also projects actually exist where people are
so much ahead of the troops that they have marvelous ideas okay but if it's
not it's if it's not living in the community okay then they they consider that
they have this brilliant idea with the three of them okay and they have to find
like a half a billion bucks with the three of them and And it's not going to happen.
But then, Sebron, do you see a stage where the people who might be in front
of the troops start really an active, let's say, campaign?
Yes, that's usually what is being done, okay? Okay, so how is that structured?
Yeah, no, it's just like going for it, okay? Okay, so you have this idea,
and then typically you try to get conference talks.
You try to get, and also what is very powerful in our case is you try to get
invited at certain research institutes.
You present your case, and especially you go with these people for lunch and
maybe dinner, and you try to convince them that it's a really good case and
that it would be really marvelous if their group would be joining.
And the other thing that is in the first stages is that, of course,
at that point, it doesn't cost anything.
So it's practically resource-free because it's just an idea.
So you join this collaboration, and then it's just a little bit,
we sort out the business later, okay?
First, join us, okay, and do a few studies and run a few simulations and see
what you can contribute to the goals or to the ideas of realizing the goals. And then we see.
This is usually how collaboration is formed and then it's then sort of it acquires
sort of it goes over a critical mass,
but then you start looking for resources and then actually this helps because
you're already tied into all the other organizations the universities,
the research institutes etc and then each of them has to fight in their own
institute for a piece of the pie right But that means you first build a coalition around the idea,
which is a process that has psychological, social components to it.
Does that also lead to, let's say, a regression to the mean of the idea?
Do you see that also as a compromise?
Yes, of course. Like I said, at several stages, the idea is watered down.
There's no question about it.
Usually in the goals, okay, if you're still in the dreaming world,
okay, you can actually stack goals. So it doesn't matter too much.
Then when you get into the stage where you have to secure the resources,
then that's the first time you're confronted with reality.
And then you find out, okay, maybe you have to descope a couple of things,
or maybe you have to descope many things.
And then usually, of course, the people that can secure the money,
can secure the resources, their ideas will prevail.
Of course, usually also a couple of good ideas that are in common to everybody will also make it.
There's no doubt about that. But then some other ideas of people that are less
resourceful, they will have to go because they are compromised on that.
And in fact, it's true that sometimes good ideas go. Right, exactly.
But then, so now you form your consortium in some sense.
Now they started to compete with other consortia to dominate for the resource.
But now the whole process is extended over years so what's the mean what's the
duration of such a process?
10 years? So in our field the process of doing an experiment is typically that
you have a phase of 5 to 10 years which we call proto-collaboration so that's
where the collaboration is formed,
then typically you have a phase of 5.
Well sometimes even to 10 years where you try to secure resources it's also
true that you never secure the resources that you really need so you have to
decide at what point you're going just go,
so typically at 50 60 of the resources pledged you just go and you hope for the rest,
typically by the way this is a strong mechanism because
um this is like with all sort of large
as public projects if you go over a certain uh
volume it's too
big to fail so typically you will
get so typically what what happens is you find 50 or
60 percent and there's not so much problem in
finding another 30 percent it's usually the last 10 percent which is a problem
um because then the argument of too big to fail doesn't work anymore and for
that you have to descope This is typically also a pity because de-scoping for
the last 10% actually means that typically the apparatus only works half as well,
which is something that funding organizations do not always realize.
But yeah, this is how it goes. And then there's the time of construction.
Again, time of construction in our field is by now five to 10 years.
And then there's the time of exploitation, which is typically in our field now
going up to 20 years or 25 years.
So you're talking about...
40 years. You're talking about people that actually can spend their entire career,
doing one project. Right.
This is important, right, to understand, because now the question becomes,
if the goal is so critical in keeping this together,
how do you assure that that goal also stays intact over that whole period,
is propagated properly, and is also that you don't have mission drift,
right, so that the goal fragments?
No, in our case, okay, I've never seen that really happen.
So the goals are typically so well, like these are typically already questions
that are decades above the market.
So really big things that we want to solve.
Okay um some well what what are the most interesting cases of mission drift
of course is that you you you build something for a certain goal and then the
thing is built and then you switch the apparatus on and you look at the data
and you just find out something completely different.
