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TheAncientGeek comments on Open thread, Jul. 25 - Jul. 31, 2016 - Less Wrong
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Comments (133)
Missing the point. The point is how their conclusions are verified.
Logic is implicit in empricisicm because the idea that contradictions are false is implicit in the idea of disproof by contradictory evidence.
Missing the point. I didn't say that logic is sufficient for correctness. I am saying that if you have some sort of black-box, but effective reasoning, then some kind of presupposition is going to be needed to verify it.
If you have other reasoning show it. Otherwise that was an irrelevant nitpick.
I think Science and Sanity lays out a framework for dealing with beliefs that doesn't categories them into true/false that is better than the basic true/false dichomity.
I care more about what Science and Sanity called semantic reactions than I care about presuppositions.
Basically you feed the relevant data into your mind and then you let it process the data. As a result of processing it there's a semantic reaction. Internally the brain does that with a neural net that doesn't use logical chains to do it's work.
When I write here I point out the most important piece of the data, but not all of what my reasoning is based on because it's based on lots of experiences and lots of empiric data.
Using a ramified logic with more than two truth values is not the same as not using logic at all!
That is such a vague description of reasoning that it covers everything from superforecasting to schizobabble. You have relieved yourself of the burden of explaining how reasoning works without presupposiitons by not treating reasoning as something that necessarily works at all.
Could you define what you mean with "logic" if not thinking in terms of whether a statement is true?
Thinking about how probable it is, or how much subjective credence it should have. There are formal ways of demonstrating how fuzzy logic and probability theory extend bivalent logic.
Science and Sanity is not about probability theory or similar concepts of having numbers between 0 and 1.
"The map is not the territory" doesn't mean "The map is the territory with credence X that's between 0 and 1". It's rather a rejection about the concept of the is of identity and instead thinking in terms like semantic reactions.
I was pointing out that the claim that logic is implicit in empiricism survives an attack on bivalence. I couldn't see any other specific point being made.
Let's say I want to learn juggling. Simply reading a book that gives me a theory of juggling won't give me the skill to juggle. What gives me the skill is practicing it and exposing myself with the practice to empiric feedback.
I don't think it's useful to model that part of empiric learning to juggle with logic.
Juggling with logic is a loose metaphor...literally, juggling is a physical skill, so it cannot be learnt from pure theory. But reasoning is not a physical skill.
If you were able to make implicit reasoning explicit, you would be able to do useful things like seeing how it works, and improving it. I'm not seeing the downside to explicitness. Implicit reasoning is usually more complex than explicit reasoning, and it's advantage lies in its complexity, not it's implicitness.
Why do you think the dualistic distinction of physical and mental is useful for skill learning? But if you want a more mental skill how about dual n-Back?
The problem is that the amount of information that you can use for implicit reasoning vastly outweighs the amount of information for explicit reasoning. It's quite often useful to make certain information explicit but you usually can't make all available information that a brain uses for a reasoning process explicit.
Besides neither General Semantics or the Superforcasting principles are against using explicit reasoning. In both cases there are quite explicit heuristics about how to reason.
I started by saying that your idea that all reasoning processes are either explicit or implicit is limiting. In General Sematics you rather say "X is more explicit than Y" instead of "X is explicit". Using the binary classifier mean that your model doesn't show certain information about reality that someone who uses the General Sematics model uses shows.
"Explicitness is important" isn't a defense at all because it misses the point. I'm not against using explicit information just as I'm not against using implicit information.
If you agree that it covers superforcasting than my argument is right. Using presuppotions is a very particular way of reasoning and there are many other possible heuristics that can be used.
A LW comment also isn't long enough to lay out a complete system of reasoning as complex as the one proposed in Science and Sanity or that proposed in Superforcasting. That why I refer to general arguments are refer to the books for a more detailed explanation of particular heuristics.
There's basically two kinds of reasoning - the kind that can be made manifest (explicit,etc) and the kind that can't. The gold standard of solving of solving the problem of presuppositions (foundations, intuitions) is to show that nothing presupposition-like is needed in explicit reasoning. Failed attempts tend to switch to implicit reasoning, or to take it that sufficiently obvious presupposiitons don't count as presuppositions (We can show this with induction...we can show this with empiricism).
I don't think that's the case. Trying to put complex concepts into two boxes binary boxes is done very frequently in the Western tradition but there no inherent argument that it's the best way to do things. Science and Sanity argues in detail why binary thining is limiting.
As far as this particular case of the implicit/explicit distinction, most kinds of reasoning tend to be a mix. Reasoning that's completely explicit is the kind of reasoning that can be done by a computer with very limited bandwith. For many problems we know that computers can't solve them as easily as calculating 23472349 * 5435408 which can be done completely explicitely. If you limit yourself to what can be made completely explicit you limit yourself to a level of intelligence that can't outperform computers with very limited memory/CPU power.
Explicit reasoning has a its disadvantages, but is still hard to do without. In talking about superforecasters, you are taking it that someone has managed to determine who they are as opposed to ordinary forecasters, raving lunatics, etc. Deterimining that kind of thing is where explicit reasoning..what's the alternative? Groups of people intuiting that each other are reliable intuiters?
That's why you mix it with implicit reasoning if you care about the outcome of the reasoning process. Doing everything implict is as bad as doing everything explicit.
I would have thought the problem with doing everything explicitly is that it is not possible.
Our usual way of combining explicit about and implicit reasoning is to reason explicitly from premises which we find intuitively appealing, ie which we arrive at by implicit reasoning. That isn't a solution to the problem, that is the problem: everything is founded on presuppositions, and if they are implicit we can't check how they are arrived at, and we also can't check how reliable they are without needing to use further presuppositions.
Korzybski seems to be saying we should be using more implicit reasoning. I don't s how that helps.
I don't think that's what he's saying. In the case of "consciousness of abstraction" he even encourages people to be explicit about things that they usually aren't.
Korzybski takes a long book to explain how he thinks reasoning should be done and coins a bunch of basic concepts on which it should be built that are internally consistent. I don't think I can give you a full understanding of how the framework works in the space of a few comments.