(Im going to make some references to Critical Rationalism, since I don't know where the line is drawn between CR and CF).
Arguments Should Be Decisive Criticisms
Decisive criticism would be great,if available. You said that proof is difficult because you have to show that a theory works in every case. But there can be criticism of criticism, counter counter argument against counter argument. (Even criticisms of critical rationalism!). So, to know that a criticism is decisive, you have to know that no one could possibly come up with a counter criticism. (And that no one could reframe the whole arena). Which presents a similar problem to the problem of decisive proof. Neither form of perfection is available.
Theoretical questions have a particularly acute version of the problem. In philosophy, even the nature of truth is disputable. Practical decisions where you get instant feedback on success are much easier. The two are not the same.
Decisive positive arguments are either rare or entirely inaccessible
Ditto decisive negative arguments!
Imperfect criticisms and imperfect negative evidence could still weigh more heavily than imperfect support and positive evidence. That still doesn't add up to the CR claims that there is no justification, and induction never works. You are right that induction is dumb, but it still sometimes works..especially if taken probabilistically. The Turkey paradox is an example where it doesn't work, the Sun rising in the East is an example where it does.
Dumb is better than nothing. In an imperfect world, you need to use anything with non-zero value.
A decisive argument (or group of arguments) contradicts the negation of its conclusion, so both can’t be true
Weighting is needed to see which is false. Contradiction alone does not settle anything because it is symmetric. Regarding evidence as weighing more than theory, which is needed to make naive Popperism work, is a form of weighting assumed by default.
I’ve written criticisms of induction and weighted factors before.
Weighting isn't adding apples and oranges , it's adding value_of(n apples) and value_of(m oranges). Everything gets converted to the same type first.
Yes, it's indecisive, but you have to use weighting , so you are stuck with it.
CF’s most important idea involves distinguishing between decisive and indecisive arguments. CF evaluates ideas by whether they succeed or fail at a purpose (achieve a goal, solve a problem, answer a question). Ideas are for something and can’t be evaluated in isolation
That's two ideas, and they are somewhat at odds.
Something can seem decisive in a limited context, but not in a wider one. Since we do not have perfect knowledge of the widest possible context, we can't say acontextually that anything is decisive. And we can't "just see" that something is "just true". Deep theoretical issues are different to shallow empirical ones.
All our arguments are fallible
Then none are decisive!
A single counter-example or error can be a decisive criticism without considering vast numbers of possibilities. Popper discussed this with examples like “all ravens are black”, which is refutable with one contradictory observation, but still not proven with a million compatible observations.
Well .. it's not as simple as naive CR makes out. A single observation can be erroneous (eg Martian canals, cold fusion). Hence the norm of replication in science. Also , theories can be patched to handle objections (Kuhn, Quine, Duheim). Etc.
If indecisive arguments are logically flawed, then why do they often appear to work moderately well
Indecisive arguments dont have to be logically flawed...they can be reframed as valid probabilistic arguments. So long as you are willing to abandon certainty...which you are if you are a falliblist!
Positive arguments point out good trait(s)
Positive evidence...is evidence, not argument
CF’s main motivation is logical arguments showing that various other approaches cannot possibly work.
I don't think you have shown that. You haven't shown that decisive falsification always works, or that the alternatives never do.
Thanks for engaging.
So, to know that a criticism is decisive, you have to know that no one could possibly come up with a counter criticism.
I think you didn't take into account the definition that I used: "A decisive argument (or group of arguments) contradicts the negation of its conclusion, so both can't be true." Excluding the possibility of counter criticism is unnecessary for this definition to be met. The point is that if A and B could both be true – if they're compatible – then it's problematic to view B as a criticism of A.
Neither form of perfection is available.
The goal is basically logical relevance, not perfection.
You are right that induction is dumb, but it still sometimes works..especially if taken probabilistically.
For induction to work, it'd have to define steps a person can follow to induce a theory. It'd have to specify what constitutes inducing a theory. The main issue with induction isn't the quality of the results, but actually defining a specific method that produces any results. Over the years, I've never been able to get an answer to this along with a worked example and answers to basic questions like which of the infinitely many patterns fitting the data should be induced and which shouldn't and why those.
Weighting is needed to see which is false.
When two things contradict and you're deciding what side to take, weighting them and choosing the higher weighted side is one approach. But it's certainly not the only approach. Since you're just choosing between two things, quantitative evaluation seems less relevant or appealing than in many other scenarios.
I could go into more detail here and it's an interesting topic but I think I've written enough for an initial reply so I'll leave it at saying I don't see what aspect of contradiction-resolution makes quantitative approaches mandatory. My best guess is you think they're always mandatory for everything, which might be better approached from another angle, not via this sub-problem.
Weighting isn't adding apples and oranges , it's adding value_of(n apples) and value_of(m oranges). Everything gets converted to the same type first.
My link discusses dimension conversion (like from apples to value) being problematic. That's covered.
All our arguments are fallible
Then none are decisive!
Do you think fallibilism prohibits reaching conclusions? Decisive basically means conclusive, aka adequate to tentatively, fallibly reach a conclusion, as against arguments that don't provide that much (where accepting the truth of the argument, as a premise, would still be inadequate to reach a conclusion).
Well .. it's not as simple as naive CR makes out. A single observation can be erroneous (eg Martian canals, cold fusion).
Popper knew that and wrote about it.
Indecisive arguments dont have to be logically flawed...they can be reframed as valid probabilistic arguments.