We we don't call this mission drift okay it's just like okay you adapt your
goals would adapt if the implementation stage you find new information that
will change your goals or bring you...
Typically, the old goals are still there, but you acquire new goals.
And sometimes the new goals sort of overshadow the old goals.
So famous for that is that there was an experiment that tried to look at decay
of protons, proton in the elementary particle, and it's predicted to decay,
but with a very long lifetime because otherwise we wouldn't be sitting here.
And so you need a huge detector, and then you look for the one proton to decay,
one in enormously many protons.
So they build a huge detector, they wait for this proton to decay,
and what they found out is that for a completely different particle called neutrino,
they actually did some measurements which were done by the PhD student,
because, well, they have to do something while waiting for the proton to decay,
and they actually found out that these neutrinos, they change their nature.
So a neutrino could start as one particle and after some while could end up
as a completely different particle, still a neutrino, but a different neutrino, okay?
So they could completely sort of...
Change. This was a fantastic discovery.
But the thing is, if it runs over such a long period of time,
does it also mean you have different generations?
That's, for instance, the first push even by a senior generation that in the
end also will hate each other so much they cannot go on, but then there's a
younger group that comes in to push you to the next stage. That's part of the dynamics.
Typically, it's the old people just before or just after retirement that dream
of the new big things that they will actually never see working alive.
And then in the process, of course, they drag on the younger colleagues.
And the most interesting thing is that the peak of, say,
the young colleagues joining the collaboration is typically when the experiment
is already running for a while. So in the last stages.
And part of it is because the old guys in the experiment then are already busy
with dreaming up the next project. Right.
So we go from old to young in these collaborations.
Right. The average age goes down in steps, but it goes down along the way. Right.
So now we looked at the important challenge of goal setting and how this might
be maintained or might also change.
But you also mentioned that there are these serendipitous discoveries that were
not part of the original path. path, on the average, how is that balanced?
So if you look at the high-impact outcomes of all these huge experiments,
are most of them on the critical path of the original goal, or are they on this
serendipitous offshoot?
No, typically for all the experiments that I know, hardly any of them actually
have failed to fulfill their initial goal.
All. Of course, sometimes you're looking for things and the things turn out not to exist,
which is among the public and it's called a failure, but we think actually this
is success because that makes a paradigm shift because we always thought something
was there and now we discover it's not.
So that is a big discovery, basically, for us, because that means that your
common theory doesn't work, which is a big,
overturn of the thinking exactly so
in that sense in that sense the goals are nearly
always met like either you find what you
were looking for and in fact personally okay i always find like you have this
theoretical idea and you you set up to check it and then you check it and it
turns out to be true yeah it's like ticking a box okay it's really boring so
it's much nicer if you don't find it, or you find something completely different.
So now we looked at the goals, we looked at outcomes, but as you mentioned,
the process is complex and also contentious.
There are phases where people, like you said, are in a cage fight.
Now, another aspect of collaboration is that also the different participants
at least have a sense of trust in each other and in the process to move forward.
So how is trust then defined and maintained in such a complex process?
I don't know how trust is defined, but it turns out that even in these cage
fights or at the end of the cage fights, people make up.
So they they still know that
they are condemned to one another okay there's there's no way
they can they can have an exodus of
people like i the famous thing okay we have is that very often we have like
three ideas on certain detector construction and they're nearly equally good
and of course there are some deciding criteria and you could sort of say who's
best okay but but the second and third best are not so far off.
And so I heard one spokesman say, like, whatever I do, okay,
I will lose two-thirds of my collaboration in this decision.
And this is true. But we also know that if you lose two-thirds of your collaboration,
the whole thing is not going to happen.
So people also know that they can be sore about things, okay,
And they can sort of, yeah, what do you do then?
Okay, you kick in a door or so, and then you...
Go home and um but how
could how can we do how do you get a little bit of shopping you return and you
say well how can i help okay so you give in in the end but is trust not based
because in that sense physics is a very unique domain right it's one of the
few if to my knowledge the only area,
in science where people succeed in pulling off these huge collaborative initiatives
but But isn't that also not related to the availability of, let's say,
a theory that everybody trusts in some sense?
Yeah, that's also true. Like I said, that's also why there is so little goal drift.
Because usually the goals are already sort of stably defined.
