Some time ago I saw an article here on the topic, but what do the words “deep causality” and “surface analogy” mean. For me personally, at that time it was intuitively obvious what the difference was, including for me it was obvious that the article was not about deep analogies, but only about the concentration of the probabilistic mass, which of course is a very important skill for a rationalist, actually key, but that’s just not what I mean by deep causes, at least.
However, despite this, I could not express my intuition in words at that time. I wasn’t even sure if the analogy could be “truly deep” or just “more profound” than any other.
Since then, I have better understood and deduced for myself the concepts of upward and downward understanding, that is, calculating large consequences from simple base-level laws, or trying to determine basic laws by seeing only large consequences. And in the first case, you can confidently talk about the deepest level, in the second, you can’t, so in theory it can always turn out that you didn’t know the true laws and there is a level even deeper.
An obvious analogy for the surface and the deep is the black box, where the surface is a purely statistical analysis of purely patterns in inputs and outputs, and the deep is an attempt to build a model of the inside of the box.
The difference is that when you talk about statistics, you are always ready to say that there is always some possibility of a different outcome, this will not disprove your model, and when you build the internal structure of the box, your hypothesis says that some combinations of inputs and outputs are strictly impossible. according to your model, if this appears, then your model is wrong, deep causal models are more falsifiable and give clearer answers.
You can also say that the difference is like between a neural network with two layers, input and output and connections between them, and a neural network with a certain number of hidden, invisible, internal layers.
The difference is that deep causal networks require not just saying that there is a certain correlation between the states of inputs and outputs, it requires building a chain of causes between inputs and outputs, laying a specific path between them. And in the real world, you can also often check the specific steps of that very path, not just the ins and outs.
But it can also be compared to a logical versus probabilistic construction, you can “ring out” this circuit and clearly say which outputs will be at which inputs. And as in inference, if you deny the conclusion, you need to point out the specific premises that you then reject. You cannot reject the conclusion without refuting the whole structure of this model.
Like that. Probably later I will formulate it even better and add it.
Some time ago I saw an article here on the topic, but what do the words “deep causality” and “surface analogy” mean. For me personally, at that time it was intuitively obvious what the difference was, including for me it was obvious that the article was not about deep analogies, but only about the concentration of the probabilistic mass, which of course is a very important skill for a rationalist, actually key, but that’s just not what I mean by deep causes, at least. However, despite this, I could not express my intuition in words at that time. I wasn’t even sure if the analogy could be “truly deep” or just “more profound” than any other. Since then, I have better understood and deduced for myself the concepts of upward and downward understanding, that is, calculating large consequences from simple base-level laws, or trying to determine basic laws by seeing only large consequences. And in the first case, you can confidently talk about the deepest level, in the second, you can’t, so in theory it can always turn out that you didn’t know the true laws and there is a level even deeper. An obvious analogy for the surface and the deep is the black box, where the surface is a purely statistical analysis of purely patterns in inputs and outputs, and the deep is an attempt to build a model of the inside of the box. The difference is that when you talk about statistics, you are always ready to say that there is always some possibility of a different outcome, this will not disprove your model, and when you build the internal structure of the box, your hypothesis says that some combinations of inputs and outputs are strictly impossible. according to your model, if this appears, then your model is wrong, deep causal models are more falsifiable and give clearer answers. You can also say that the difference is like between a neural network with two layers, input and output and connections between them, and a neural network with a certain number of hidden, invisible, internal layers. The difference is that deep causal networks require not just saying that there is a certain correlation between the states of inputs and outputs, it requires building a chain of causes between inputs and outputs, laying a specific path between them. And in the real world, you can also often check the specific steps of that very path, not just the ins and outs. But it can also be compared to a logical versus probabilistic construction, you can “ring out” this circuit and clearly say which outputs will be at which inputs. And as in inference, if you deny the conclusion, you need to point out the specific premises that you then reject. You cannot reject the conclusion without refuting the whole structure of this model. Like that. Probably later I will formulate it even better and add it.