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Idea Machines

Nov 27, 2023

A conversation with Tim Hwang about historical simulations, the interaction of policy and science, analogies between research ecosystems and the economy, and so much more. 


  • Historical Simulations
  • Macroscience
  • Macro-metrics for science
  • Long science
  • The interaction between science and policy
  • Creative destruction in research
  • “Regulation” for scientific markets
  • Indicators for the health of a field or science as a whole
  • “Metabolism of Science”
  • Science rotation programs
  • Clock speeds of Regulation vs Clock Speeds of Technology




[00:02:02] Ben: Wait, so tell me more about the historical LARP that you're doing. Oh,

[00:02:07] Tim: yeah. So this comes from like something I've been thinking about for a really long time, which is You know in high school, I did model UN and model Congress, and you know, I really I actually, this is still on my to do list is to like look into the back history of like what it was in American history, where we're like, this is going to become an extracurricular, we're going to model the UN, like it has all the vibe of like, after World War II, the UN is a new thing, we got to teach kids about international institutions.

Anyways, like, it started as a joke where I was telling my [00:02:35] friend, like, we should have, like, model administrative agency. You know, you should, like, kids should do, like, model EPA. Like, we're gonna do a rulemaking. Kids need to submit. And, like, you know, there'll be Chevron deference and you can challenge the rule.

And, like, to do that whole thing. Anyways, it kind of led me down this idea that, like, our, our notion of simulation, particularly for institutions, is, like, Interestingly narrow, right? And particularly when it comes to historical simulation, where like, well we have civil war reenactors, they're kind of like a weird dying breed, but they're there, right?

But we don't have like other types of historical reenactments, but like, it might be really valuable and interesting to create communities around that. And so like I was saying before we started recording, is I really want to do one that's a simulation of the Cuban Missile Crisis. But like a serious, like you would like a historical reenactment, right?

Yeah. Yeah. It's like everybody would really know their characters. You know, if you're McNamara, you really know what your motivations are and your background. And literally a dream would be a weekend simulation where you have three teams. One would be the Kennedy administration. The other would be, you know, Khrushchev [00:03:35] and the Presidium.

And the final one would be the, the Cuban government. Yeah. And to really just blow by blow, simulate that entire thing. You know, the players would attempt to not blow up the world, would be the idea.

[00:03:46] Ben: I guess that's actually the thing to poke, in contrast to Civil War reenactment. Sure, like you know how

[00:03:51] Tim: that's gonna end.


[00:03:52] Ben: and it, I think it, that's the difference maybe between, in my head, a simulation and a reenactment, where I could imagine a simulation going

[00:04:01] Tim: differently. Sure, right.

[00:04:03] Ben: Right, and, and maybe like, is the goal to make sure the same thing happened that did happen, or is the goal to like, act? faithfully to

[00:04:14] Tim: the character as possible.

Yeah, I think that's right, and I think both are interesting and valuable, right? But I think one of the things I'm really interested in is, you know, I want to simulate all the characters, but like, I think one of the most interesting things reading, like, the historical record is just, like, operating under deep uncertainty about what's even going on, right?

Like, for a period of time, the American [00:04:35] government is not even sure what's going on in Cuba, and, like, you know, this whole question of, like, well, do we preemptively bomb Cuba? Do we, we don't even know if the, like, the warheads on the island are active. And I think I would want to create, like, similar uncertainty, because I think that's where, like, that's where the strategic vision comes in, right?

That, like, you have the full pressure of, like, Maybe there's bombs on the island. Maybe there's not even bombs on the island, right? And kind of like creating that dynamic. And so I think simulation is where there's a lot, but I think Even reenactment for some of these things is sort of interesting. Like, that we talk a lot about, like, oh, the Cuban Missile Crisis.

Or like, the other joke I had was like, we should do the Manhattan Project, but the Manhattan Project as, like, historical reenactment, right? And it's kind of like, you know, we have these, like, very, like off the cuff or kind of, like, stereotype visions of how these historical events occur. And they're very stylized.

Yeah, exactly, right. And so the benefit of a reenactment that is really in detail Yeah. is like, oh yeah, there's this one weird moment. You know, like that, that ends up being really revealing historical examples. And so even if [00:05:35] you can't change the outcome, I think there's also a lot of value in just doing the exercise.

Yeah. Yeah. The, the thought of

[00:05:40] Ben: in order to drive towards this outcome that I know. Actually happened I wouldn't as the character have needed to do X. That's right That's like weird nuanced unintuitive thing,

[00:05:50] Tim: right? Right and there's something I think about even building into the game Right, which is at the very beginning the Russians team can make the decision on whether or not they've even actually deployed weapons into the cube at all, yeah, right and so like I love that kind of outcome right which is basically like And I think that's great because like, a lot of this happens on the background of like, we know the history.

Yeah. Right? And so I think like, having the team, the US team put under some pressure of uncertainty. Yeah. About like, oh yeah, they could have made the decision at the very beginning of this game that this is all a bluff. Doesn't mean anything. Like it's potentially really interesting and powerful, so.

[00:06:22] Ben: One precedent I know for this completely different historical era, but there's a historian, Ada Palmer, who runs

[00:06:30] Tim: a simulation of a people election in her class every year. That's so good. [00:06:35] And

[00:06:36] Ben: it's, there, you know, like, it is not a simulation.

[00:06:40] Tim: Or,

[00:06:41] Ben: sorry, excuse me, it is not a reenactment. In the sense that the outcome is indeterminate.

[00:06:47] Tim: Like, the students

[00:06:48] Ben: can determine the outcome. But... What tends to happen is like structural factors emerge in the sense that there's always a war. Huh. The question is who's on which sides of the war? Right, right. And what do the outcomes of the war actually entail? That's right. Who

[00:07:05] Tim: dies? Yeah, yeah. And I

[00:07:07] Ben: find that that's it's sort of Gets at the heart of the, the great

[00:07:12] Tim: man theory versus the structural forces theory.

That's right. Yeah. Like how much can these like structural forces actually be changed? Yeah. And I think that's one of the most interesting parts of the design that I'm thinking about right now is kind of like, what are the things that you want to randomize to impose different types of like structural factors that could have been in that event?

Right? Yeah. So like one of the really big parts of the debate at XCOM in the [00:07:35] early phases of the Cuban Missile Crisis is You know, McNamara, who's like, right, he runs the Department of Defense at the time. His point is basically like, look, whether or not you have bombs in Cuba or you have bombs like in Russia, the situation has not changed from a military standpoint.

Like you can fire an ICBM. It has exactly the same implications for the U. S. And so his, his basically his argument in the opening phases of the Cuban Missile Crisis is. Yeah. Which is actually pretty interesting, right? Because that's true. But like, Kennedy can't just go to the American people and say, well, we've already had missiles pointed at us.

