To me, it seems this dialogue diverged a lot into a question of what is self-referential, how important that is, etc. I don’t think that’s The core idea of complex systems, and does not seem a crux for anything in particular.
So, what are core ideas of complex systems? In my view:
1. Understanding that there is this other direction (complexity) physics can expand to; traditionally, physics has expanded in scales of space, time, and energy—starting from everyday scales of meters, seconds, and kgs, gradually understanding the world on more and more distant scales.
While this was super successful, with a careful look, you notice that while we had claims like ‘we now understand deeply how the basic building blocks of matter behave’, this comes with a * disclaimer/footnote like ‘does not mean we can predict anything if there are more of the blocks and they interact in nontrivial ways’.
This points to some other direction in the space of stuff to apply physics way of thinking than ‘smaller’, ‘larger’, ‘high energy’, etc., and also different than ‘applied’.
Accordingly, good complex systems science is often basically the physics way of thinking applied to complex systems. Parts of statistical mechanics fit neatly into this, but because being developed first, have somewhat specific brand.
Why it isn’t done just under the brand of ‘physics’ seems based on, in my view, often problematic way of classifying fields by subject of study, and not by methods. I know some personal experiences of people who tried to do, e.g., physics of some phenomena in economic systems, having a hard time to survive in traditionally physics academic environments (“does it really belong here if instead of electrons you are now applying it to some …markets?”)
(This is not really strict; for example, decent complex systems research is often published in venues like Physica A, which is nominally about Statistical Mechanics and its Applications)
2. ‘Physics’ in this direction often stumbled upon pieces of math that are broadly applicable in many different contexts. (This is actually pretty similar to the rest of physics, where, for example, once you have the math of derivatives, or math of groups, you see them everywhere.) The historically most useful pieces are e.g., math of networks, statistical mechanics, renormalization, parts of entropy/information theory, phase transitions,...
Because of the above-mentioned (1.), it’s really not possible to show ‘how is this a distinct contribution of complex systems science, in contrast to just doing physics of nontraditional systems’. Actually, if you look at the ‘poster children’ of some of the ‘complex systems science’… my maximum likelihood estimate about their background is physics. (Just googled authors of the mentioned book: Stefan Thurner… obtained a PhD in theoretical physics, worked on e.g., topological excitations in quantum field theories, statistics and entropy of complex systems. Petr Klimek… was awarded a PhD in physics. Albert-László Barabási… has a PhD in physics. Doyne Farmer… University of California, Santa Cruz, where he studied physical cosmology etc. etc.). Empirically they prefer the brand of complex systems vs. just physics.
3. Part of what distinguishes complex systems [science / physics / whatever … ] is in aesthetics. (Also here it becomes directly relevant to alignment).
A lot of traditional physics and maths basically has a distaste toward working on problems which are complex, too much in the direction of practical relevance, too much driven by what actually matters.
Mentioned Albert-László Barabási got famous for investigating properties of real-world networks, like the internet or transport networks. Many physicists would just not work on this because it’s clearly ‘computer science’ or something, as the subject are computers or something like that. Discrete maths people studying graphs could have discovered the same ideas a decade earlier … but my inner sim of them says studying the internet is distasteful. It’s just one graph, not some neatly defined class of abstract objects. It’s data-driven. There likely aren’t any neat theorems. Etc.
Complex systems has an opposite aesthetics: applying math to real-world matters. Important real-world systems are worth studying also because of real-world importance, not just math beauty.
In my view AI safety would be a on a better track if this taste/aesthetics was more common. What we have now often either lacks what’s good about physics (aim for somewhat deep theories which generalize) or lacks what’s good about complexity science branch of physics (reality orientation, assumption that you often find cool math when looking at reality carefully vs. when just looking for cool maths)
To me, it seems this dialogue diverged a lot into a question of what is self-referential, how important that is, etc. I don’t think that’s The core idea of complex systems, and does not seem a crux for anything in particular.
So, this matches my impression before this dialogue, but going back to this dialogue, the podcast that Nora linked does to me seem to indicate that self-referentiality and adaptiveness are the key thing that defines the field. To give a quote from the podcast that someone else posted:
We have to theorize about theorizers and that makes all the difference. And so notions of agency or reflexivity, these kinds of words we use to denote self-awareness or what does a mathematical theory look like when that’s an unavoidable component of the theory. Feynman and Murray both made that point. Imagine how hard physics would be if particles could think. That is essentially the essence of complexity. And whether it’s individual minds or collectives or societies, it doesn’t really matter. And we’ll get into why it doesn’t matter, but for me at least, that’s what complexity is. The study of teleonomic matter.