And it also plays a role in what I said about there are a few forerunners that promote ideas,
but there is lots of followers that already thought like, well,
I had the same idea, okay, so I can as well join them and then we'll make it to success together.
So it means that the meme is already living in the crowds.
Exactly. And that, of course, has much to do with like an underlying theory,
which is commonly believed in and relatively stable.
So it's not like we change theory every couple of years.
Right. And so what you're saying is that the trust is in the thing itself,
in the ideas, in the concept, and not so much interpersonal trust.
Is that what you're saying?
Oh, there's also interpersonal trust. There's also interpersonal distrust.
But the interesting thing is
that even people that have mutual distrust in the end will work together.
And really cooperate. Is competition in the sense a positive thing for the outcome?
I don't mean competition over funds, but I mean competition in terms of ideas.
Yes, I think overall, yes. So what the competition makes is that people are
just sort of working harder,
going the extra mile in developing their ideas and in presenting their ideas
and getting the details right.
Because they know that if any of these has a little flaw, then they will be shot by the other party.
So in that sense, it works very much elevating the quality.
On the other hand okay practically always
it's like not quite the best idea that
wins but nearly the best idea but then our my
my feeling is that nearly the best idea then would still be much better than
in the case if there would have been no competition right it's actually quite
subtle the whole thing but now if you have competition about ideas in in in
science there are various characteristic mechanisms also to express this competition as,
for instance, in the critical dependence on peer review.
So do you see that people also use that mechanism to dominate or win in that competition? In which?
Because I'm trying to figure out what you mean, okay?
So if one part has to sort of be the referee of the other part. Exactly, yeah.
There's interdependence, In the background, there's interdependence because
everyone is, in some sense, a potential reviewer of everybody else.
Yes, yes. And in our fields, they're more or less permanently because it's sort of in the open.
So I think it's not that, okay? It's not that that influences things so much.
What does influence things is that, of course, some people are more powerful than others.
And uh so there there's there's also a lot of backroom policy so so a lot of
i mean basically all the important decisions are taken uh in the cafeteria.
Right so so so there's a lot of wheeling and dealing and hassling okay uh to,
to set up certain people against other people, et cetera.
So there's, of course, also physicists are completely human.
Yes. But now you have been at different sides of this process.
You have been in the middle of it.
But also, if you had the board of CERN, you have to manage it.
So do you then, from that perspective, are you able to really manage and or
engineer that process by, for instance, bringing people together in certain
ways or setting up forms of communication?
That's also where we distinguish things. Okay, so I was not in management.
I was in governance, which is a different game.
And in fact, most of the structures are set up in governance, not by management.
And actually, this is an important distinction distinction.
Because in governance, there's a little bit more distance because the governance
is always over a number and maybe even a large number of projects.
So it's more like a mechanism that would fit all the different things. Okay.
Well, management is much more trying to tailor
things to a certain situation and it really helps that the structures are set
up from a more global perspective.
Because I've also seen structures that have been set up to tailor the situation
and they nearly always end up getting messy because they were so much tailored to the situation.
New events arise they have to be adapted and then you have to add on things
and you have maybe to scrap a few things and add on more things and it should
sort of get a biologically grown structure and in the end,
that is very advantageous for the cowboys but not for the.
Straight thinking people yes, but also then the management becomes part of the
situation to be managed yes, exactly Exactly, okay, exactly.
But then from the governance perspective, what you can do is put in place procedures,
protocols to try to structure the collaboration.
Now, for CERN, did you also do that in a very deliberate way that you also knew?
Yeah, all the things are structured very deliberately.
So the idea is to have peer monitoring on practically everything.
So that's an important thing. And so those are people that are in the spare
monitoring that are very knowledgeable about what is going on,
but they're completely independent to both the thing they monitor and to the
body they actually report to.
So also it's not like the body that is in the governance is actually arranging
the monitoring, but it arranged it through a third party, which is an independent monitor.
So they can also sort of say things that are not very much to the liking of the governance.
And without consequences. And that is a very powerful structure.
So peer monitoring is a quality control system, but that has to operate on,
let's say, protocols of, let's say, communication, responsibility.
So are you also structuring the OSINT in a very specific way? Like hierarchy?
Well, this monitoring is usually sort of, there's not, okay,
in our case, there's not so much hierarchy in the monitoring.
