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.
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Thank you.
Yeah, well, I sometimes worry it's not the best term. The term is there to indicate that it's coming from the science of complex systems and that we're using methods and maybe even a scientific philosophy of epistemology that's coming out of complex systems, which, although economists like to point to Adam Smith and so on, it's very different than the way they do things
And I think it's much more explicitly complex systems than the way they do things.
Well, yes and no. That is, you know, first of all, I'm bending over backwards to be nice to the economists. I hope they appreciate that. And so I intentionally took as much criticism of economics as I possibly could out of the book. I had several editors help me do that. Now, I think there are some things that conventional economics is pretty good at.
in a simple situation where you need to understand strategic interaction, and that plays an important role, or strategic thinking plays a role, but in a simple context where you can understand, then I think it can work pretty well. I think where it fails is when things get more complicated, when you need to put in more institutional structure, or when
individual agents can't reason well about what's going on. And so you really have to fall back to heuristics and more simple reasoning. And maybe to amplify a little bit on that first point, because I think it's a central one. And in my book, I quote economists laying the problem out. And the problem is,
And maybe we need to digress to how mainstream economics works and what the difference between the two approaches are. And then I think this will become more apparent. But in mainstream economics, in the capsule version, is that you begin by assigning all the agents, all the decision makers, utility functions, scorecards that say what they like better and what they don't like as much.
And then you give them some way of reasoning about the world. Traditionally, that's rational expectations, meaning they're like Mr. Spock in Star Trek. They can reason about everything, and they're very logical, and they can process all the information and arrive at the correct conclusions. And so you give them those things, and you furthermore assume equilibrium.
which in a standard economic model means supply equals demand. But sometimes you're in a strategic setting where it means you're doing something like game theory, where it's a strategic equilibrium, meaning we've all arrived at strategies that make decisions that are as good as we can do, given that everybody else is not changing what they're doing. And everybody does that.
So that'd be the standard thing, rational expectations. Now, And then just to finish, you write all that down in equations. You solve what economists call the first order conditions, meaning you set the derivative to zero. And you compute the decisions that maximize utility for all the agents. And then you calculate the economic consequences of those decisions.
Now, so in complexity economics, we do things completely differently. So it's really throwing out stuff that's been in economics since the 19th century. And we say, well, let's assume we have some agents. Let's give them some ways of making decisions that could be very simple or more complicated, but we're not assuming optimality. So information flows in.
The agents use their rules to make decisions, which might be learning algorithms or they might just be simple heuristics like buy undervalued assets or imitate the best. Look around and see who's doing the best. Imitate them. Or it might be trial and error. Try something. If it works, keep doing it. Doesn't work, try something else. Simple stuff. And so they make their decisions.
We then calculate the economic consequences of the decisions. That generates new information. In addition, new information may flow in from the outside. And then we repeat the process. And we just go around and around that loop. We may arrive at an equilibrium where supply equals demand or agents decisions get locked in.
If so, that's what we would regard as complex system scientists as an emergent property or we might not. And actually oftentimes we don't. And I think that's one of the important strengths of this formalism that we more naturally capture dynamics And endogenous dynamics, that is dynamics that arises from within.
Things like business cycles, where if you take, say, the financial crisis of 2008, the so-called great financial crisis, I think it's pretty clear it's an endogenous crisis. It wasn't like a meteor hit the earth and that caused the crisis or that people suddenly changed. It was that we introduced new types of technologies
Financial instruments, mortgage-backed securities, housing market got overpriced, it crashed. All these things happened from within the economy. In a mainstream model, you can't get that to happen. And so you just don't get, it's very, you can get endogenous dynamics, but you have to push the economy into extreme, you have to make what seem like unreasonable assumptions in order to get there.
There's actually something called a turnpike theorem that says things are just gonna settle into a fixed point unless certain conditions are satisfied, like very myopic reasoning, et cetera. But if under normal reasonable conditions, You know, it's like a turnpike. You look down the road. You see where things are going. You make corrections as needed because you can see everything well ahead.
And so you're not steering wildly. Now, in the book, I show several examples where behavioral errors, bounded rationality, making mistakes we should expect people should make, lead to endogenous dynamics. And, you know, the analogy I make is that the economy is more like a drunk driver on a mountain road, you know, swerving and not quite always doing what he or she is supposed to do.
So maybe one more thing. Sure. So it gets me back to what caused the whole digression. The part that economists, I think, will all agree on is when you're computing optimal strategies for each agent, you're deducing those strategies, you can't make things very complicated. Once the system gets nonlinear, once you have more than a dozen agents, you can't solve the equations anymore.
And so you have to keep the models simple. You're just forced to do that. You're also writing down equations. You have to write down everything in equations. In an agent-based model, those constraints don't exist. We have models, we've run simulations with millions of agents making decisions. And there's just a lot more room to put in institutional structure, heterogeneity, real world stuff.
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