Do you have an example? If it's actually valid, I might tell you it's decisive. As above, decisive is an easier standard than you interpreted it as. I'm not sure what sort of probabilistic argument you have in mind though.
I think you didn’t take into account the definition that I used: “A decisive argument (or group of arguments) contradicts the negation of its conclusion, so both can’t be true.” Excluding the possibility of counter criticism is unnecessary for this definition to be met. The point is that if A and B could both be true – if they’re compatible – then it’s problematic to view B as a criticism of A.
I don't see how that connects to the ordinary meaning of "decisive".
In fact passages like this
Decisive positive arguments are either rare or entirely inaccessible. Pointing out 1000 good things isn’t enough to prove an idea will succeed at its purpose
Make it sound like decisiveness, is the same as certainty. But if a "decisive" argument is fallible, and can be overridden, that is treating the overriding argument as having more weught.
The goal is basically logical relevance, not perfection.
Is this all about Hempel's paradox?
Nothing about seeing a lot of black ravens actually means that there couldn’t be a white one.
Something about seeing a black raven means the next raven you see is slightly more likely to be black. Yet , you reject that reasoning. Yet, it's relevant enough.
Something about seeing a white raven means that ravens aren't all black ...but not with certainty ..? But decisiveness isn't certainty?
For induction to work, it’d have to define steps a person can follow to induce a theory. It’d have to specify what constitutes inducing a theory.
Minimally, induction induces patterns , not theories , and the simplest pattern is that events that have been observed multiple times in the past are likely to occur again in the future.
As you yourself said:-
The basic concept of induction is to find patterns in data and learn from them.
The simplest pattern is "what will.happen before will happen again". Simple organisms can implement that...ve"
The main issue with induction isn’t the quality of the results, but actually defining a specific method that produces any results.
...while.machine learning can implement more complex versions. https://en.wikipedia.org/wiki/Rule_induction
Over the years, I’ve never been able to get an answer to this along with a worked example and answers to basic questions like which of the infinitely many patterns fitting the data should be induced and which shouldn’t and why those.
Obviously, we should start with the simplest. And we have to, if we are building an induction machine. We don't have to find a singular, perfect rule, if we are just trying to make good-enough probabilistic predictions. Even the Turkey is right 364/365 times..
The important thing is not to expect the probabilistic prediction to amount to certainty, and not to expect prediction to amount to explanation. Within those limits, induction, as probabilistic prediction, works.
David Deutsch says induction is all about generating theories or knowledge or something , but you don't have to take that at face value. There's a simpler way of thinking about induction that is much more defensible.
When two things contradict and you’re deciding what side to take, weighting them and choosing the higher weighted side is one approach. But it’s certainly not the only approach.
You need to argue that it cannot work."CF’s main motivation is logical arguments showing that various other approaches cannot possibly work".
Since you’re just choosing between two things, quantitative evaluation seems less relevant or appealing than in many other scenarios.
I don't see the relevance.
I could go into more detail here and it’s an interesting topic but I think I’ve written enough for an initial reply so I’ll leave it at saying I don’t see what aspect of contradiction-resolution makes quantitative approaches mandatory.
I don't see the alternative. If arguments aren't infallible, you would need to count and weight them.
My link discusses dimension conversion (like from apples to value) being problematic.
Problematic is far short of "couldn't possibly work".
Do you think fallibilism prohibits reaching conclusions?
You could lower the bar so that you draw conclusions at some likelihood lesss than 100% ...but a lot of the things you object to could pass that bar too.
Decisive basically means conclusive, aka adequate to tentatively, fallibly reach a conclusion,
In the absence of certainty, you can reach (tentative) conclusions by weighing evidence and arguments , and going with the strongest. I can't see how you can do that without weighing.
as against arguments that don’t provide that much (where accepting the truth of the argument, as a premise, would still be inadequate to reach a conclusion).
It's obvious that C.F. Is better than arguments that are irrelevant. Its not obviou s it's better than weighting and induction.
Popper knew that and wrote about it.
I wasn't accusing Pooper of naive Popperism.
Thanks for engaging again.
Decisive: I think this is the best issue to resolve first and I'm hopeful we'll be able to succeed here.
The ordinary meaning of "decisive" is "settling an issue; producing a definite result". I don't see where it says infallibly, permanently, without the possibly of later revision, or anything like that. We can reach a definite result (a conclusion) based on our currently available evidence and ideas.
People often talk about strong and weak arguments. All weak or moderate arguments, and many strong arguments, are indecisive. When shopping for a house, you might note nice kitchen countertops (indecisive, weak argument), a pool (indecisive, strong argument), painted a pretty color (indecisive, weak argument), large yard (indecisive, moderate argument), and many more things. Or you might figure out your goal specifically enough to enable a decisive argument like "I want a commute under 15 minutes and 4+ bedrooms; this house has 3 bedrooms so I won't buy it". Both styles of argument are fallible. But they do have a clear, significant difference. I think "decisive" is a good fit for this difference: 3 bedrooms being too few settles the issue and produces a definite result, whereas the large yard didn't. Logically, on the assumptions or premises that the house has 3 bedrooms and the goal is 4+, we can reach a conclusion. But if we know it has a large yard and our goal is a good house, we cannot reach a conclusion: that's compatible with picking or not picking this house.
Nothing about this is infallible. I could have misunderstood logic, or counting, or my goal, or what a house is, or all sorts of other things. While any of my conclusions are open to potential revision, it's also realistic that they aren't revised anytime soon, so despite fallibilism there is a significant difference between issues where I reached a conclusion and issues where I didn't.