So there's monitoring at, in principle, at a rather global level,
but they're allowed to actually go into the detail. tell.
So it monitors a large span of things.
So part of my career, I've been into one of these monitoring bodies.
And on the one hand, we were dealing with bolts that you should make sure that
they were not containing any magnetic material.
And on the other hand, of the spectrum, we were also dealing with the accountancy of.
Material requisitions and how this was done and how this was monitored and whether
all sorts of buying procedures were in place, etc.
So over the whole span, we were actually ...
But I'm a bit confused now.
Because The monitoring I understand, but the monitoring will very often have
to refer to certain standards to look at different parts of the process.
And these standards also must be defined.
And I would assume that's also part of the governance structure.
Well, okay, that's the other interesting thing, okay? So the real monitoring
is done by committees which have very little constraint.
And who establishes the committees? Who establishes the committees?
The committees are established by the governance structure.
Okay. So, of course, there's a subtlety there, because usually they are proposed
by management, but then have to be established by the governance structure.
But that sounds very loosely defined then, because that would also mean… That
part is loosely defined, okay?
Actually, the strict standards are, of course, things like safety, accountancy.
They are quite strictly defined, okay? But since we are in the process of building
things that have never been built before, it is very hard to set standards for this.
And we go for the common sense of the people in the review committees or in
the monitoring committees,
to see what is right and what is wrong. But then you don't try to then extract a rule book or so,
or a playbook from successful projects to say, well, this was the playbook of
this project and this could be standards for a future project. Oh, yeah.
But that's again not in the monitoring side, but that is in the implementation
side, so on the management level.
And yes, there is also like a strong idea
of how the collaboration
is formalized and there are variations on that but if you look at it and it's
from a distance okay then you see that the overall structure is the same and
the variations are minor yeah so one of the interesting things about these large
collaborations for example is they don't have a boss,
so the actually the the acting boss is called spokesperson so traditionally
Additionally, that was the person speaking to the management and the press when there was like, well,
to the management to say how things were going and to the press to say what they discovered.
And basically, that is the person now who is in charge of a collaboration.
But that person has no personnel. So there's no hierarchical relation between
that person and the rest of the collaboration.
Usually it's somebody from a larger group so he has like some of his group members
but typically in a large LHC experiment like 3,500 collaborators,
then the spokesperson has like at most 20 people that he or she can fire and
all the others okay there's no leverage,
how is the spokesperson so you have to do it on persuasion and the spokesperson is elected okay,
okay celebration so and that and
that actually that is an important thing because that that gives credit,
So you're elected and then of course your power in the collaboration comes from
the fact that you were elected.
You can always say like, okay, you don't like it then you shouldn't have chosen me.
People also vying and competing for this role of spokesperson?
Yeah, of course. This is highly prestigious. And again, so
there's like the formal thing of of uh there's the
competition and there are usually several candidates and then there's
the voting and then there's all the the the
things that you have in a voting process like people try to gain votes and and
they make all sorts of promises etc but there's also uh and that is maybe different
from a normal democracy or at least from a national democracy there there's
a lot of things is going on behind the scenes.
Right. So there's a lot of trading.
Like if I become spokesperson, then this and this person from your institute
gets this and this position in the collaboration. Right. Okay.
Okay. One thing, okay.
We, the Dutch, are very not good at this, okay? Mm-hmm. So that's why Italians always win.
Why? They're much better at these things, okay? We always think that it should
be fair and honest, et cetera, okay? And they don't care.
It should just be effective. Exactly. That's also something you mentioned earlier, right?
That there were cultural differences and how people approach that process.
And without necessarily wanting
to hear you say certain stereotypes about different cultures, still,
it would be useful to understand how these cultural backgrounds make a difference
in how collaborations get built up and work or fail, right?
So what are the basic dimensions there?
Well, again, okay, so in these large collaborations, it actually helps to have
a lot of culture because some of them are more risk-taking than others.
Some of them are more solid than others. And if you somehow can deploy all these
qualities more or less in the right places, then it's a very powerful combination.
And so, again, okay, this is in the process. So usually, like,
the more risk-taking people, they're the ones with the big dreams and the fancy
ideas, and they try things.
And sometimes they succeed and often they fail, but sometimes you get very good ideas from that.
