Doyne Farmer
speaker
145 appearances
1 recordings
1 series
first heard Oct 2024
last heard Oct 2024
Doyne Farmer’s voice in public audio — every appearance, attributed to the second.
Trend
recordings per month · last 12 monthsNo recordings in the last 12 months.Older appearances are listed below; set an alert to hear about the next one.
Appearances
Well... I think it's fundamentally endogenous. And it's a challenge to model in economics. In biology, you can just say, well, innovation is randomness. You randomly change a gene. Or, OK, you have to deal with recombination, which is more complicated. But it's just a process you can specify in an algorithm. Because we have this universal biological code that everything follows.
Whereas we don't have anything that clean in the economy, Innovation depends on human reasoning, but sometimes just assuming it happens randomly is not a bad start. There's something called Wright's Law or learning by doing, comes under several different names. That's a very useful way to predict technological improvement.
The theory for that actually, ironically, was originally started by John Muth John Muth is a guy who actually invented rational expectations in 1960. But he also, he could work with both hands. He said, let's assume that inventors just throw darts at a dartboard at random. And let's assume they're just smart enough to see when they've made a better throw.
And so he then showed that you got Wright's law, although only with an exponent of one, right? That's the only exponent he could drive. Fast forward, there was another simulation paper. And then we wrote a paper where, again, we enhanced that idea by looking at the fact that technologies are connected within a device.
If you change the carburation system, you may need to change the ignition system. So you look at what's called a design structure matrix that automobile designers and other designers of complex things use to understand interactions.
So we enhanced the theory to deal with that, derived a bunch of stuff physics style, and we were able to show that, derived the Wright's law exponent, and show that the more complicated and less modular the system is, the lower that exponent is and the slower it improves. And that's been tested. It seems to be more or less true.
It's another example of throwing darts at a dartboard actually is good enough to get you there, as was ironically realized by John Muth, the founder of rational expectations. But somehow economics got locked into a framework where you couldn't do what Muth did anymore. That's just bad cricket. to assume that people just flip coins. Not okay.
Yes. I think the same is true in physics and many other disciplines. Because those guys had to wrestle with coming up with the concepts in the first place. So they understood the slippery ground they were standing on better than subsequent generations.
I think actually the simulations will help us derive better theories. The theories that get derived, though, are different than that standard template I gave you. Sure. Although sometimes it could be that, if it goes to equilibrium, could work, right?
But the theories that we complexity economists use are often more like statistical mechanics or evolutionary biology models, which may or may not have equilibrium in them. And so it's a much more flexible theoretical framework. But I'd like to draw an analogy to fluid flow. The Navier-Stokes equations, you can derive them from Newton's laws. And you can write them down.
take one line that looks pretty simple, a few little upside down triangles that confuse the math to calculus. But once you understand what the math means, simple to write down. Solving them, they're not solvable in general. And we now know why. It's because they have chaotic solutions.
When you have chaotic solutions, there are typically no shortcuts to just grinding things out numerically one step at a time. They're intrinsically complex in that regard. But now back to theory, well, One of the big changes in fluid dynamics is we now have numerical fluid computation. We can make use of computer power to simulate what fluids do pretty accurately.
But that's also been a big driver of theory because now you can test your theory without having to set up a wind tunnel and get a big grant from the NSF. And so there's a rich interaction between the simulators and the equation guys. So I actually think being able to simulate is ultimately going to give us a deeper theoretical understanding
And by the way, let me say, when we see a phenomenon in an agent-based model, the first thing we do is try and strip it down. We go, let's give it the pulse of what's causing this. So we start throwing stuff away or using really simple dummy versions for a component. If it keeps on doing it, we go, OK, that's not the cause.
So we try and figure out the causality by doing what biologists would call knockout experiments. Also, we often get the phenomenon by doing addition experiments, meaning we start simple, we add a feature, we look at what happens, we add another feature, we look at what happens. So you can go from either direction to try and pin down the causality.
And then once you do that, the theoretician can step in and try and make a stripped down mathematical model, and in some cases, explain what's happening.
By and large, no. A few exceptional individuals do. My book has an endorsement by Larry Summers. There you go. Who really surprised me because, you know, I sent the book to the early manuscript to him saying, Larry, I use your name several times in the book. Just search for your name. Look and see if what I said is OK. And let me know. I don't you know, I want to be nice to everybody.
So to my astonishment, he sent it back saying, I read your book, and I really agree. I think you have a really good point. You made some errors. He corrected a bunch of my errors. But he said, I overall agree. So wow, I was blown away. My old friend John Giannacopoulos, who was actually Larry Summers' roommate when they were graduate students at Harvard,
He's also, we've been arguing about this stuff since the late 80s. So yeah, he appreciates it. I've co-authored papers with him. My colleague Andrew Lowe at MIT, there's a few people like that. But by and large, what we're doing is ignored by the mainstream. We can't publish in their journals. They'll just say, you're not making the kind of theory we consider acceptable.
It's like a loop quantum gravity person trying to publish in a string theory journal. For those of you who happen to know that controversy. There's not a lot of traction with the mainstream. Now, things are changing. I sent some cracks opening up. We're getting interest from central bankers.
Showing 101–120 of 145 · page 6 of 8
← Previous
Next →