Also, are you familiar with Elimination by Aspects (EBA) or Satisficing? They have similarities/overlap with CF which could help clarify this part.
If you're familiar with MCDM/MCDA literature, that could help too. There's a concept of compensatory and non-compensatory approaches. Compensatory approaches mean that a weak score on some factors can be compensated for by a strong score on other factors. Compensatory approaches use factors indecisively, while non-compensatory approaches use factors decisively. In EBA, if a theory fails at one of the criteria then it's eliminated with no way to un-eliminate it within the current decision making process (you have to go outside the process and invoke fallibility, new information, etc., to revise the conclusion).
Hempel's Paradox: Relevant. Part of the issue.
Asymmetry: When you see a white raven, that doesn't provide certainty. You could have misidentified the bird species. But on the premise that you saw a white raven, then logic enables you to conclude that "all ravens are black" is false. Asymmetrically, on the premise that you really did see a black raven, or a million of them, you cannot conclude that "all ravens are black" is true. With some arguments, if you assume your premises and background knowledge are true, then logic dictates a conclusion, while with other arguments even if your premises and background knowledge are correct that still wouldn't be enough to reach the conclusion. Some arguments are decisive (settle issues, produce definite results) when assuming their premises and your background knowledge, while others still aren't. This difference is compatible with fallibility (your premises and background knowledge could be doubted, revised, etc.).
Simplest pattern:
The simplest pattern is "what will.happen before will happen again". Simple organisms can implement that...ve"
There are infinitely many patterns which fit the past. Of those patterns, infinitely many will break in the near future, infinitely many will break in the distant future, and infinitely many will hold forever. Many of these different patterns fit the data perfectly and contradict each other. Do you disagree? If you agree, then this simple pattern idea doesn't guide which patterns to induce/use, right? So I don't see how this claim helps. Examples: https://xkcd.com/1122/
Rule induction: Do any of these claim to offer a general purpose thinking method (including capable of doing philosophy debates, like we are now) which solves the which pattern(s) problem?
Cannot work for induction: patterns are likely to continue in the future approaches cannot possibly work in the context of infinitely many patterns that don't continue and no viable solution for choosing between patterns.
Cannot work for weighted factors: Dimension conversion to generic goodness only works approximately and only in special cases. Other dimension conversions are also special cases, though some aren't approximate (like E=mc^2). Relying on dimension conversion cannot possibly work for a general purpose thinking system because it's not generally available. Also, the concept of factor weights relies on the importance of the factor being approximately the same for different values of the factor, which is often false (both due to failure breakpoints and due to diminishing marginal utility).
Certainty: I've been trying to discuss fallibilist versions of CF, weighted factors and induction. Critiquing infallibilists wasn't my focus. One of my last discussions with David Deutsch was actually about this, back in ~2013. From memory, he basically claimed that all justificationists (advocates of any kind of positive/supporting arguments) are infallibilists, which I denied. I brought up LessWrong people in general as an example, since they tend to be non-Popperian fallibilists. He claimed that they're only fallibilists by contradicting themselves, which doesn't really count or help. I was unable to find out from him what the alleged contradiction is between 1) fallibilism 2) positive/supporting/justifying arguments.
Duhem-Quine:
I wasn't accusing Pooper of naive Popperism.
ok great. I don't know who you were accusing, but generally speaking there are plenty of Popperians who I'm unimpressed by, so we might agree, idk.
Decisiveness
. When shopping for a house, you might note nice kitchen countertops (indecisive, weak argument), a pool (indecisive, strong argument), painted a pretty color (indecisive, weak argument), large yard (indecisive, moderate argument), and many more things. Or you might figure out your goal specifically enough to enable a decisive argument like “I want a commute under 15 minutes and 4+ bedrooms; this house has 3 bedrooms so I won’t buy it”.
If I find two houses with four bedrooms and a fifteen minute commute, I can decide beteeen them using indecisive, nice-to-have features like a swimming pool as a further criterion.
I'm not forbidden from using decisive criteria, if that's what they are. CRs and CFs are self-forbidden from using various things , though.
Decisive + indecisive criteria is better than decisive alone, because it enables.more fine grained decision making.
Both styles of argument are fallible. But they do have a clear, significant difference. I think “decisive” is a good fit for this difference: 3 bedrooms being too few settles the issue and produces a definite result, whereas the large yard didn’t. Logically, on the assumptions or premises that the house has 3 bedrooms and the goal is 4+, we can reach a conclusion. But if we know it has a large yard and our goal is a good house, we cannot reach a conclusion: that’s compatible with picking or not picking this house.
Nothing about this is infallible.
Then decisiveness.isn't an objective criterion ...it's a question of setting up a threshhold, saying that 80% or 90% or 99% likelihood counts as decisiveness. Decisiveness is disguised weighting, if it isn't infallibility.
Asymmetry
When you see a white raven, that doesn’t provide certainty. You could have misidentified the bird species. But on the premise that you saw a white raven, then logic enables you to conclude that “all ravens are black” is false. Asymmetrically, on the premise that you really did see a black raven, or a million of them, you cannot conclude that “all ravens are black” is true.
You cannot conclude it is certain, but you can conclude it is likely and.calculate a likeliihood.
Induction
There are infinitely many patterns which fit the past. Of those patterns, infinitely many will break in the near future, infinitely many will break in the distant future, and infinitely many will hold forever. Many of these different patterns fit the data perfectly and contradict each other.