And then in the end, okay, you have to have an apparatus that actually works.
Then there's also a large part of the community, which are just very solid working people.
And apart from the one or two highlights where we have a fantastic idea that
works, most of the apparatus is actually built on steady progress.
Progress and and so but this combination is is
quite powerful and it's and and again okay
you never get it perfectly sort of
everybody in the right position right but
there is a sort of a natural selection on that because the the the risk-taking
people they they tend to focus on the parts where they allow to take a risk
the people that are delivering solid work okay they They would go to places
where their solid work is appreciated.
So there is some sorting out mechanism, okay, with the nature of people sort
of fits with the thing they're doing.
Right. But then who are the real players in this context? Who are,
in the end, managing to grab the most control over that complex process?
Like you say, these are more the Italians or the Americans, the British.
Who are the strong players there?
No, in the end, often, okay, so what you see is like spokespeople,
et cetera, they're very often like more of the risk-taking types and the outgoing types.
And then there's also a very important role in all of these large enterprises
is something we call technical coordinator, which is just what it is, okay?
So it's like the person who coordinates all the technical stuff.
And therefore, you need a very solid and stress-resistant person.
And that also selects itself, okay? Because typically, people that are not robust
and stress-resistant will actually disappear within finite time. Sure.
But now, so we looked at the process.
Can you give an example of what is the biggest success of your time at CERN?
And what was the biggest failure? And what's the difference between these two?
Yeah, of my time at CERN. I had, of course, different times at CERN.
Oh, no, no. So you can choose.
Okay. Well, by far my biggest failure, I consider that I lost the detector.
So I was responsible for the detector, which actually melted.
It was not a small detector. It was actually a fairly substantial part of a large detector.
And that was because basically not all the security that should have been there was in place.
And then there was a series of human failure in my absence.
But okay, I still was responsible for it. So that was... How does it reflect
on the process of collaboration? That was a failure of collaboration?
Or it was a failure of you just not being there? No, no,
it was a failure on my part of sufficiently recognizing human weakness.
And trying to exclude the risks associated to that with the fact that there was like a sequence
of of human failures so a highly unlikely sequence but and then you can say okay well,
normally this wouldn't have happened it's not your fault etc but but any of
the failures that was that was there could have been prevented if the right
procedures would have been in place,
right and and so in that sense okay because after that you immediately see okay
if we had done this and this okay then this would not have happened and it would
have been presented in at least three or four different,
things that would not have failed.
But of course, what you learn is that you can be more careful in thinking out
all the possible scenarios.
And even if you think like, well, who on earth would do this?
Okay, if it can be done, somebody will do it. Yeah, exactly.
But in terms of the collaborative projects and... What's interesting is that
in that case again, okay, Okay, so this was a case in which the experiment was
actually coming to a grinding halt.
Not only that, it was actually one experiment out of four on the lap accelerator.
And so the accelerator had to be stopped. So the other three experiments were also victim.
So it was a big thing because you also have to realize that the running costs
of these things are about a million a day.
And this failure took out like one and a half months.
It's not like small problem so now you're the 60 million dollar man well,
this was spelled out to me very explicitly when I had to go to the director to,
explain how on earth this could have happened.
You feel very tiny at the time but I had nothing but support,
and even from the directory it was not like okay of course this is really bad
but the immediately like there's a switch like okay how do we how do we recuperate
from this how do we how we get back on track right but what do you see as the
biggest success then of of these large-scale experiments,
over the last decades what's the biggest success story the biggest success okay so okay,
Well, the biggest success of that, no, there's several dimensions.
So, of course, there have been like marvelous discoveries, expected and unexpected.
And we have a very different view of our universe, actually,
from the discoveries of the last, well, I would say three decades ago.
So this is one asset from this enterprises.
The other asset is that we have this worldwide collaboration.
So there's like, I don't know how many nationalities involved,
180 or so, like practically all nationalities in the world, peacefully cooperating.
I've seen Israelis and Palestine doing shifts together and actually getting
along, et cetera. So I consider that also to be a big success.
And then probably the biggest success for society is that we educated many people
in performing state-of-the-art,
beyond state-of-the-art things in large collaborations in an international setting.
And of all the people that come to these large experiments as a master's student
even, or a PhD student, or a postdoc,
in the end, 90% ends up not in research, but in some other part of society.