Some more missiles off, you know, the coast of Florida is not going to make a difference. Yeah. And so like that deep politics, and particularly the politics of the Kennedy administration being seen as like weak on communism. Yeah. Is like a huge pressure on all the activity that's going on. And so it's almost kind of interesting thinking about the Cuban Missile Crisis, not as like You know us about to blow up the world because of a truly strategic situation but more because of like the local politics make it so difficult to create like You know situations where both sides can back down [00:08:35] successfully.

Basically. Yeah

[00:08:36] Ben: The the one other thing that my mind goes to actually to your point about it model UN in schools. Huh, right is Okay, what if? You use this as a pilot, and then you get people to do these

[00:08:49] Tim: simulations at

[00:08:50] Ben: scale. Huh. And that's actually how we start doing historical counterfactuals. Huh.

Where you look at, okay, you know, a thousand schools all did a simulation of the Cuban Missile Crisis. In those, you know, 700 of them blew

[00:09:05] Tim: up the world. Right, right.

[00:09:07] Ben: And it's, it actually, I think it's, That's the closest

[00:09:10] Tim: thing you can get to like running the tape again. Yeah. I think that's right. And yeah, so I think it's, I think it's a really underused medium in a lot of ways.

And I think particularly as like you know, we just talk, talk like pedagogically, like it's interesting that like, it seems to me that there was a moment in American pedagogical history where like, this is a good way of teaching kids. Like, different types of institutions. And like, but it [00:09:35] hasn't really matured since that point, right?

Of course, we live in all sorts of interesting institutions now. And, and under all sorts of different systems that we might really want to simulate. Yeah. And so, yeah, this kind of, at least a whole idea that there's lots of things you could teach if you, we like kind of opened up this way of kind of like, Thinking about kind of like educating for about institutions.

Right? So

[00:09:54] Ben: that is so cool. Yeah, I'm going to completely,

[00:09:59] Tim: Change. Sure. Of course.

[00:10:01] Ben: So I guess. And the answer could be no, but is, is there connections between this and your sort of newly launched macroscience

[00:10:10] Tim: project?

There is and there isn't. Yeah, you know, I think like the whole bid of macroscience which is this project that I'm doing as part of my IFP fellowship. Yeah. Is really the notion that like, okay, we have all these sort of like interesting results that have come out of metascience. That kind of give us like, kind of like the beginnings of a shape of like, okay, this is how science might work and how we might like get progress to happen.

And you know, we've got [00:10:35] like a bunch of really compelling hypotheses. Yeah. And I guess my bit has been like, I kind of look at that and I squint and I'm like, we're, we're actually like kind of in the early days of like macro econ, but for science, right? Which is like, okay, well now we have some sense of like the dynamics of how the science thing works.

What are the levers that we can start, like, pushing and pulling, and like, what are the dials we could be turning up and turning down? And, and, you know, I think there is this kind of transition that happens in macro econ, which is like, we have these interesting results and hypotheses, but there's almost another...

Generation of work that needs to happen into being like, oh, you know, we're gonna have this thing called the interest rate Yeah, and then we have all these ways of manipulating the money supply and like this is a good way of managing like this economy Yeah, right and and I think that's what I'm chasing after with this kind of like sub stack but hopefully the idea is to build it up into like a more coherent kind of framework of ideas about like How do we make science policy work in a way that's better than just like more science now quicker, please?

Yeah, right, which is I think we're like [00:11:35] we're very much at at the moment. Yeah, and in particular I'm really interested in the idea of chasing after science almost as like a Dynamic system, right? Which is that like the policy levers that you have You would want to, you know, tune up and tune down, strategically, at certain times, right?

And just like the way we think about managing the economy, right? Where you're like, you don't want the economy to overheat. You don't want it to be moving too slow either, right? Like, I am interested in kind of like, those types of dynamics that need to be managed in science writ large. And so that's, that's kind of the intuition of the project.

[00:12:04] Ben: Cool.

I guess, like, looking at macro, how did we even decide, macro econ,

[00:12:14] Tim: how did we even decide that the things that we're measuring are the right things to measure? Right? Like,

[00:12:21] Ben: isn't it, it's like kind of a historical contingency that, you know, it's like we care about GDP

[00:12:27] Tim: and the interest rate. Yeah. I think that's right.

I mean in, in some ways there's a triumph of like. It's a normative triumph, [00:12:35] right, I think is the argument. And you know, I think a lot of people, you hear this argument, and it'll be like, And all econ is made up. But like, I don't actually think that like, that's the direction I'm moving in. It's like, it's true.

Like, a lot of the things that we selected are arguably arbitrary. Yeah. Right, like we said, okay, we really value GDP because it's like a very imperfect but rough measure of like the economy, right? Yeah. Or like, oh, we focus on, you know, the money supply, right? And I think there's kind of two interesting things that come out of that.

One of them is like, There's this normative question of like, okay, what are the building blocks that we think can really shift the financial economy writ large, right, of which money supply makes sense, right? But then the other one I think which is so interesting is like, there's a need to actually build all these institutions.

that actually give you the lever to pull in the first place, right? Like, without a federal reserve, it becomes really hard to do monetary policy. Right. Right? Like, without a notion of, like, fiscal policy, it's really hard to do, like, Keynesian as, like, demand side stuff. Right. Right? And so, like, I think there's another project, which is a [00:13:35] political project, to say...

Okay, can we do better than just grants? Like, can we think about this in a more, like, holistic way than simply we give money to the researchers to work on certain types of problems. And so this kind of leads to some of the stuff that I think we've talked about in the past, which is like, you know, so I'm obsessed right now with like, can we influence the time horizon of scientific institutions?

Like, imagine for a moment we had a dial where we're like, On average, scientists are going to be thinking about a research agenda which is 10 years from now versus next quarter. Right. Like, and I think like there's, there's benefits and deficits to both of those settings. Yeah. But man, if I don't hope that we have a, a, a government system that allows us to kind of dial that up and dial that down as we need it.

Right. Yeah. The, the,

[00:14:16] Ben: perhaps, quite like, I guess a question of like where the analogy like holds and breaks down. That I, that I wonder about is, When you're talking about the interest rate for the economy, it kind of makes sense to say [00:14:35] what is the time horizon that we want financial institutions to be thinking on.

That's like roughly what the interest rate is for, but it, and maybe this is, this is like, I'm too,

[00:14:49] Tim: my note, like I'm too close to the macro,

[00:14:51] Ben: but thinking about. The fact that you really want people doing science on like a whole spectrum of timescales. And, and like, this is a ill phrased question,

[00:15:06] Tim: but like, I'm just trying to wrap my mind around it.

Are you saying basically like, do uniform metrics make sense? Yeah, exactly. For

[00:15:12] Ben: like timescale, I guess maybe it's just. is an aggregate thing.

[00:15:16] Tim: Is that? That's right. Yeah, I think that's, that's, that's a good critique. And I think, like, again, I think there's definitely ways of taking the metaphor too far.