Yes. But I can still choose the simplest that fits the data I currently have , Ie. I can do induction in a good-enough way.
Do you disagree? If you agree, then this simple pattern idea doesn’t guide which patterns to induce/use, right?
I do not agree, it does, that's the whole point. You start with the simplest, and move in to the next simplest, and so on.
We know that machines can induce, in a good-enough way, so there must be an algorithm for it.
Try it yourself. ... imagine you are playing a game where you have to guess the next letter in a sequence. If the sequence starts "aaa..." You would naturally guess " a" ". Anyone would, because everyone can do basic induction. If it turned out the next letter was "b" you could guess that he pattern is
"aaabaaabaaab.."
or
"aaabbbaaabbb.."
or
"aaabbbbccccddd..."
And maybe some other possibilities. Notice that you are not certain which pattern is the right one. Notice also that you are not at a loss to come up with simple candidate patterns ... the infinity of possible patterns isn't impeding you. Notice also that you can still make a probablistic prediction, eg. 2/3 probability that the fifth letter will be a "b".
" But surely there are more than three candidate patterns! " There are more complex patterns that fit, but they get low weighting because they are complex.
"But that's Conjecture and Refutation!" Maybe it is! If you want to say induction cannot possibly work , and maintain that C&R does work, you need to show that induction isn't a form of C&R. (And also that it's failing at something that is actually claimed for it by inductionists).
Rule induction: Do any of these claim to offer a general purpose thinking method (including capable of doing philosophy debates, like we are now) which solves the which pattern(s) problem?
The only valid objections to induction are that it doesn't achieve some.kind of perfection , such as complete certainty, or complete generality
That was an objection from generality. Its irrelevant , because I only claimed that induction was capable of working predictively and probabilistically. That indeed does not work for certain high bar problems, but that should not be summarised as "cannot work at all".
BTW, we dont know that what we are doing now is fully general. Maybe there are things human beings just can't think of.
Cannot work for induction: patterns are likely to continue in the future approaches cannot possibly work in the context of infinitely many patterns that don’t continue and no viable solution for choosing between patterns.
There is a way of choosing between patterns. It's simplicity as I said. It can be shown to work,... so long as you are only aiming for probabilistic prediction. There's an argument t that induction can't tell you the exact laws of nature, with certainty, given a limited data set, but that much more ambitious than what I am talking about.
Weighting
Cannot work for weighted factors: Dimension conversion to generic goodness only works approximately and only in special cases.
If you are only trying to satisfy your only values, then the weighting is just how much you value things in relation to each other. Presumably, your objection is that the lack of objective criteria ..but if you are making a personal decision, why would that matter.
Justification
Critiquing infallibilists wasn’t my focus. One of my last discussions with David Deutsch was actually about this, back in ~2013. From memory, he basically claimed that all justificationists (advocates of any kind of positive/supporting arguments) are infallibilists, which I denied. I brought up LessWrong people in general as an example, since they tend to be non-Popperian fallibilists. He claimed that they’re only fallibilists by contradicting themselves, which doesn’t really count or help. I was unable to find out from him what the alleged contradiction is between 1) fallibilism 2) positive/supporting/justifying arguments.
Yes, Deutsch is frustrating. He tends to state things without justification. That's consistent with his rejection of justifcationism , but at the same time you generally need more than "my idea is by contradicted by anything" to motivate you change your mind. Which is an argument for justificationism from argumentative reasoning.
Then decisiveness.isn't an objective criterion ...it's a question of setting up a threshhold, saying that 80% or 90% or 99% likelihood counts as decisiveness. Decisiveness is disguised weighting, if it isn't infallibility.
Per my article, decisiveness, like other idea evaluation, depends on the goal and context. "It costs $100" is decisive criticism for a $20 budget goal but not a $200 budget goal.
But this doesn't use likelihoods or weights. It uses qualitative differences or breakpoints for quantities (which are the points where there difference in quantity makes a qualitative difference). The generic breakpoint is "good enough for success at my goal or not?"
Decisive + indecisive criteria is better than decisive alone, because it enables.more fine grained decision making.
You can do fine-grained decision making, without limitation, using decisive reasoning alone. And convenience comparisons or marginal benefits are irrelevant given my claim (which is currently an open issue under discussion) that indecisive reasoning doesn't work at all.
If you are only trying to satisfy your only values, then the weighting is just how much you value things in relation to each other. Presumably, your objection is that the lack of objective criteria ..but if you are making a personal decision, why would that matter.
Epistemology should be general purpose and cover impersonal issues like scientific controversies, and allow for productive debate rather than being subjective or arbitrary.
By no objective criteria do you mean people can and should just subjectively/intuitively make up the numbers with no math? If so, how can they do that? How would they or their intuition determine what numbers roughly feel right? By using intelligence via some other full general-purpose epistemology which has been used as a premise/prerequisite of this approach? My understanding is that for this kind of weighted factor math stuff to be a first epistemology – a first solution to how people think intelligently, as I believe its claimed to be – then the math has to work objectively and you can't just rely on people somehow intelligently coming up with numbers that are in the right ballpark. If you rely on intelligence then it's only a secondary method which leaves all the primary questions in epistemology open.
Also if the numbers are being made up non-objectively so they feel about right, why not just make up a conclusion that feels about right directly? What good is the intermediate step of making up the numbers?