And there they play a large role. For example, the whole of ASML would not have been possible.
If not for the science that we do.
Not in terms of that we invented what they should do, because we didn't,
but we delivered the people. Right, exactly.
Go ahead, Julia. Sorry, Paul. If you were to construct,
a future major collaboration using Using your knowledge of what works very well,
using your knowledge of the frameworks that you had in place,
but mindful of the fact that failures are inevitable in areas of science where
there is not a basis of already understanding mechanisms,
as you say, in many of the parts of the project.
How do you try to compensate for the unknown when you are constructing a collaboration?
Compensate for the unknown when constructing a collaboration.
I think the best robustness you can gain is by having a pluriform collaboration,
to have many talents on board.
That is actually the main mitigation mechanism, is that if something fails,
you have so many different angles to look at it that you fix it again.
And also before it fails, to have so many different angles to look at things
that failure is avoided.
And there again, this competition within collaboration plays a large role because
if you're competing for building the same piece of equipment and you see a flaw in the other ones, okay,
you would not hesitate to actually say this will never work or your thing will
burn down on the first instance because, et cetera, okay?
Okay, so I think that's probably an important thing, is this competition and
collaboration and the diversity of talent.
But are you also describing a sort of humility in the face of the challenge,
that therefore also people give each other space?
Is that also what you were expressing here? No, I don't think it is.
But yeah, what I say, people only give other people space when they are more or less forced to do so.
In terms of things are more relaxed, okay, they don't get a millimeter.
And I think humility is not one of our virtues. Okay.
But I was thinking more about humility in the context of the theories that people try to pursue.
Because there, of course, you enter highly complex conceptual frameworks that
not always everybody understands all aspects of,
where you also need to have a sense of a common approach towards the more challenging
scientific objects that you're dealing with.
Yeah, but I'm not sure that, okay.
It's not like we're sort of in awe of the big things okay because that would
be sort of freezing us so in that sense there's no humility okay we think we
can find out how nature works we really do it's probably not true but we really do,
so there's a lot of confidence there I understand yeah because why would you
otherwise embark on such things.
If you think you're not going to understand it anyway in the end, you better play golf.
Well, but you could also see it like the constructs are of a certain complexity,
that I need to be in a continuous dialogue with others to get my head around it.
I'm not going to solve this on my own. Oh, yeah. No, no. Most people realize that.
And of course, there are also some individuals that think they know it all.
But most people realize that they need all this input from all the others.
And it's it's it's i think by
now it is probably cannot find
anybody anymore who can who is
actually even willing to claim that he understands uh one of the large experiments
like completely right and and so in that sense but it's not humility i think
it's it's because many people would like to, but they feel forced to be realistic.
But understanding the limits of your knowledge is a form of humility that stands
in contrast to over confidence, right? That balance.
No, no, okay. I would rather sort of phrase it in terms of optimism and pessimism.
And there again, in the collaboration, you find these hopeless optimists who think anything goes.
And then you also have some group of people that are always like, they'll not work.
Right, exactly. Okay. And yeah, and they both have a function.
Sure. Okay. It would be unhealthy for a collaboration not to have this group.
Yep. Now that I understand.
So before we go to the final stretch, I want to understand a little bit the
continuity of large-scale projects in physics.
So is it fair to say that the Manhattan Project was the first one.
Oh that i don't know that was really like a huge constructed project but of
course well wait a minute before
that okay the manhattan project is basically the answer on a sort of.
Uh you much larger scale of uh german projects uh both uh for building a an atom bomb and also in
rocket science, to call it that.
Yeah, but that scale, that's my point.
So they were also of a certain scale, okay?
And actually, then the Manhattan Project, of course, was independent,
and they really made it happen.
But it was like really, that was really like power.
It was an enormous project.
Yep. But you know, it was not the biggest project during the war by the US.
It was the second biggest the biggest one was for the B-29 superfortress but
my question would then be,
was the dynamics and the organization of these early projects like the Manhattan
Project already anticipating how they run today at organizations like CERN or
was there some transition point?
No, that's an interesting thing because of course the Manhattan Project was
really run as a military project,
And since then, that has been tried one more time for one of our projects,
which was the SSC, the Superconducting Super Collider, which ended up in demise.