Which sure doesn’t sound to me like “applying physics to non-physics topics”. It seems to put the self-referentially pretty centrally into the field.
(high-level comment)
To me, it seems this dialogue diverged a lot into a question of what is self-referential, how important that is, etc. I don’t think that’s The core idea of complex systems, and does not seem a crux for anything in particular.
So, what are core ideas of complex systems? In my view:
1. Understanding that there is this other direction (complexity) physics can expand to; traditionally, physics has expanded in scales of space, time, and energy—starting from everyday scales of meters, seconds, and kgs, gradually understanding the world on more and more distant scales.
While this was super successful, with a careful look, you notice that while we had claims like ‘we now understand deeply how the basic building blocks of matter behave’, this comes with a * disclaimer/footnote like ‘does not mean we can predict anything if there are more of the blocks and they interact in nontrivial ways’.
This points to some other direction in the space of stuff to apply physics way of thinking than ‘smaller’, ‘larger’, ‘high energy’, etc., and also different than ‘applied’.
Accordingly, good complex systems science is often basically the physics way of thinking applied to complex systems. Parts of statistical mechanics fit neatly into this, but because being developed first, have somewhat specific brand.
Why it isn’t done just under the brand of ‘physics’ seems based on, in my view, often problematic way of classifying fields by subject of study, and not by methods. I know some personal experiences of people who tried to do, e.g., physics of some phenomena in economic systems, having a hard time to survive in traditionally physics academic environments (“does it really belong here if instead of electrons you are now applying it to some …markets?”)
(This is not really strict; for example, decent complex systems research is often published in venues like Physica A, which is nominally about Statistical Mechanics and its Applications)
2. ‘Physics’ in this direction often stumbled upon pieces of math that are broadly applicable in many different contexts. (This is actually pretty similar to the rest of physics, where, for example, once you have the math of derivatives, or math of groups, you see them everywhere.) The historically most useful pieces are e.g., math of networks, statistical mechanics, renormalization, parts of entropy/information theory, phase transitions,...
Because of the above-mentioned (1.), it’s really not possible to show ‘how is this a distinct contribution of complex systems science, in contrast to just doing physics of nontraditional systems’. Actually, if you look at the ‘poster children’ of some of the ‘complex systems science’… my maximum likelihood estimate about their background is physics. (Just googled authors of the mentioned book: Stefan Thurner… obtained a PhD in theoretical physics, worked on e.g., topological excitations in quantum field theories, statistics and entropy of complex systems. Petr Klimek… was awarded a PhD in physics. Albert-László Barabási… has a PhD in physics. Doyne Farmer… University of California, Santa Cruz, where he studied physical cosmology etc. etc.). Empirically they prefer the brand of complex systems vs. just physics.
3. Part of what distinguishes complex systems [science / physics / whatever … ] is in aesthetics. (Also here it becomes directly relevant to alignment).
A lot of traditional physics and maths basically has a distaste toward working on problems which are complex, too much in the direction of practical relevance, too much driven by what actually matters.
Mentioned Albert-László Barabási got famous for investigating properties of real-world networks, like the internet or transport networks. Many physicists would just not work on this because it’s clearly ‘computer science’ or something, as the subject are computers or something like that. Discrete maths people studying graphs could have discovered the same ideas a decade earlier … but my inner sim of them says studying the internet is distasteful. It’s just one graph, not some neatly defined class of abstract objects. It’s data-driven. There likely aren’t any neat theorems. Etc.
Complex systems has an opposite aesthetics: applying math to real-world matters. Important real-world systems are worth studying also because of real-world importance, not just math beauty.
In my view AI safety would be a on a better track if this taste/aesthetics was more common. What we have now often either lacks what’s good about physics (aim for somewhat deep theories which generalize) or lacks what’s good about complexity science branch of physics (reality orientation, assumption that you often find cool math when looking at reality carefully vs. when just looking for cool maths)
So, this matches my impression before this dialogue, but going back to this dialogue, the podcast that Nora linked does to me seem to indicate that self-referentiality and adaptiveness are the key thing that defines the field. To give a quote from the podcast that someone else posted:
Which sure doesn’t sound to me like “applying physics to non-physics topics”. It seems to put the self-referentially pretty centrally into the field.