The Nonlinear Library LW - On Complexity Science by Garrett Baker
Apr 5, 2024
06:53
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: On Complexity Science, published by Garrett Baker on April 5, 2024 on LessWrong.
I have a long and confused love-hate relationship with the field of complex systems. People there never want to give me a simple, straightforward explanation about what its about, and much of what they say sounds a lot like woo ("edge of chaos" anyone?). But it also seems to promise a lot! This from the primary textbook on the subject:
The present situation can be compared to an archaeological project, where a mosaic floor has been discovered and is being excavated.
While the mosaic is only partly visible and the full picture is still missing, several facts are becoming clear: the mosaic exists; it shows identifiable elements (for instance, people and animals engaged in recognizable activities); there are large patches missing or still invisible, but experts can already tell that the mosaic represents a scene from, say, Homer's Odyssey.
Similarly, for dynamical complex adaptive systems, it is clear that a theory exists that, eventually, can be fully developed.
Of course, that textbook never actually described what the mosaic it thought it saw actually was. The closest it came to was:
More formally, co-evolving multiplex networks can be written as, ddtσi(t)F(Mαij,σj(t)) ddtMαijG(Mβij(t),σj(t)).(1.1) [...] The second equation specifies how the interactions evolve over time as a function G that depends on the same inputs, states of elements and interaction networks. G can be deterministic or stochastic. Now interactions evolve in time. In physics this is very rarely the case. The combination of both equations makes the system a co-evolving complex system.
Co-evolving systems of this type are, in general, no longer analytically solvable.
Which... well... isn't very exciting, and as far as I can tell just describes any dynamical system (co-evolving or no).
The textbook also seems pretty obsessed with a few seemingly random fields:
Economics
Sociology
Biology
Evolution
Neuroscience
AI
Probability theory
Ecology
Physics
Chemistry
"What?" I had asked, and I started thinking
Ok, I can see why some of these would have stuff in common with others.
Physics brings in a bunch of math you can use.
Economics and sociology both tackle similar questions with very different techniques. It would be interesting to look at what they can tell each other (though it seems strange to spin off a brand new field out of this).
Biology, evolution, and ecology? Sure. Both biology and ecology are constrained by evolutionary pressures, so maybe we can derive new things about each by factoring through evolution.
AI, probability theory, and neuroscience? AI and neuroscience definitely seem related. The history of AI and probability theory has been mixed, and I don't know enough about the history of neuroscience and probability theory to have a judgement there.
And chemistry??? Its mostly brought into the picture to talk about stoichiometry, the study of the rate and equilibria of chemical reactions. Still, what?
And how exactly is all this meant to fit together again?
And each time I heard a complex systems theorist talk about why their field was important they would say stuff like
Complexity spokesperson: Well, current classical economics mostly assumes you are in an economic equilibrium, this is because it makes the math easier, but in fact we're not! And similarly with a bunch of other fields! We make a bunch of simplifying assumptions, but they're all usually a simplification of the truth! Thus, complex systems science.
Me: Oh... so you don't make any simplifying assumptions? That seems... intractable?
Complexity spokesperson: Oh no our models still make plenty of simplifications, we just run a bunch of numerical simulations of toy scenarios, then make wide and sweeping claims about the results.
Me: That seems... wors...
I have a long and confused love-hate relationship with the field of complex systems. People there never want to give me a simple, straightforward explanation about what its about, and much of what they say sounds a lot like woo ("edge of chaos" anyone?). But it also seems to promise a lot! This from the primary textbook on the subject:
The present situation can be compared to an archaeological project, where a mosaic floor has been discovered and is being excavated.
While the mosaic is only partly visible and the full picture is still missing, several facts are becoming clear: the mosaic exists; it shows identifiable elements (for instance, people and animals engaged in recognizable activities); there are large patches missing or still invisible, but experts can already tell that the mosaic represents a scene from, say, Homer's Odyssey.
Similarly, for dynamical complex adaptive systems, it is clear that a theory exists that, eventually, can be fully developed.
Of course, that textbook never actually described what the mosaic it thought it saw actually was. The closest it came to was:
More formally, co-evolving multiplex networks can be written as, ddtσi(t)F(Mαij,σj(t)) ddtMαijG(Mβij(t),σj(t)).(1.1) [...] The second equation specifies how the interactions evolve over time as a function G that depends on the same inputs, states of elements and interaction networks. G can be deterministic or stochastic. Now interactions evolve in time. In physics this is very rarely the case. The combination of both equations makes the system a co-evolving complex system.
Co-evolving systems of this type are, in general, no longer analytically solvable.
Which... well... isn't very exciting, and as far as I can tell just describes any dynamical system (co-evolving or no).
The textbook also seems pretty obsessed with a few seemingly random fields:
Economics
Sociology
Biology
Evolution
Neuroscience
AI
Probability theory
Ecology
Physics
Chemistry
"What?" I had asked, and I started thinking
Ok, I can see why some of these would have stuff in common with others.
Physics brings in a bunch of math you can use.
Economics and sociology both tackle similar questions with very different techniques. It would be interesting to look at what they can tell each other (though it seems strange to spin off a brand new field out of this).
Biology, evolution, and ecology? Sure. Both biology and ecology are constrained by evolutionary pressures, so maybe we can derive new things about each by factoring through evolution.
AI, probability theory, and neuroscience? AI and neuroscience definitely seem related. The history of AI and probability theory has been mixed, and I don't know enough about the history of neuroscience and probability theory to have a judgement there.
And chemistry??? Its mostly brought into the picture to talk about stoichiometry, the study of the rate and equilibria of chemical reactions. Still, what?
And how exactly is all this meant to fit together again?
And each time I heard a complex systems theorist talk about why their field was important they would say stuff like
Complexity spokesperson: Well, current classical economics mostly assumes you are in an economic equilibrium, this is because it makes the math easier, but in fact we're not! And similarly with a bunch of other fields! We make a bunch of simplifying assumptions, but they're all usually a simplification of the truth! Thus, complex systems science.
Me: Oh... so you don't make any simplifying assumptions? That seems... intractable?
Complexity spokesperson: Oh no our models still make plenty of simplifications, we just run a bunch of numerical simulations of toy scenarios, then make wide and sweeping claims about the results.
Me: That seems... wors...