Because it was at some point really converted into a military operation,
really with military leaders.
And that doesn't work with us. Okay. Again, okay, it's all about the goal.
So it probably works if you have like a world war going on, but it doesn't work for anything less.
Right. Yeah. Right. Exactly.
So how much is your science worth to you?
Yeah. Yeah, it's a lot worth, okay, but running that as a military operation didn't work.
Right, exactly. But now, if you look forward, and also if you look at the current
situation we're in, we have two big crises in front of us, the COVID pandemic
and ecological collapse,
sustainability of the planet.
Do you believe that our society could learn something from the rulebook of these
large-scale physics experiments?
Okay. Well, first of all, I'm already sort of, I heard a lot of people doing
it, so I'm not surprised anymore.
But I think there's quite a big difference between COVID.
Of course, COVID is just hitting us now very hard, okay? But COVID is just a
ripple compared to our ecological problem.
Okay? COVID will go over. Our ecological problem, probably not.
So it's of a quite different scale and so what maybe I think people in my field
and I think many people in my field are of the same opinion.
If not most. Because I think in my field, people can actually look like 20,
30, 40 years ahead and look at it in that perspective.
And also when you talk about COVID, it's not COVID-19, which is the problem.
Maybe it'll be COVID-22, COVID-25, COVID-27, COVID-28.
Or a completely different virus, right? No, that's why I label them all by year.
Right. This is how it's done. But we're not done yet with that type of vulnerability.
And so that requires something different than what we're doing now in fighting
this one virus or even this one type of virus.
So we have to think about how we organize this.
If it's everyone's question, can we learn from you guys?
Can we learn from you guys to then respond? Well, exactly this, okay?
That if you, of course, it's important that next week we cannot go to a cafe anymore.
Well, is it? Is it really?
Or is it that over the years, okay,
we will have a very limited life for 20 or 30 or 40 years, that our children
will have a completely different life because they will have to isolate one
from the other, more or less continuously.
So think a little bit further, okay?
So think about what you're doing in terms of a much longer timescale.
And of course, I mean, if there's an imminent disaster, okay, you should solve it.
But at the same time, you should live in a long-term perspective.
And that's, of course, in general missing.
Right. But my question was a more specific one.
Can we learn from the models that the physicists have developed for their large-scale
experiments to really also advance collaboration in society,
responding to large-scale experiments?
I don't think that in COVID it was because of our organization that they organized things that way.
But the fact that in a year's time you have a vaccine is due to this focused effort, okay?
So this is how we would have done it. But it's not because they looked at us
and they said, oh, we do it like those guys.
But because there is this huge common goal which just has to be met.
And then how do we bring that to the ecological challenge?
Challenge yeah so there my feeling is that apparently people are only compelled
to actually go for the goal,
when they really feel it when they recognize it so so.
As long as as you just drive your car okay you hardly notice from day to day
that that things are deteriorating I mean.
Yeah. So it's about times. Again, okay, it's about timescale.
It's about thinking about the long-term perspective.
And I think, in general, humans are not very well adapted to that.
No, we're not. Which is also completely logical in terms of looking at the evolution, okay?
Because if something is threatening you today, you should immediately respond to survive.
Survive and if it's threatening you in 20 years you
can still produce some offspring and you and you survive that
way but now do you believe humans will be ever able to collaborate effectively
to answer these kinds of challenges will are we able intrinsically yes in particle
physics we are in in astronomy we are okay there's more fields like this so
there are fields in which it has been demonstrated.
So, yes, it can be done. But I think what may be important there,
of course, is the subset of people that you...
It's also selection bias, right? Because it works in physics because it works in physics.
But maybe in other domains of human endeavor, However, the conditions are not
conducive to actually instill these kinds of collaboration because of whatever,
competitive forces, resource limitations, what have you.
Well, it's more like a short-term win.
Sure. That is the most destructive force, I think, for these things. Yeah.
But now, if you could change one thing in humans in order to make them successful
collectively, also as non-physicists, you can change one thing. What would you change?
Probably would help if you could kill the instant satisfaction gene,
alright Cybron de Jong, thank you very much for this conversation ok hi,
you listened to one of our podcasts in the series on collaboration,
produced by the Ernst Trommel Forum and the Conversion Science Network you can
find more episodes on our website.