The main benefit of complexity economics in my opinion is that it addresses some of the seriously flawed and over-simplified assumptions that go into classical macroenomic models, such as rational expectations, homogenous agents, and that the economy is at equilibrium. However it turns out that replacing these with more relaxed assumptions is very difficult in practice. Approaches such as agent-based models (ABMs) are tricky to get right, since they have so many degrees of freedom. However I do think that this is a promising avenue of research, but it maybe it still needs more time and effort to pay off. Although it’s possible that I’m falling into a “real communism has never been tried” trap.
I also think that ML approaches are very complementary to simulation based approaches like ABMs.
In particular the complexity economics approach is useful for dealing with the interactions between the economy and other complex systems, such as public health. There was some decent research done on economics and the covid pandemic, such as Doyne Farmer’s work: https://www.doynefarmer.com/covid19-research, who is a well known complexity scientist.
It’s hard to know how much of this “heterodox” economics would have happened anyway, even in the absence of people who call themselves complexity scientists. But I do think complexity economics played a key role in advocating for these new approaches.
Having said that: I’m not an economist, so I’m not that well placed to criticise the field of economics.
More broadly I found the discussion on self-referential and recursive predictions very interesting, but I don’t necessarily think of that as central to complexity science.
I’d also be interested in hearing more about how this fits in with AI Alignment, in particular complexity science approaches to AI Governance.
I really enjoyed this dialogue, thanks!
A few points on complexity economics:
The main benefit of complexity economics in my opinion is that it addresses some of the seriously flawed and over-simplified assumptions that go into classical macroenomic models, such as rational expectations, homogenous agents, and that the economy is at equilibrium. However it turns out that replacing these with more relaxed assumptions is very difficult in practice. Approaches such as agent-based models (ABMs) are tricky to get right, since they have so many degrees of freedom. However I do think that this is a promising avenue of research, but it maybe it still needs more time and effort to pay off. Although it’s possible that I’m falling into a “real communism has never been tried” trap.
I also think that ML approaches are very complementary to simulation based approaches like ABMs.
In particular the complexity economics approach is useful for dealing with the interactions between the economy and other complex systems, such as public health. There was some decent research done on economics and the covid pandemic, such as Doyne Farmer’s work: https://www.doynefarmer.com/covid19-research, who is a well known complexity scientist.
It’s hard to know how much of this “heterodox” economics would have happened anyway, even in the absence of people who call themselves complexity scientists. But I do think complexity economics played a key role in advocating for these new approaches.
Having said that: I’m not an economist, so I’m not that well placed to criticise the field of economics.
More broadly I found the discussion on self-referential and recursive predictions very interesting, but I don’t necessarily think of that as central to complexity science.
I’d also be interested in hearing more about how this fits in with AI Alignment, in particular complexity science approaches to AI Governance.