Yeah. But I think one of the things I would say back to that is It's fine to imagine that we might not necessarily have an interest rate for all of science, right? So, like, you could imagine saying, [00:15:35] okay, for grants above a certain size, like, we want to incentivize certain types of activity. For grants below a certain size, we want different types of activity.

Right, another way of slicing it is for this class of institutions, we want them to be thinking on these timescales versus those timescales. Yeah. The final one I've been thinking about is another way of slicing it is, let's abstract away institutions and just think about what is the flow of all the experiments that are occurring in a society?

Yeah. And are there ways of manipulating, like, the relative timescales there, right? And that's almost like, kind of like a supply based way of looking at it, which is... All science is doing is producing experiments, which is like true macro, right? Like, I'm just like, it's almost offensively simplistic. And then I'm just saying like, okay, well then like, yeah, what are the tools that we have to actually influence that?

Yeah, and I think there's lots of things you could think of. Yeah, in my mind. Yeah, absolutely. What are some, what are some that are your thinking of? Yeah, so I think like the two that I've been playing around with right now, one of them is like the idea of like, changing the flow of grants into the system.

So, one of the things I wrote about in Microscience just the past week was to think [00:16:35] about, like sort of what I call long science, right? And so the notion here is that, like, if you look across the scientific economy, there's kind of this rough, like, correlation between size of grant and length of grant.

Right, where so basically what it means is that like long science is synonymous with big science, right? You're gonna do a big ambitious project. Cool. You need lots and lots and lots of money Yeah and so my kind of like piece just briefly kind of argues like but we have these sort of interesting examples like the You know Like framing a heart study which are basically like low expense taking place over a long period of time and you're like We don't really have a whole lot of grants that have that Yeah.

Right? And so the idea is like, could we encourage that? Like imagine if we could just increase the flow of those types of grants, that means we could incentivize more experiments that take place like at low cost over long term. Yeah. Right? Like, you know, and this kind of gets this sort of interesting question is like, okay, so what's the GDP here?

Right? Like, or is that a good way of cracking some of the critical problems that we need to crack right now? Right? Yeah. And it's kind of where the normative part gets into [00:17:35] it is like, okay. So. You know, one way of looking at this is the national interest, right? We say, okay, well, we really want to win on AI.

We really want to win on, like, bioengineering, right? Are there problems in that space where, like, really long term, really low cost is actually the kind of activity we want to be encouraging? The answer might be no, but I think, like, it's useful for us to have, like, that. Color in our palette of things that we could be doing Yeah.

In like shaping the, the dynamics of science. Yeah. Yeah.

[00:18:01] Ben: I, I mean, one of the things that I feel like is missing from the the meta science discussion Mm-Hmm. is, is even just, what are those colors? Mm-Hmm. like what, what are the, the different and almost parameters of

[00:18:16] Tim: of research. Yeah. Right, right, right.

And I think, I don't know, one of the things I've been thinking about, which I'm thinking about writing about at some point, right, is like this, this view is, this view is gonna piss people off in some ways, because where it ultimately goes is this idea that, like, like, the scientist or [00:18:35] science Is like a system that's subject to the government, or subject to a policy maker, or a strategist.

Which like, it obviously is, right? But like, I think we have worked very hard to believe that like, The scientific market is its own independent thing, And like, that touching or messing with it is like, a not, not a thing you should do, right? But we already are. True, that's kind of my point of view, yeah exactly.

I think we're in some ways like, yeah I know I've been reading a lot about Keynes, I mean it is sort of interesting that it does mirror... Like this kind of like Great Depression era economic thinking, where you're basically like the market takes care of itself, like don't intervene. In fact, intervening is like the worst possible thing you could do because you're only going to make this worse.

And look, I think there's like definitely examples of like kind of like command economy science that like don't work. Yes. But like, you know, like I think most mature people who work in economics would say there's some room for like at least like Guiding the system. Right. And like keeping it like in balance is like [00:19:35] a thing that should be attempted and I think it's kind of like the, the, the argument that I'm making here.

Yeah. Yeah. I

[00:19:41] Ben: mean, I think that's,

[00:19:42] Tim: that's like the meta meta thing. Right. Right. Is even

[00:19:46] Ben: what, what level of intervention, like, like what are the ways in which you can like usefully intervene and which, and what are the things that are, that are foolish and kind of. crEate the, the,

[00:20:01] Tim: Command economy.

That's right. Yeah, exactly. Right. Right. And I think like, I think the way through is, is maybe in the way that I'm talking about, right? Which is like, you can imagine lots of bad things happen when you attempt to pick winners, right? Like maybe the policymaker whoever we want to think of that as like, is it the NSF or NIH or whatever?

Like, you know, sitting, sitting in their government bureaucracy, right? Like, are they well positioned to make a choice about who's going to be the right solution to a problem? Maybe yes, maybe no. I think we can have a debate about that, right? But I think there's a totally reasonable position, which is they're not in it, so they're not well positioned to make that call.

Yeah. [00:20:35] Right? But, are they well positioned to maybe say, like, if we gave them a dial that was like, we want researchers to be thinking about this time horizon versus that time horizon? Like, that's a control that they actually may be well positioned to inform on. Yeah. As an outsider, right? Yeah. Yeah. And some of this I think, like, I don't know, like, the piece I'm working on right now, which will be coming out probably Tuesday or Wednesday, is you know, some of this is also like encouraging creative destruction, right?

Which is like, I'm really intrigued by the idea that like academic fields can get so big that they become they impede progress. Yes. Right? And so this is actually a form of like, I like, it's effectively an intellectual antitrust. Yeah. Where you're basically like, Basically, like the, the role of the scientific regulator is to basically say these fields have gotten so big that they are actively reducing our ability to have good dynamism in the marketplace of ideas.

And in this case, we will, we will announce new grant policies that attempt to break this up. And I actually think that like, that is pretty spicy for a funder to do. But like actually maybe part of their role and maybe we should normalize that [00:21:35] being part of their role. Yeah. Yeah, absolutely.

[00:21:37] Ben: I I'm imagining a world where There are, where this, like, sort of the macro science is as divisive as

[00:21:47] Tim: macroeconomics.

[00:21:48] Ben: Right? Because you have, you have your like, your, your like, hardcore free market people. Yeah. Zero government intervention. Yeah, that's right. No antitrust. No like, you know, like abolish the Fed. Right, right. All of that. Yeah, yeah. And I look forward to the day. When there's there's people who are doing the same thing for research.

[00:22:06] Tim: Yeah, that's right. Yeah. Yeah when I think that's actually I mean I thought part of a lot of meta science stuff I think is this kind of like interesting tension, which is that like look politically a lot of those people in the space are Pro free market, you know, like they're they're they're liberals in the little L sense.

Yeah, like at the same time Like it is true that kind of like laissez faire science Has failed because we have all these examples of like progress slowing down Right? Like, I don't know. Like, I think [00:22:35] that there is actually this interesting tension, which is like, to what degree are we okay with intervening in science to get better outcomes?

