My best argument as to why coarse-graining and “going up a layer” when describing complex systems are necessary:
Often we hear a reductionist case against ideas like emergence which goes something like this: “If we could simply track all the particles in e.g. a human body, we’d be able to predict what they did perfectly with no need for larger-scale simplified models of organs, cells, minds, personalities etc.”. However, this kind of total knowledge is actually impossible given the bounds of the computational power available to us.
First of all, when we attempt to track billions of particle interactions we very quickly end up with a chaotic system, such that tiny errors in measurements and setting up initial states quickly compound into massive prediction errors (A metaphor I like is that you’re “using up” the decimal points in your measurement: in a three body system the first timestep depends mostly on the value of the non-decimal portions of the starting velocity measurements. A few timesteps down changing .15 to .16 makes a big difference, and by the 10000th timestep the difference between a starting velocity of .15983849549 and .15983849548 is noticeable). This is the classic problem with weather prediction.
Second of all, tracking “every particle” means that the scope of the particles you need to track explodes out of the system you’re trying to monitor into the interactions the system has with neighbouring particles, and then the neighbours of neighbours, so on and so forth. In the human case, you need to track every particle in the body, but also every particle the body touches or ingests (could be a virus), and then the particles that those particles touch… This continues until you reach the point where “to understand the baking process of an apple pie you must first track the position of every particle in the universe”
The emergence/systems solution to both problems is to essentially go up a level. Instead of tracking particles, you should track cells, organs, individual humans, systems etc. At each level (following Erik Hoel’s Causal Emergence framework) you trade microscale precision for predictive power i.e. the size of the system you can predict for a given amount of computational power. Often this means collapsing large amounts of microscale interactions into random noise—a slot machine could in theory be deterministically predicted by tracking every element in the randomiser mechanism/chip, but in practice it’s easier to model as a machine with an output distribution set by the operating company. Similarly, we trade Feynman diagrams for brownian motion and Langevin dynamics.
Addendum for the future: Concepts like agents, agency, and choice only make sense at the systemic macroscale. If you had total atomic knowledge (complete knowledge of every single particle interaction in a human—which, as we discussed, basically requires complete knowledge of every single particle interaction in the universe), the determinists are right. There is no choice. It’s only neurons firing and chemicals bonding. But we operate at a higher level, with noise and uncertainty. Then preferences and policies make sense as things to talk about.
I’m curious: do we often see this reductionist case you claim? I think what you’ve written is pretty uncontroversial: the claim is true, but irrelevant because its precondition can’t be satisfied. But if we “often” see the case against, then presumably it is in fact controversial!
My best argument as to why coarse-graining and “going up a layer” when describing complex systems are necessary:
Often we hear a reductionist case against ideas like emergence which goes something like this: “If we could simply track all the particles in e.g. a human body, we’d be able to predict what they did perfectly with no need for larger-scale simplified models of organs, cells, minds, personalities etc.”. However, this kind of total knowledge is actually impossible given the bounds of the computational power available to us.
First of all, when we attempt to track billions of particle interactions we very quickly end up with a chaotic system, such that tiny errors in measurements and setting up initial states quickly compound into massive prediction errors (A metaphor I like is that you’re “using up” the decimal points in your measurement: in a three body system the first timestep depends mostly on the value of the non-decimal portions of the starting velocity measurements. A few timesteps down changing .15 to .16 makes a big difference, and by the 10000th timestep the difference between a starting velocity of .15983849549 and .15983849548 is noticeable). This is the classic problem with weather prediction.
Second of all, tracking “every particle” means that the scope of the particles you need to track explodes out of the system you’re trying to monitor into the interactions the system has with neighbouring particles, and then the neighbours of neighbours, so on and so forth. In the human case, you need to track every particle in the body, but also every particle the body touches or ingests (could be a virus), and then the particles that those particles touch… This continues until you reach the point where “to understand the baking process of an apple pie you must first track the position of every particle in the universe”
The emergence/systems solution to both problems is to essentially go up a level. Instead of tracking particles, you should track cells, organs, individual humans, systems etc. At each level (following Erik Hoel’s Causal Emergence framework) you trade microscale precision for predictive power i.e. the size of the system you can predict for a given amount of computational power. Often this means collapsing large amounts of microscale interactions into random noise—a slot machine could in theory be deterministically predicted by tracking every element in the randomiser mechanism/chip, but in practice it’s easier to model as a machine with an output distribution set by the operating company. Similarly, we trade Feynman diagrams for brownian motion and Langevin dynamics.
Addendum for the future: Concepts like agents, agency, and choice only make sense at the systemic macroscale. If you had total atomic knowledge (complete knowledge of every single particle interaction in a human—which, as we discussed, basically requires complete knowledge of every single particle interaction in the universe), the determinists are right. There is no choice. It’s only neurons firing and chemicals bonding. But we operate at a higher level, with noise and uncertainty. Then preferences and policies make sense as things to talk about.
I’m curious: do we often see this reductionist case you claim? I think what you’ve written is pretty uncontroversial: the claim is true, but irrelevant because its precondition can’t be satisfied. But if we “often” see the case against, then presumably it is in fact controversial!
I’ve heard it quite a few times when discussing emergence and complexity topics.