Daniel Buckmaster08/25/2023, 12:57 AM
Reductionism and holism are both mistakes. In reality, explanations do not form a hierarchy with the lowest level being the most fundamental. Rather, explanations at any level of emergence can be fundamental. Abstract entities are real, and can play a role in causing physical phenomena. Causation is itself such an abstraction.
When we use theories about emergent physical quantities to explain the behaviour of water in a kettle, we are using an abstraction - an ‘idealized’ model of the kettle that ignores most of its details - as an approximation to a real physical system. But when we use a computer to investigate prime numbers, we are doing the reverse: we are using the physical computer as an approximation to an abstract one which perfectly models prime numbers. Unlike any real computer, the latter never goes wrong, requires no maintenance, and has unlimited memory and unlimited time to run its program.
Abstractions are essential to a fuller explanation. You know that if your computer beats you at chess, it is really the program that has beaten you, not the silicon atoms or the computer as such. [Nor, I would add, the arrangements of pixels on the screen which symbolize knights, pawns, etc.] The abstract program is instantiated physically as a high-level behaviour of vast numbers of atoms, but the explanation of why it has beaten you cannot be expressed without also referring to the program in its own right. That program has also been instantiated, unchanged, in a long chain of different physical substrates, including neurons in the brains of the programmers and radio waves when you downloaded the program via wireless networking, and finally as states of long- and short-term memory banks in your computer. The specifics of that chain of instantiations may be relevant to explaining how the program reached you, but it is irrelevant to why it beat you: there, the content of the knowledge (in it, and in you) is the whole story. That story is an explanation that refers ineluctably to abstractions; and therefore those abstractions exist, and really do affect physical objects in the way required by the explanation.To connect this to the content of the episode - I think all the different representations of a cube on a screen are "real". The parts that you might dismiss as not having an "effect on the world", like the data structures in RAM, or the textual representation stored on disk, are as unavoidably real at their own levels of abstraction. *Colloquially, I think working programmers use "abstraction" to mean things ranging from "I can ignore the details 'below' this point" to "I can compress this implementation into fewer syntax tokens". A contrary view from Dijkstra, which I think is closer to Deutsch's view, is abstraction as "a new semantic layer in which one can be absolutely precise".
Daniel Buckmaster08/25/2023, 1:05 AM
Daniel Buckmaster08/25/2023, 3:36 AM
For the human mind, the tree is the easiest vehicle for complex thoughts. But the city is not, cannot and must not be a tree. The city is a receptacle for life. If the receptacle severs the overlap of the strands of life within it, because it is a tree, it will be like a bowl full of razor blades on edge, ready to cut up whatever is entrusted to it. In such a receptacle life will be cut to pieces. If we make cities which are trees, they will cut our life within to pieces.
Andrew F08/25/2023, 4:49 AM
Konrad Hinsen08/25/2023, 7:06 AM
Daniel Buckmaster08/25/2023, 7:06 AM
Explanations are just another model, and all models are wrong.I think Deutsch would probably agree with this. The "beginning of infinity" in his book's title is referring to his fallibilist philosophy that our explanations and models will never fully capture the entirety of reality, that we will improve on them infinitely - that there is no upper bound on the creation of new knowledge. By "real" in the quotes above, I think one should read something like "our best current approximation of reality", not absolute. objective reality itself.
That's a statement about your own information processing ability, not about reality.I think Deutsch's argument there is that even assuming you can model the entire operation of the computer (down to whatever most-granular level we are currently aware of) - even then, it would be an incomplete explanation, because it would not capture the higher-level abstractions that are real. "Chess" is as real an abstraction as "algebra". Temperature, not so much I think - it's a "rule of thumb" that works well locally, like Newton's laws. But the rules of chess work universally.
Alex Cruise08/25/2023, 6:05 PM
Now it would be very remarkable if any system existing in the real world could be exactly represented by any simple model. However, cunningly chosen parsimonious models often do provide remarkably useful approximations. For example, the law PV = nRT relating pressure P, volume V and temperature T of an “ideal” gas via a constant R is not exactly true for any real gas, but it frequently provides a useful approximation and furthermore its structure is informative since it springs from a physical view of the behavior of gas molecules. For such a model there is no need to ask the question “Is the model true?“. If “truth” is to be the “whole truth” the answer must be “No”. The only question of interest is “Is the model illuminating and useful?“.
|truly wild MEMS hacks>
Andrew F08/25/2023, 6:47 PM
By "real" in the quotes above, I think one should read something like "our best current approximation of reality", not absolute. objective reality itself.
I can only interpret this as "by 'real' in the quotes above one should read 'definitely not real'". Once you've admitted "we don't understand reality, we only have our current best approximation", you've already reached the correct conclusion. Moving on from there to redefine "real" to something more conveniently accessible is not justified. IMO you continue to need separate terms for "real" and "current best understanding", and hey, both those existing terms do alright. You can't lose track of what you're optimizing your models toward, what you're measuring them against.
Daniel Buckmaster08/25/2023, 10:52 PM
Andrew F08/25/2023, 11:42 PM
Daniel Buckmaster08/26/2023, 5:26 AM
Stefan08/26/2023, 7:23 AM
Stefan08/26/2023, 1:52 PM
Beni Cherniavsky-Paskin08/29/2023, 11:21 AM
wtaysom08/30/2023, 7:55 AM
Daniel Buckmaster08/30/2023, 8:00 AM
Beni Cherniavsky-Paskin08/31/2023, 8:27 PM