Yeah. Right? Yeah. Well, as,

[00:22:43] Ben: as I, I might put on my hat and say, Yeah, yeah. Maybe, maybe this is, this is me saying true as a fair science has never been tried. Huh, right. Right? Like, that, that, that may be kind of my position. Huh. But anyways, I... And I would argue that, you know, since 1945, we have been, we haven't had laissez faire

[00:23:03] Tim: science.

Oh, interesting.

[00:23:04] Ben: Huh. Right. And so I'm, yeah, I mean, it's like, this is in

[00:23:09] Tim: the same way that I think

[00:23:11] Ben: a very hard job for macroeconomics is to say, well, like, do we need

[00:23:15] Tim: more or less intervention? Yeah. Yeah.

[00:23:17] Ben: What is the case there? I think it's the same thing where. You know, a large amount of science funding does come from the government, and the government is opinionated about what sorts of things

[00:23:30] Tim: it funds.

Yeah, right. Right. And you

[00:23:33] Ben: can go really deep into that. [00:23:35] So, so I

[00:23:35] Tim: would. Yeah, that's actually interesting. That flips it. It's basically like the current state of science. is right now over regulated, is what you'd say, right? Or, or

[00:23:44] Ben: badly regulated. Huh, sure. That is the argument I would say, very concretely, is that it's badly regulated.

And, you know, I might almost argue that it is... It's both over and underregulated in the sense that, well, this is, this is my, my whole theory, but like, I think that there, we need like some pockets where it's like much less regulated. Yeah. Right. Where you're, and then some pockets where you're really sort of going to be like, no.

You don't get to sort of tune this to whatever your, your project, your program is. Yeah, right, right. You're gonna be working with like

[00:24:19] Tim: these people to do this thing. Yeah, yeah. Yeah, and I think there actually is interesting analogies in like the, the kind of like economic regulation, economic governance world.

Yeah. Where like the notion is markets generally work well, like it's a great tool. Yeah. Like let it run. [00:24:35] Right. But basically that there are certain failure states that actually require outside intervention. And I think what's kind of interesting in thinking about in like a macro scientific, if you will, context is like, what are those failure states for science?

Like, and you could imagine a policy rule, which is the policymaker says, we don't intervene until we see the following signals emerging in a field or in a region. Right. And like, okay, that's, that's the trigger, right? Like we're now in recession mode, you know, like there's enough quarters of this problem of like more papers, but less results.

You know, now we have to take action, right? Oh, that's cool. Yeah, yeah. That would be, that would be very interesting. And I think that's like, that's good, because I think like, we end up having to think about like, you know, and again, this is I think why this is a really exciting time, is like MetaScience has produced these really interesting results.

Now we're in the mode of like, okay, well, you know, on that policymaker dashboard, Yeah. Right, like what's the meter that we're checking out to basically be like, Are we doing well? Are we doing poorly? Is this going well? Or is this going poorly? Right, like, I think that becomes the next question to like, make this something practicable Yeah.

For, for [00:25:35] actual like, Right. Yeah. Yeah. One of my frustrations

[00:25:38] Ben: with meta science

[00:25:39] Tim: is that it, I

[00:25:41] Ben: think is under theorized in the sense that people generally are doing these studies where they look at whatever data they can get. Huh. Right. As opposed to what data should we be looking at? What, what should we be looking for?

Yeah. Right. Right. And so, so I would really like to have it sort of be flipped and say, okay, like this At least ideally what we would want to measure maybe there's like imperfect maybe then we find proxies for that Yeah, as opposed to just saying well, like here's what we can measure. It's a proxy for

[00:26:17] Tim: okay.

That's right, right Yeah, exactly. And I think a part of this is also like I mean, I think it is like Widening the Overton window, which I think like the meta science community has done a good job of is like trying to widen The Overton window of what funders are willing to do. Yeah. Or like what various existing incumbent actors are willing to [00:26:35] do.

Because I think one way of getting that data is to run like interesting experiments in this space. Right? Like I think one of the things I'm really obsessed with right now is like, okay, imagine if you could change the overhead rate that universities charge on a national basis. Yeah. Right? Like, what's that do to the flow of money through science?

And is that like one dial that's actually like On the shelf, right? Like, we actually have the ability to influence that if we wanted to. Like, is that something we should be running experiments against and seeing what the results are? Yeah, yeah.

[00:27:00] Ben: Another would be earmarking. Like, how much money is actually earmarked

[00:27:05] Tim: for different things.

That's right, yeah, yeah. Like, how easy it is to move money around. That's right, yeah. I heard actually a wild story yesterday about, do you know this whole thing, what's his name? It's apparently a very wealthy donor. That has convinced the state of Washington's legislature to the UW CS department. it's like, it's written into law that there's a flow of money that goes directly to the CS department.

I don't think CS departments need more money. I [00:27:35] know, I know, but it's like, this is a really, really kind of interesting, like, outcome. Yeah. Which is like a very clear case of basically just like... Direct subsidy to like, not, not just like a particular topic, but like a particular department, which I think is like interesting experiment.

I don't like, I don't know what's been happening there, but yeah. Yeah. Yeah. Natural, natural experiment.

[00:27:50] Ben: Totally. Has anybody written down, I assume the answer is no, but it would be very interesting if someone actually wrote down a list of sort of just all the things you

[00:28:00] Tim: could possibly

[00:28:00] Ben: want to pay attention to, right?

Like, I mean, like. Speaking of CS, it'd be very interesting to see, like, okay, like, what fraction of the people who, like, get PhDs in an area, stay in this area, right? Like, going back to the, the

[00:28:15] Tim: health of a field or something, right? Yeah, yeah. I think that's right. I, yeah. And I think that those, those types of indicators are interesting.

And then I think also, I mean, in the spirit of like it being a dynamic system. Like, so a few years back I read this great bio by Sebastian Malaby called The Man Who Knew, which is, it's a bio of Alan Greenspan. So if you want to ever read, like, 800 pages about [00:28:35] Alan Greenspan, book for you. It's very good.

But one of the most interesting parts about it is that, like, there's a battle when Alan Greenspan becomes head of the Fed, where basically he's, like, extremely old school. Like, what he wants to do is he literally wants to look at, like, Reams of data from like the steel industry. Yeah, because that's kind of got his start And he basically is at war with a bunch of kind of like career People at the Fed who much more rely on like statistical models for predicting the economy And I think what's really interesting is that like for a period of time actually Alan Greenspan has the edge Because he's able to realize really early on that like there's It's just changes actually in like the metabolism of the economy that mean that what it means to raise the interest rate or lower the interest rate has like very different effects than it did like 20 years ago before it got started.

Yeah. And I think that's actually something that I'm also really quite interested in science is basically like When we say science, people often imagine, like, this kind of, like, amorphous blob. But, like, I think the metabolism is changing all the [00:29:35] time. And so, like, what we mean by science now means very different from, like, what we mean by science, like, even, like, 10 to 20 years ago.

