🌐 My chat (+transcript) with chaos theorist Doyne Farmer on our interconnected economy

Farmer is the Baillie Gifford Professor of Complex Systems at Oxford's Institute for New Economic Thinking. Before joining Oxford in 2012, he worked at Los Alamos National Laboratory and the Santa Fe Institute, where he studied complex systems and economic dynamics. During the 1990s, he took a break from academia to run a successful quantitative trading firm using statistical arbitrage strategies.

Farmer has been a pioneer in chaos theory and complexity economics, including the development of agent-based models to understand economic phenomena. His work spans from housing markets to climate change, and he recently authored Making Sense of Chaos exploring complexity science and economic modeling.

In This Episode

* What is complexity economics? (1:23)

* Compliment or replacement for traditional economics (6:55)

* Modeling Covid-19 (11:12)

* The state of the science (15:06)

* How to approach economic growth (20:44)

Below is a lightly edited transcript of our conversation.

What is complexity economics? (1:23)

We really can model the economy as something dynamic that can have its own business cycles that come from within the economy, rather than having the economy just settle down to doing something static unless it's hit by shocks all the time, as is the case in mainstream models.

Pethokoukis: What does the sort of economics that people would learn, let's say, in the first year of college, they might learn about labor and capital, supply-demand equilibrium, rational expectations, maybe the importance of ideas. How does that differ from the kind of economics you are talking about? Are you looking at different factors?

Farmer: We’re really looking at a completely different way of doing economics. Rather than maximizing utility, which is really the central conceptual piece of any standard economic model, and writing down equations, and deducing the decision that does that, we simulate the economy.

We assume that we identify who the agents in the and economy are, who's making the decisions, what information do they have available, we give them methods of making the decisions — decision-making rules or learning algorithms — and then they make decisions, those decisions have economic impact, that generates new information, other information may enter from the outside, they make decisions, and we just go around and around that loop in a computer simulation that tries to simulate what the economy does and how it works.

You've been writing about this for some time. I would guess — perhaps I'm wrong — that just having more data and more computer power has been super helpful over the past 10 years, 20 years.

It's been super helpful for us. We take much more advantage of that than the mainstream does. But yes, computers are a billion times more powerful now than they were when Herb Simon first suggested this way of doing things, and that means the time is ripe now because that's not a limiting factor anymore, as it was in the past.

So if you're not looking at capital and labor per se, then what are the factors you're looking at?

Well, we do look at capital and labor, we just look at them in a different way. Our models are concerned about how much capital is there to invest, what labor is available. We do have to assign firms production functions that tells, given an amount of capital and labor and all their other inputs, how much can the firms produce? That part of the idea is similar. It's a question of the way the decision about how much to produce is made, or the way consumers decide how much to consume, or laborers decide at what price to provide their labor. All those parts are different.

Another difference — if I'm understanding it correctly — is, rather than thinking about economies that tend toward equilibrium and focusing how outside shocks may put an economy in disequilibrium, you're looking a lot more at what happens internally. Am I correct?

We don't assume equilibrium. Equilibrium, it has two senses in economics: One is supply equals demand. We might or might not run a model where we assume that. In many models we don't, and if that happens, that's great, but it's an outcome of the model rather than an assumption we put in at the beginning.

There's another sense of equilibrium, which is that everybody's strategy is lined up. You've had time to think about what you're doing, I've had time to think about what I'm doing, we’ve both come to the optimal decision for each of us to make, taking the other one into account. We don't assume that, as standard models typically do. We really can model the economy as something dynamic that can have its own business cycles that come from within the economy, rather than having the economy just settle down to doing something static unless it's hit by shocks all the time, as is the case in mainstream models. We still allow shocks to hit our models, but the economy can generate dynamics even without those shocks.

This just popped in my head: To whom would this model make more intuitive sense, Karl Marx or<a ta