"But that's Conjecture and Refutation!" Maybe it is! If you want to say induction cannot possibly work , and maintain that C&R does work, you need to show that induction isn't a form of C&R. (And also that it's failing at something that is actually claimed for it by inductionists).
There are many different versions of induction. If you pick a specific version of induction (preferably one with at least one book explaining it in detail like Popper's books explain Critical Rationalism) then we can discuss how it differs from C&R, what it claims, and whether it lives up to those claims.
There are infinitely many patterns which fit the past. Of those patterns, infinitely many will break in the near future, infinitely many will break in the distant future, and infinitely many will hold forever. Many of these different patterns fit the data perfectly and contradict each other.
Yes. But I can still choose the simplest that fits the data I currently have , Ie. I can do induction in a good-enough way.
Which patterns are simplest? What's the rule to judge that? Does applying the rule require intelligence as a prerequisite?
Per my article, decisiveness, like other idea evaluation, depends on the goal and context. “It costs $100” is decisive criticism for a $20 budget goal but not a $200 budget goal.
But you can't expect any given context to supply you with a set of decisive criteria that narrow your options to one.
But this doesn’t use likelihoods or weights.
It uses an arbitrary threshold of decisiveness.
It uses qualitative differences or breakpoints for quantities (which are the points where there difference in quantity makes a qualitative difference). The generic breakpoint is “good enough for success at my goal or not?”
The examples you have given look.qualitative.
Decisive + indecisive criteria is better than decisive alone, because it enables.more fine grained decision making.
You can do fine-grained decision making, without limitation, using decisive reasoning alone.
I don't see how.
And convenience comparisons or marginal benefits are irrelevant given my claim (which is currently an open issue under discussion) that indecisive reasoning doesn’t work at all.
If you are only trying to satisfy your only values, then the weighting is just how much you value things in relation to each other. Presumably, your objection is that the lack of objective criteria ..but if you are making a personal decision, why would that matter.
Epistemology should be general purpose and cover impersonal issues like scientific controversies, and allow for productive debate rather than being subjective or arbitrary.
Epistemology quite possibly can't be general purpose, in the sense that the same techniques apply to different kinds of problem.
By no objective criteria do you mean people can and should just subjectively/intuitively make up the numbers with no math?
I mean with subjective criteria.
If so, how can they do that? How would they or their intuition determine what numbers roughly feel right?
They can do that. Asking how they do it doesn't mean it's impossible.
By using intelligence via some other full general-purpose epistemology which has been used as a premise/prerequisite of this approach? My understanding is that for this kind of weighted factor math stuff to be a first epistemology – a first solution to how people think intelligently, as I believe its claimed to be – then the math has to work objectively and you can’t just rely on people somehow intelligently coming up with numbers that are in the right ballpark. If you rely on intelligence then it’s only a secondary method which leaves all the primary questions in epistemology open.
Different problems require different approaches. I'm not saying subjective weighting is the answer to everything
Also if the numbers are being made up non-objectively
Non objective and made-up are not the same thing.
so they feel about right, why not just make up a conclusion that feels about right directly?
People do. I am not saying there is one method to rule them all.
What good is the intermediate step of making up the numbers?
If a thing is worth doing , it is worth doing with made up numbers.
There are many different versions of induction.
Which is why it is difficult to show none of them could possibly work.
If you pick a specific version of induction (preferably one with at least one book explaining it in detail like Popper’s books explain Critical Rationalism) then we can discuss how it differs from C&R, what it claims, and whether it lives up to those claims.
I have picked probabilistic prediction, which can be shown to work directly, without needing a theoretical justification.
There are infinitely many patterns which fit the past. Of those patterns, infinitely many will break in the near future, infinitely many will break in the distant future, and infinitely many will hold forever. Many of these different patterns fit the data perfectly and contradict each other.
Yes. But I can still choose the simplest that fits the data I currently have , Ie. I can do induction in a good-enough way.
Which patterns are simplest?
You know the "aaaaa" pattern is simpler than the others. Its no great mystery.
What’s the rule to judge that?
People here like Kolmogorov complexity. That isn't some unanswerable question.
Does applying the rule require intelligence as a prerequisite?
You don't need much intelligence to do simple induction, since simple organisms can do it.
But you can't expect any given context to supply you with a set of decisive criteria that narrow your options to one.
Most goals have many solutions which we should be ~indifferent between – they all work and it's not worth our time to optimize more.
In the cases where optimization is worthwhile and there are multiple solutions, we can narrow it down further by considering more ambitous goals.
As a simple approximation, looking only at viable solutions you want to optimize between, you may maximize one factor. Maximizing a single factor doesn't require combining factors, dimension conversion, rank ordering or weighting, and keeps the method non-compensatory (a problem with one factor can't be outweighed by some other factors being good). The problems with non-linear value functions are often quite manageable when dealing with only one non-binary factor. If you model decision making as multiplying many binary factors, you can also multiply in one non-binary factor without the problems that come from multiple non-binary factors. This gives you a simple answer which I don't consider ideal but it's mostly OK and doesn't require reading essays to get a more complicated answer.
It uses an arbitrary threshold of decisiveness.
Budgets, or more generally goals, aren't arbitrary and have breakpoints/thresholds inherent in them, which we should look for. The most generic threshold is "enough (or a low enough amount for negative factors) for goal success".
If so, how can they do that? How would they or their intuition determine what numbers roughly feel right?
They can do that. Asking how they do it doesn't mean it's impossible.