Yes. And, like, it also means that all of our tactics need to keep up with that change, right? And so, one of the things I'm interested in to your question about, like, has anyone compiled this list of, like, science health? Or the health of science, right? It's maybe the right way of thinking about it. is that, like, those indicators may mean very different things at different points in time, right?

And so part of it is trying to understand, like, yeah, what is the state of the, what is the state of this economy of science that we're talking about? Yeah. You're kind of preaching

[00:30:07] Ben: to the, to the choir. In the sense that I'm, I'm always, I'm frustrated with the level of nuance that I feel like many people who are discussing, like, science, quote, making air quotes, science and research, are, are talking about in the sense that.

They very often have not actually like gone in and been part of the system.

Huh, right. And I'm, I'm open to the fact that [00:30:35] you

[00:30:35] Tim: don't need to have got like

[00:30:36] Ben: done, been like a professional researcher to have an opinion

[00:30:41] Tim: or, or come up with ideas about it.

[00:30:43] Ben: Yeah. But at the same time, I feel like

[00:30:46] Tim: there's, yeah, like, like, do you, do you think about that tension at all?

Yeah. I think it's actually incredibly valuable. Like, I think So I think of like Death and Life of Great American Cities, right? Which is like, the, the, the really, one of the really, there's a lot of interesting things about that book. But like, one of the most interesting things is sort of the notion that like, you had a whole cabal of urban planners that had this like very specific vision about how to get cities to work right and it just turns out that like if you like are living in soho at a particular time and you like walk along the street and you like take a look at what's going on like there's always really actually super valuable things to know about yeah that like are only available because you're like at that like ultra ultra ultra ultra micro level and i do think that there's actually some potential value in there like one of the things i would love to be able to set up, like, in the community of MetaScience or whatever you want to call it, right, [00:31:35] is the idea that, like, yeah, you, you could afford to do, like, very short tours of duty, where it's, like, literally, you're just, like, spending a day in a lab, right, and, like, to have a bunch of people go through that, I think, is, like, really, really helpful and so I think, like, thinking about, like, what the rotation program for that looks like, I think would be cool, like, you, you should, you should do, like, a six month stint at the NSF just to see what it looks like.

Cause I think that kind of stuff is just like, you know, well, A, I'm selfish, like I would want that, but I also think that like, it would also allow the community to like, I think be, be thinking about this in a much more applied way. Yeah. Yeah. Yeah.

[00:32:08] Ben: I think it's the, the meta question there for, for everything, right?

Is how much in the

weeds, like, like what am I trying to say? The. It is possible both to be like two in the weeds. Yeah, right and then also like too high level Yeah, that's right. And in almost like what what is the the right amount or like? Who, who should

[00:32:31] Tim: be talking to whom in that? That's right. Yeah, I mean, it's like what you were saying earlier that like the [00:32:35] success of macro science will be whether or not it's as controversial as macroeconomics.

It's like, I actually hope that that's the case. It's like people being like, this is all wrong. You're approaching it like from a too high level, too abstract of a level. Yeah. I mean, I think the other benefit of doing this outside of like the level of insight is I think one of the projects that I think I have is like We need to, we need to be like defeating meta science, like a love of meta science aesthetics versus like actual like meta science, right?

Like then I think like a lot of people in meta science love science. That's why they're excited to not talk about the specific science, but like science in general. But like, I think that intuition also leads us to like have very romantic ideas of like what science is and how science should look and what kinds of science that we want.

Yeah. Right. The mission is progress. The mission isn't science. And so I think, like, we have to be a lot more functional. And again, I think, like, the benefit of these types of, like, rotations, like, Oh, you just are in a lab for a month. Yeah. It's like, I mean, you get a lot more of a sense of, like, Oh, okay, this is, this is what it [00:33:35] looks like.

Yeah. Yeah. I'd like to do the same thing for manufacturing. Huh. Right.

[00:33:39] Ben: Right. It's like, like, and I want, I want everybody to be rotating, right? Huh. Like, in the sense of, like, okay, like, have the scientists go and be, like, in a manufacturing lab. That's right.

[00:33:47] Tim: Yeah.

[00:33:48] Ben: And be like, okay, like, look. Like, you need to be thinking about getting this thing to work in, like, this giant, like, flow pipe instead of a

[00:33:54] Tim: test tube.

That's right, right. Yeah, yeah, yeah. Yeah,

[00:33:57] Ben: unfortunately, the problem is that we can't all spend our time, like, if everybody was rotating through all the

[00:34:03] Tim: things they need to rotate, we'd never get anything done. Yeah, exactly.

[00:34:06] Ben: ANd that's, that's, that's kind of

[00:34:08] Tim: the problem. Well, and to bring it all the way back, I mean, I think you started this question on macroscience in the context of transitioning away from all of this like weird Cuban Missile Crisis simulation stuff.

Like, I do think one way of thinking about this is like, okay, well, if we can't literally send you into a lab, right? Like the question is like, what are good simulations to give people good intuitions about the dynamics in the space? Yeah. And I think that's, that's potentially quite interesting. Yeah.

Normalized weekend long simulation. That's right. Like I love the idea of basically [00:34:35] like like you, you get to reenact the publication of a prominent scientific paper. It's like kind of a funny idea. It's just like, you know, yeah. Or, or, or even trying to

[00:34:44] Ben: get research funded, right? Like, it's like, okay, like you have this idea, you want yeah.

[00:34:55] Tim: I mean, yeah, this is actually a project, I mean, I've been talking to Zach Graves about this, it's like, I really want to do one which is a game that we're calling Think Tank Tycoon, which is basically like, it's a, it's a, the idea would be for it to be a strategy board game that simulates what it's like to run a research center.

But I think like to broaden that idea somewhat like it's kind of interesting to think about the idea of like model NSF Yeah, where you're like you you're in you're in the hot seat you get to decide how to do granting Yeah, you know give a grant

[00:35:22] Ben: a stupid thing. Yeah, some some some congressperson's gonna come banging

[00:35:26] Tim: on your door Yeah, like simulating those dynamics actually might be really really helpful Yeah I mean in the very least even if it's not like a one for one simulation of the real world just to get like some [00:35:35] common intuitions about like The pressures that are operating here.


[00:35:38] Ben: think you're, the bigger point is that simulations are maybe underrated

[00:35:42] Tim: as a teaching tool. I think so, yeah. Do you remember the the paperclip maximizer? Huh. The HTML game? Yeah, yeah.

[00:35:48] Ben: I'm, I'm kind of obsessed with it. Huh. Because, it, you've, like, somehow the human brain, like, really quickly, with just, like, you know, some numbers on the screen.

Huh. Like, just like numbers that you can change. Right, right. And some, like, back end. Dynamic system, where it's like, okay, like based on these numbers, like here are the dynamics of the

[00:36:07] Tim: system, and it'll give you an update.

[00:36:09] Ben: Like, you start to really get an intuition for, for system dynamics. Yeah. And so, I, I, I want to see more just like plain HTML, like basically like spreadsheet

[00:36:20] Tim: backend games.

Right, right, like the most lo fi possible. Yeah, I think so. Yeah. Yeah, I think it's helpful. I mean, I think, again, particularly in a world where you're thinking about, like, let's simulate these types of, like, weird new grant structures that we might try out, right? Like, you know, we've got a bunch [00:36:35] of hypotheses.

It's kind of really expensive and difficult to try to get experiments done, right? Like, does a simulation with a couple people who are well informed give us some, at least, inclinations of, like, where it might go or, like, what are the unintentional consequences thereof? Yeah.

[00:36:51] Ben: Disciplines besides the military that uses simulations

[00:36:56] Tim: successfully.

Not really. And I think what's kind of interesting is that like, I think it had a vogue that like has kind of dissipated. Yeah, I think like the notion of like a a game being the way you kind of do like understanding of a strategic situation, I think like. Has kind of disappeared, right? But like, I think a lot of it was driven, like, RAND actually had a huge influence, not just on the military.

But like, there's a bunch of corporate games, right? That were like, kind of invented in the same period. Yeah. That are like, you determine how much your steel production is, right? And was like, used to teach MBAs. But yeah, I think it's, it's been like, relatively limited. Hm. [00:37:35] Yeah. It, yeah. Hm.

[00:37:38] Ben: So. Other things.

Huh. Like, just to,

[00:37:41] Tim: to shift together. Sure, sure, go ahead. Yeah, yeah, yeah, yeah. I guess another

[00:37:44] Ben: thing that we haven't really talked about, but actually sort of plays into all of this, is thinking about better

[00:37:50] Tim: ways of regulating technology.

[00:37:52] Ben: I know that you've done a lot of thinking about that, and maybe this is another thing to simulate.

[00:38:00] Tim: Yeah, it's a model OSTP. But

[00:38:04] Ben: it's maybe a thing where, this is actually like a prime example where the particulars really matter, right? Where you can't just regulate. quote unquote technology. Yeah. Right. And it's like, there's, there's some technologies that you want to regulate very, very closely and very tightly and others that you want to regulate very

[00:38:21] Tim: loosely.

Yeah, I think that's right. And I think that's actually, you know, I think it is tied to the kind of like macro scientific project, if you will. Right. Which is that I think we have often a notion of like science regulation being like. [00:38:35] literally the government comes in and is like, here are the kind of constraints that we want to put on the system.

Right. And there's obviously like lots of different ways of doing that. And I think there's lots of contexts in which that's like appropriate. But I think for a lot of technologies that we confront right now, the change is so rapid that the obvious question always becomes, no matter what emerging technology talking about is like, how does your clock speed of regulation actually keep up with like the clock speed of technology?

And the answer is frequently like. It doesn't, right? And like you run into these kind of like absurd situations where you're like, well, we have this thing, it's already out of date by the time it goes into force, everybody kind of creates some like notional compliance with that rule. Yeah. And like, in terms of improving, I don't know, safety outcomes, for instance, it like has not actually improved safety outcomes.

And I think in that case, right, and I think I could actually make an argument that like, the problem is becoming more difficult with time. Right? Like, if you really believe that the pace of technological change is faster than it used to be, then it is possible that, like, there was a point at which, like, government was operating, and it could actually keep [00:39:35] pace effectively, or, like, a body like Congress could actually keep pace with society, or with technology successfully, to, like, make sure that it was conformant with, sort of, like, societal interests.

Do you think that was

[00:39:46] Ben: actually ever the case, or was it that we didn't, we just didn't

[00:39:50] Tim: have as many regulations? I would say it was sort of twofold, right? Like, I think one of them was you had, at least, let's just talk about Congress, right? It's really hard to talk about, like, government as a whole, right?

Like, I think, like, Congress was both better advised and was a more efficient institution, right? Which means it moved faster than it does today. Simultaneously, I also feel like for a couple reasons we can speculate on, right? Like, science, or in the very least, technology. Right, like move slower than it does today.

Right, right. And so like actually what has happened is that both both dynamics have caused problems, right? Which is that like the organs of government are moving slower at the same time as science is moving faster And like I think we've passed some inflection [00:40:35] point now where like it seems really hard to craft You know, let's take the AI case like a sensible framework that would apply You know, in, in LLMs where like, I don't know, like I was doing a little recap of like recent interoperability research and I like took a step back and I was like, Oh, all these papers are from May, 2023.

And I was like, these are all big results. This is all a big deal. Right. It's like very, very fast. Yeah. So that's kind of what I would say to that. Yeah. I don't know. Do you feel differently? You feel like Congress has never been able to keep up? Yeah.

[00:41:04] Ben: Well, I. I wonder, I guess I'm almost, I'm, I'm perhaps an outlier in that I am skeptical of the claim that technology overall has sped up significantly, or the pace of technological change, the pace of software change, certainly.

Sure. Right. And it's like maybe software as a, as a fraction of technology has spread up, sped up. And maybe like, this is, this is a thing where like to the point of, of regulations needing to, to. Go into particulars, [00:41:35] right? Mm-Hmm. . Right, right. Like tuning the regulation to the characteristic timescale of whatever talk

[00:41:40] Tim: technology we're talking about.

Mm-Hmm. , right?

[00:41:42] Ben: But I don't know, but like, I feel like outside of software, if anything, technology, the pace of technological change

[00:41:52] Tim: has slowed down. Mm hmm. Right. Right. Yeah.

[00:41:55] Ben: This is me putting on my

[00:41:57] Tim: stagnationist bias. And would, given the argument that I just made, would you say that that means that it should actually be easier than ever to regulate technology?

Yeah, I get targets moving slower, right? Like, yeah,

[00:42:12] Ben: yeah. Or it's the technology moving slowly because of the forms of

[00:42:14] Tim: the regulator. I guess, yeah, there's like compounding variables.

[00:42:16] Ben: Yeah, the easiest base case of regulating technology is saying, like, no, you can't have

[00:42:20] Tim: any.

Huh, right, right, right. Like, it can't change. Right, that's easy to regulate. Yeah, right, right. That's very easy to regulate. I buy that, I buy that. It's very easy to regulate well. Huh, right, right. I think that's

[00:42:27] Ben: That's the question. It's like, what do we want to lock in and what don't we

[00:42:31] Tim: want to lock in?

Yeah, I think that's right and I think, you [00:42:35] know I guess what that moves me towards is like, I think some people, you know, will conclude the argument I'm making by saying, and so regulations are obsolete, right? Or like, oh, so we shouldn't regulate or like, let the companies take care of it. And I'm like, I think so, like, I think that that's, that's not the conclusion that I go to, right?

Like part of it is like. Well, no, that just means we need, we need better ways of like regulating these systems, right? And I think they, they basically require government to kind of think about sort of like moving to different parts of the chain that they might've touched in the past. Yeah. So like, I don't know, we, Caleb and I over at IFP, we just submitted this RFI to DARPA.