My claim is it can't be done other than via conjectures and refutations, CF, the stuff I'm advocating. I'm claiming that other methods don't work. If people do it but you don't know how, that is compatible with my claim, since they may be using the things I'm saying do work. This isn't counter-evidence against me.
There are many different versions of induction.
Which is why it is difficult to show none of them could possibly work.
They have common themes, so it can be done using abstract arguments as long as people agree in broad strokes on what sorts of things are and aren't induction. If you start loosening up the definition of "induction" to include C&R, that's way too broad, and it's no longer the same thing that Popper or I said doesn't work, and it no longer fits the historical tradition/meaning of induction (unless we're missing something, which you'd have to show).
If you pick a specific version of induction (preferably one with at least one book explaining it in detail like Popper’s books explain Critical Rationalism) then we can discuss how it differs from C&R, what it claims, and whether it lives up to those claims.
I have picked probabilistic prediction, which can be shown to work directly, without needing a theoretical justification.
My primary concern with literature isn't the justification but just the specification of how it works. You haven't provided a well-defined non-moving target for my criticism, as both CR and CF provide to you. Usually, even when highly abstract discussion is pretty effective (as is needed to cover induction generically), it's still best to go over at least one more specific example, so if you could specify one version of induction in detail (preferably via cite) we could use it as an example.
You know the "aaaaa" pattern is simpler than the others. Its no great mystery.
I have an answer in that easy case that I believe I got via C&R. If you don't give the math, then you aren't showing that some non-C&R method can evaluate simplicity. And just because I have an answer in a few easy cases doesn't mean that you or I have a good answer in harder cases.
People here like Kolmogorov complexity. That isn't some unanswerable question.
Kolmogorov complexity is uncomputable and machine-dependent, right? So it's not a usable approach. That people like it anyway is evidence about how hard the question is and how poor the known answers are.
You don't need much intelligence to do simple induction, since simple organisms can do it.
I deny that humans can do induction. I also deny that simple organisms can do it. I doubt this is a good sub-topic to go into right now.
Most goals have many solutions which we should be ~indifferent between – they all work and it’s not worth our time to optimize more.
Many don't: they have solutions that deliver worthwhile but but critical amounts of utility. So the one size fits all approach isnt going to work for them.
My claim is it can’t be done other than via conjectures and refutations
Your claim was that it could not possibly work at all.
Anyway: simple induction can be implemented by simple organisms and programmes. They are too simple to be deliberately making conjectures, but capable of running a hardwired algorithm that just expects the same result from the same cause.
You haven’t provided a well-defined non-moving target for my criticism, as both CR and CF provide to you.
Yes I have: better than chance prediction.
You know the “aaaaa” pattern is simpler than the others. Its no great mystery.
I have an answer in that easy case that I believe I got via C&R.
That doesn't mean C&R is the only possible mechanism.
People here like Kolmogorov complexity. That isn’t some unanswerable question.
Kolmogorov complexity is uncomputable and machine-dependent, right
I'm not a fan myself, but it's not like no one has any clue about how simplicity works.
I deny that humans can do induction. I also deny that simple organisms can do it.
Deny what it like, there's evidence they do it
Chatgpt: Induction, in a broad sense, means learning a general rule or expectation from repeated experience rather than from a single fixed instinct. Many animal behaviours fit this pattern, even if they’re simpler than human reasoning. Here are some clear examples: Trial-and-error learning (generalising from outcomes) Rats in mazes learn that certain turns or paths tend to lead to food. Over time, they don’t just remember one route—they form a general expectation like “this direction usually pays off.” This kind of behaviour was famously studied by Edward Thorndike, who showed animals gradually “induce” successful strategies. Conditioning (predictive associations) In classical conditioning experiments by Ivan Pavlov, dogs learned that a bell predicts food. They generalise from repeated pairings to a rule: “bell → food is coming.” This is inductive because the animal infers a predictive relationship from repeated experience. Foraging decisions (learning patterns in the environment) Bees learn which flower colours or shapes tend to contain nectar. They don’t test every flower randomly forever—they generalise: “purple flowers here are usually rewarding.” This shows induction from multiple encounters to a probabilistic rule. Predator avoidance (learning danger cues) Birds that survive encounters with predators often learn to recognise certain shapes or movements (e.g., hawk silhouettes) as dangerous. They generalise from specific experiences to a broader category: “things like this are threats.” Habituation and sensitisation (learning what matters) Animals stop responding to repeated harmless stimuli (habituation), effectively “learning” that a stimulus predicts nothing important. Conversely, sensitisation increases response after significant events. Both involve extracting regularities from experience. Tool use and problem solving (higher-level induction) Some primates and corvids (like crows) learn rules about tools—for example, that sticks can retrieve food from holes. Over time, they apply this rule in new contexts, suggesting a more flexible, inductive generalisation. A useful distinction: Not all learned behaviour is equally “inductive.” Simple conditioning might just be association, while more complex behaviours (like those in crows or apes) come closer to forming abstract rules. But in all these cases, the key feature is the same: the animal uses past experiences to form expectations about new situations. .
Would you please provide a short closing statement with your conclusions about our discussion and/or your reasons for ending the discussion?
My claim is it can’t be done other than via conjectures and refutations
Your claim was that it could not possibly work at all.
When I said that, I was using standard definitions that excluded C&R.
You haven’t provided a well-defined non-moving target for my criticism, as both CR and CF provide to you.
Yes I have: better than chance prediction.
"better than chance prediction" with the predictions done by what method? What is the math algorithm or flowchart? You're still not providing specifics.