In part they, they were thinking about like how does DARPA play a role in dealing with like ethical considerations around emerging technologies. Yep. But the deeper point that we were making in our submission. was simply that like maybe actually science has changed in a way where like DARPA can't be the or it's harder for DARPA to be the originator of all these technologies.

Yeah. So they're, they're almost, they're, they're placing the, the, the ecosystem, the [00:43:35] metabolism of technology has changed, which requires them to rethink like how they want to influence the system. Yeah. Right. And it may be more influence at the point of like. Things getting out to market, then it is things like, you know, basic research in the lab or something like that.

Right. At least for some classes of technology where like a lot of it's happening in private industry, like AI. Yeah, exactly. Yeah.

[00:43:55] Ben: No, I, I, I think the, the concept of, of like the metabolism of, of science and technology is like really powerful. I think in some sense it is, I'm not sure if you would, how would you map that to the idea of there being a

[00:44:11] Tim: research ecosystem, right?

Right. Is it, is it that there's like

[00:44:17] Ben: the metabolic, this is, this is incredibly abstract. Okay. Like, is it like, I guess if you're looking at the metabolism, does, does the metabolism sort of say, we're going to ignore institutions for now and the metabolism is literally just the flow

[00:44:34] Tim: of [00:44:35] like ideas and, and, and outcomes and then maybe like the ecosystem is

[00:44:41] Ben: like, okay, then we like.

Sort of add another layer and say there are institutions

[00:44:46] Tim: that are sure interacting with this sort of like, yeah, I think like the metabolism view or, you know, you might even think about it as like a supply chain view, right? To move it away from, like, just kind of gesturing at bio for no reason, right?

Is I think what's powerful about it is that, you know, particularly in foundation land, which I'm most familiar with. There's a notion of like we're going to field build and what that means is we're going to name a field and then researchers Are going to be under this tent that we call this field and then the field will exist Yeah, and then the proper critique of a lot of that stuff is like researchers are smart They just like go where the money is and they're like you want to call up like I can pretend to be nanotech for a Few years to get your money Like, that's no problem.

I can do that. And so there's kind of a notion that, like, if you take the economy of science as, like, institutions at the very beginning, you actually miss the bigger [00:45:35] picture. Yes. Right? And so the metabolism view is more powerful because you literally think about, like, the movement of, like, an idea to an experiment to a practical technology to, like, something that's out in the world.

Yeah. And then we basically say, how do we influence those incentives before we start talking about, like, oh, we announced some new policy that people just, like... Cosmetically align their agendas to yeah, and like if you really want to shape science It's actually maybe arguably less about like the institution and more about like Yeah, the individual.

Yeah, exactly. Like I run a lab. What are my motivations? Right? And I think this is like, again, it's like micro macro, right? It's basically if we can understand that, then are there things that we could do to influence at that micro level? Yeah, right. Which is I think actually where a lot of Macro econ has moved.

Right. Which is like, how do we influence like the individual firm's decisions Yeah. To get the overall aggregate change that we want in the economy. Yeah. And I think that's, that's potentially a better way of approaching it. Right. A thing that I desperately

[00:46:30] Ben: want now is Uhhuh a. I'm not sure what they're, they're [00:46:35] actually called.

Like the, you know, like the metal, like, like, like the

[00:46:37] Tim: prep cycle. Yeah, exactly. Like, like, like the giant diagram of, of like metabolism,

[00:46:43] Ben: right. I want that for, for research. Yeah, that would be incredible. Yeah. If, if only, I mean, one, I want to have it on

[00:46:50] Tim: my wall and to, to just get across the idea that.

[00:46:56] Ben: It is like, it's not you know, basic research, applied

[00:47:01] Tim: research.

Yeah, totally. Right, right, right. When it goes to like, and what I like about kind of metabolism as a way of thinking about it is that we can start thinking about like, okay, what's, what's the uptake for certain types of inputs, right? We're like, okay, you know like one, one example is like, okay, well, we want results in a field to become more searchable.

Well what's really, if you want to frame that in metabolism terms, is like, what, you know, what are the carbs that go into the system that, like, the enzymes or the yeast can take up, and it's like, access to the proper results, right, and like, I think that there's, there's a nice way of flipping in it [00:47:35] that, like, starts to think about these things as, like, inputs, versus things that we do, again, because, like, we like the aesthetics of it, like, we like the aesthetics of being able to find research results instantaneously, but, like, the focus should be on, Like, okay, well, because it helps to drive, like, the next big idea that we think will be beneficial to me later on.

Or like, even being

[00:47:53] Ben: the question, like, is the actual blocker to the thing that you want to see, the thing that you think it is? Right. I've run into far more people than I can count who say, like, you know, we want more awesome technology in the world, therefore we are going to be working on Insert tool here that actually isn't addressing, at least my,

[00:48:18] Tim: my view of why those things aren't happening.

Yeah, right, right. And I think, I mean, again, like, part of the idea is we think about these as, like, frameworks for thinking about different situations in science. Yeah. Like, I actually do believe that there are certain fields because of, like, ideologically how they're set up, institutionally how [00:48:35] they're set up, funding wise how they're set up.

that do resemble the block diagram you were talking about earlier, which is like, yeah, there actually is the, the basic research, like we can put, that's where the basic research happens. You could like point at a building, right? And you're like, that's where the, you know, commercialization happens. We pointed at another building, right?

But I just happen to think that most science doesn't look like that. Right. And we might ask the question then, like, do we want it to resemble more of like the metabolism state than the block diagram state? Right. Like both are good.

Yeah, I mean, I would

[00:49:07] Ben: argue that putting them in different buildings is exactly what's causing

[00:49:10] Tim: all the problems. Sure, right, exactly, yeah, yeah. Yeah. But then, again, like, then, then I think, again, this is why I think, like, the, the macro view is so powerful, at least to me, personally, is, like, we can ask the question, for what problems?

Yeah. Right? Like, are there, are there situations where, like, that, that, like, very blocky way of doing it serves certain needs and certain demands? Yeah. And it's like, it's possible, like, one more argument I can make for you is, like, Progress might be [00:49:35] slower, but it's a lot more controllable. So if you are in the, you know, if you think national security is one of the most important things, you're willing to make those trade offs.

But I think we just should be making those trade offs, like, much more consciously than we do. And

[00:49:49] Ben: that's where politics, in the term, in the sense of, A compromise between people who have different priorities on something can actually come in where we can say, okay, like we're going to trade off, we're going to say like, okay, we're going to increase like national security a little bit, like in, in like this area to, in compromise with being able to like unblock this.

[00:50:11] Tim: That's right. Yeah. And I think this is the benefit of like, you know, when I say lever, I literally mean lever, right. Which is basically like, we're in a period of time where we need this. Yeah. Right? We're willing to trade progress for security. Yeah. Okay, we're not in a period where we need this. Like, take the, take, ramp it down.