I deny that humans can do induction. I also deny that simple organisms can do it.
Deny what it like, there's evidence they do it
This is jumping ahead. Humans do something. Whether or not its induction depends on what induction is, which is a current conversation topic.
Chatgpt: Induction, in a broad sense, means
I'm not going to debate Chatgpt, and this is unhelpful when I've already read many versions of induction and don't need an introductory summary. Is there no literature you can cite that you think writes down correct details of induction? The issue isn't my familiarity with induction, it's you picking a specific claim. Even if you're unsure and think one of many inductivist positions may be right, you could still pick a single one for us to discuss in more detail. I can't pick that for you but it needs to be picked for me to give more specific criticism.
What are your intentions with this discussion? I'd be open to trying to actually work through these issues and reach a conclusion. I'd be open to mutually agreeing to put in some effort. Right now, every time I reply, I don't know if you're ever going to reply again. I don't think we're going to resolve these issues quickly but I think the topics are important and I'm interested in trying seriously.
“better than chance prediction” with the predictions done by what method? What is the math algorithm or flowchart
Of course , there are multiple implementations , not a single essence. Eg. for next letter prediction:-
from collections import defaultdict, Counter
def train_ngram_model(text, n=2):
"""Trains a model based on a history of 'n' characters."""
ngram_map = defaultdict(list)
for i in range(len(text) - n):
context = text[i:i+n]
target = text[i+n]
ngram_map[context].append(target)
# Simplify map to only predict the highest frequency match
return {context: Counter(targets).most_common(1)[0][0] for context, targets in ngram_map.items()}
And you could have looked it up yourself, if you were interested in corroborating your theories.
I take it you aren't claiming this algorithm is how science or philosophy is done, so you aren't really answering my question. And you've now introduced the complication of claiming there are multiple implementations, which raises issues like needing a flowchart or meta-algorithm to decide which one to use when, which is not a standard part of induction.
And you could have looked it up yourself
Can you provide a source which argues for induction and gives this, which I could have found?
Epistemic learned helplessness should control here, even if it's inconvenient for rationalists trying to argue people into unusual beliefs.
What do you mean?
Here's my best guess, but this is low confidence. I presented a non-mainstream view of epistemology. You are not an expert on epistemology. So you will defer to people you see as experts on this topic without engaging with my arguments (like your link talks about using history as an example). If that's what you mean, that's fine, but I think this site is a reasonable place to find people to engage with about epistemology.
The point is that at least in most cases decisive arguments shouldn't work, and if said to rational people, won't work, because of epistemic learned helplessness.
Isn't your point about all arguments, not just decisive arguments? What does it have to do with my discussion of which types of arguments are logically and epistemically better than other types of arguments?
The idea is implicit that you should use decisive arguments because then people will have good reason to believe them.
Are you trying to say we should use worse forms of argument on purpose because of epistemic learned helplessness? I don't see how that would help and you haven't given any analysis about that. Epistemic learned helplessness is a separate issue from what I was talking about: when using arguments, which types are impersonally best, just looking at the subject matter and arguments themselves? I wasn't talking about human behavior or psychology.
I wasn’t talking about human behavior or psychology.
If you do not intend to get humans to believe the arguments, you are correct that epistemic learned helplessness doesn't apply. I do find this sort of odd, however.
I don't think you would reply like this if I wrote a post about how Bayesian arguments are better than frequentist arguments.
I don't think that such a post would imply "in order to get humans to believe them" anywhere near as much as this one did. Not every post implies the same things, after all!
My current guesses about what you mean by "decisive argument" and "indecisive argument": Every "indecisive argument" is not formally logically valid. Every "decisive argument" is formally logically valid. If something of this is incorrect, then please, let me know.
CF evaluates ideas by whether they succeed or fail at a purpose (achieve a goal, solve a problem, answer a question). Ideas are for something and can't be evaluated in isolation.
Ideas which are either false or true can be evaluated on a scale of plausibility. Do you think this may be useful at times? If yes, does this fit with "succeed/fail at a purpose" frame?
A decisive positive argument contradicts its target being false (it says the idea must succeed at its purpose). A decisive negative argument contradicts its target being true (it says the idea must fail at its purpose).
I'm not completely sure on how the target relates with the argument and the idea. I guess the target is not the conclusion. Because if I assume that the target is the conclusion, then a decisive negative argument contradicts its conclusion which can't be. I guess the target is not the idea. Because if I assume the target and the idea are the same, then all what a decisive positive argument is doing is contradicting the idea being false. But the idea being true doesn't necessarily mean the idea will succeed at its purpose. Here I assume "its purpose" is not something the idea inherently has, it's an artifact of someone's choice. The idea being false doesn't necessarily mean the idea will fail at its purpose. If the idea is a fiction and its purpose is "entertain" and/or "teach", then the idea being false doesn't preclude it from succeeding at its purpose. Did I get this correctly?
Indecisive negative arguments have the same basic flaw as indecisive positive arguments: you can accept the argument but still reject the argument's conclusion without contradicting yourself.
My intuition is that if one rejects the conclusion of the argument, then necessarily they do not accept the argument. Which means one can't accept the argument and reject the argument's conclusion. Did you mean by "accept the argument" something like "accept the premises and intermediate conclusions if any"?
An indecisive positive argument is "I'll go to McDonald's because their food tastes good". It's indecisive because the reasoning (McDonald's food tastes good) doesn't contradict "I won't go to McDonald's". Both can be true.