Right? Like, we want science to have less of this, this kind of structure. Yeah. That's something we need to, like, have fine tuned controls over. Right? Yeah. And to be thinking about in, like, a, a comparative sense, [00:50:35] so. And,

[00:50:36] Ben: to, to go

[00:50:36] Tim: back to the metabolism example. Yeah, yeah. I'm really thinking about it.

Yeah, yeah.

[00:50:39] Ben: Is there an equivalent of macro for metabolism in the sense that like I'm thinking about like, like, is it someone's like blood, like, you know, they're like blood glucose level,

[00:50:52] Tim: like obesity, right? Yeah, right. Kind of like our macro indicators for metabolism. Yeah, that's right. Right? Or like how you feel in the morning.

That's right. Yeah, exactly. I'm less well versed in kind of like bio and medical, but I'm sure there is, right? Like, I mean, there is the same kind of like. Well, I study the cell. Well, I study, you know, like organisms, right? Like at different scales, which we're studying this stuff. Yeah. What's kind of interesting in the medical cases, like You know, it's like, do we have a Hippocratic, like oath for like our treatment of the science person, right?

It's just like, first do no harm to the science person, you know?

[00:51:32] Ben: Yeah, I mean, I wonder about that with like, [00:51:35] with research. Mm hmm. Is there, should we have more heuristics about how we're

[00:51:42] Tim: Yeah, I mean, especially because I think, like, norms are so strong, right? Like, I do think that, like, one of the interesting things, this is one of the arguments I was making in the long science piece.

It's like, well, in addition to funding certain types of experiments, if you proliferate the number of opportunities for these low scale projects to operate over a long period of time, there's actually a bunch of like norms that might be really good that they might foster in the scientific community.

Right. Which is like you learn, like scientists learn the art of how to plan a project for 30 years. That's super important. Right. Regardless of the research results. That may be something that we want to put out into the open so there's more like your median scientist has more of those skills Yeah, right, like that's another reason that you might want to kind of like percolate this kind of behavior in the system Yeah, and so there's kind of like these emanating effects from like even one offs that I think are important to keep in mind

[00:52:33] Ben: That's actually another [00:52:35] I think used for simulations.

Yeah I'm just thinking like, well, it's very hard to get a tight feedback loop, right, about like whether you manage, you planned a project for 30 years

[00:52:47] Tim: well, right,

[00:52:48] Ben: right. But perhaps there's a better way of sort of simulating

[00:52:51] Tim: that planning process. Yeah. Well, and I would love to, I mean, again, to the question that you had earlier about like what are the metrics here, right?

Like I think for a lot of science metrics that we may end up on, they may have these interesting and really curious properties like we have for inflation rate. Right. We're like, the strange thing about inflation is that we, we kind of don't like, we have hypotheses for how it happens, but like, part of it is just like the psychology of the market.

Yeah. Right. Like you anticipate prices will be higher next quarter. Inflation happens if enough people believe that. And part of what the Fed is doing is like, they're obviously making money harder to get to, but they're also like play acting, right? They're like. You know, trust me guys, we will continue to put pressure on the economy until you feel differently about this.

And I think there's going to be some things in science that are worth [00:53:35] measuring that are like that, which is like researcher perceptions of the future state of the science economy are like things that we want to be able to influence in the space. And so one of the things that we do when we try to influence like the long termism or the short termism of science It's like, there's lots of kind of like material things we do, but ultimately the idea is like, what does that researcher in the lab think is going to happen, right?

Do they think that, you know, grant funding is going to become a lot less available in the next six months or a lot more available in the next six months? Like influencing those might have huge repercussions on what happens in science. And like, yeah, like that's a tool that policymakers should have access to.

Yeah. Yeah.

[00:54:11] Ben: And the parallels between the. The how beliefs affect the economy,

[00:54:18] Tim: and how beliefs

[00:54:19] Ben: affect science, I think may also be a

[00:54:21] Tim: little bit underrated. Yeah. In the sense that,

[00:54:24] Ben: I, I feel like some people think that It's a fairly deterministic system where it's like, ah, yes, this idea's time has come.

And like once, once all the things that are in place, like [00:54:35] once, once all, then, then it will happen. And like,

[00:54:38] Tim: that is, that's like how it works.

[00:54:40] Ben: Which I, I mean, I have, I wish there was more evidence to my point or to disagree with me. But like, I, I think that's, that's really not how it works. And I'm like very often.

a field or, or like an idea will, like a technology will happen because people think that it's time for that technology to happen. Right. Right. Yeah. Obviously, obviously that isn't always the case. Right. Yeah. Yeah. There's, there's, there's hype

[00:55:06] Tim: cycles. And I think you want, like, eventually, like. You know, if I have my druthers, right, like macro science should have like it's Chicago school, right?

Which is basically like the idea arrives exactly when it should arrive. Scientists will discover it on exactly their time. And like your only role as a regulator is to ensure the stability of scientific institutions. I think actually that that is a, that's not a position I agree with, but you can craft a totally, Reasonable, coherent, coherent governance framework that's based around that concept, right?

Yes. Yeah. I think [00:55:35] like

[00:55:35] Ben: you'll, yes. I, I, I think like that's actually the criteria for success of meta science as a field uhhuh, because like once there's schools , then, then, then it will have made it,

[00:55:46] Tim: because

[00:55:47] Ben: there aren't schools right now. Mm-Hmm. , like, I, I feel , I almost feel I, I, I now want there to be schools because.

I want a, a better thing to, to craft my disagreements with people on.

[00:55:56] Tim: Right.

[00:55:56] Ben: Right. And be like, Oh, like, you know, right now it's, it's like individual people. That's right. Yeah. So it's like, I

[00:56:02] Tim: want, I want some team. Yeah. I think, I don't know. I think so one of my favorite museums in the world is this museum called the Pitt Rivers Museum, which is in Oxford.

It's like, it's preserved like many things at Oxford from like when it was first founded in whatever century it was. And what's great about it is that you walk into it and you're like, what is this? Like it builds itself as a museum, but it's just like a closet of stuff that this guy collected and it's basically like this early I'm like, yeah, this is the early phase of every Science or every field.

Yeah, it's like you're we're still in the phase of like that's interesting. I guess I'll put it in the [00:56:35] box That's interesting. I guess I'll put it in my back and we're just collecting at the moment, right? Yeah, but I think like, you know, you can only you can only do that for so long, right? Ultimately, you have to have a point of view because if it's gonna be more than a purely observational field It's gonna be a thing that actually should inform science policymaking Yeah, it has to come with some normative judgments that we're not gonna always have empirical results for And part of it is, like, these really hard to deal with questions epistemologically of, like, does science discover the idea, like, immediately upon all the resources being available?

Or are there, like, lots of provisionalities to science that would require intervention? There's no way of proving that's a really hard thing to prove or disprove. It ends up being a matter of, like, what's the philosophy that will dominate how... Like science planners think about the issue.