I don't think people use arguments like that when choosing if to go to a restaurant and to which restaurant, at least not in a literal sense. One may say McDonald's food tastes good as one of the steps in determining a subjective degree of goodness of a restaurant, or how it compares with others, or how it compares with not going at all, and then choosing the best option.
To make them decisive, clarify the purpose, ...
...
To convert to a decisive argument, you often need to clarify your purpose/goal.
"Clarify the purpose/goal" seems like the defining step which contains basically all of the complexity of making a decision. Once that is determined, then the decisive negative argument phase is just a trivial logical deduction.
How does CF deal with the following? One has determined intuitively that a certain restaurant is the best for them to go. Which is like this restaurant scores most on their subjective degree of goodness kind of scale. Then they apply CF. They either tailor their "clarify purpose/goal" step to make it produce such a goal which ensures the decisive negative argument phase excludes all restaurants besides the "best" one. This makes CF application in this case entirely irrelevant. Or they "clarify their purpose/goal" somehow but the decisive negative argument phase excludes the "best" restaurant. So if they comply with whichever the CF output happens to be, they will be upset for missing out on the "best" restaurant.
My current guesses about what you mean by "decisive argument" and "indecisive argument": Every "indecisive argument" is not formally logically valid. Every "decisive argument" is formally logically valid. If something of this is incorrect, then please, let me know.
That's not how I see it. Basically no arguments anyone uses for complex issues are formally logically valid. One way to view it is differentiating cases where you think a formal deductive argument would be theoretically possible and ones where you wouldn't. I'm trying to distinguish between e.g. these arguments, neither of which is formally valid:
Ideas which are either false or true can be evaluated on a scale of plausibility. Do you think this may be useful at times?
"Plausibility" here refers to hundreds of different things. Are any useful for anything? Yes. Should we choose between competing ideas based on evaluating plausibility as a quantity? No.
But the idea being true doesn't necessarily mean the idea will succeed at its purpose. If the idea is a fiction and its purpose is "entertain" and/or "teach", then the idea being false doesn't preclude it from succeeding at its purpose.
I claim: ideas can't be evaluated independent of any purpose or context. The purpose and context must be supplied explicitly, as background assumptions, or built into the idea. Truth is success at a purpose, not a different type of evaluation that can reach a different answer.
It's like how you can't evaluate an answer, and whether it's true, without knowing what the question is.
"2+2=4" is true for the purpose of obeying the laws of arithmetic but false as the answer to "What color is the sky?"
My intuition is that if one rejects the conclusion of the argument, then necessarily they do not accept the argument. Which means one can't accept the argument and reject the argument's conclusion. Did you mean by "accept the argument" something like "accept the premises and intermediate conclusions if any"?
I think this is just a terminology issue. For the conclusion "go to Stanford" we may make arguments like "Stanford has high prestige". We can accept that argument and also reject the conclusion without contradicting ourselves. You're welcome to think of the high prestige as a premise, but to answer your literal question: I didn't mean to use that terminology myself.
How does CF deal with the following? One has determined intuitively that a certain restaurant is the best for them to go. Which is like this restaurant scores most on their subjective degree of goodness kind of scale. Then they apply CF. They either tailor their "clarify purpose/goal" step to make it produce such a goal which ensures the decisive negative argument phase excludes all restaurants besides the "best" one. This makes CF application in this case entirely irrelevant. Or they "clarify their purpose/goal" somehow but the decisive negative argument phase excludes the "best" restaurant. So if they comply with whichever the CF output happens to be, they will be upset for missing out on the "best" restaurant.
Where does that intuition come from in the first place? This raises all the usual epistemology issues, about what sorts of processes can and can't create knowledge, correct errors or reach rational conclusions, to which I have answered CF and rejected alternatives.
What people do later, after already having an initial conclusion, is often superficial, regardless of the explicit methods used (CF, MCDM, induction, etc.) People often use one method to backwards rationalize from a conclusion that they reached with another method, though it's also possible to do useful review and catch errors.
Truth is success at a purpose, not a different type of evaluation that can reach a different answer.
...
"2+2=4" is true for the purpose of obeying the laws of arithmetic but false as the answer to "What color is the sky?"
Is "2+2=5" true for the purpose of NOT obeying the laws of arithmetic?
Is a fiction-idea true for the purpose of providing entertainment if it succeeds at this purpose?
Is "2+2=5" true for the purpose of NOT obeying the laws of arithmetic?
Yes. If the question is "What is a math equation which does not obey the laws of arithmetic?" then "2+2=5" is a true answer for that question.
Is a fiction-idea true for the purpose of providing entertainment if it succeeds at this purpose?
Yes. If the question is e.g. "What is an entertaining story?" then a fictional story can be a true answer to that.
PS I think this website is temporarily blocking your comments or something. It says you posted this a week ago and edited two days ago but I only received a notification today. I did check for notifications yesterday and had none. This happened for at least one of your other comments previously where me seeing it was significantly delayed. I don't know if you're waiting in a queue for moderators to approve your posts or what. If you want to talk much more it might work better to sort out the issue with the mods or join my forum.
Of all ideas I'd like to consider statements in particular. I guess according with your usage of "true/false" a statement can be true/false only for a purpose. Just saying a statement is true/false without assuming any purpose has no valid meaning. A statement is true/false for a purpose if and only if it succeeds/fails at that purpose. The {statement, purpose} pairing for the "true/false" evaluation can be any, at least in principle.
When you say you "accept/believe/think that" followed by a statement, like for example, "I think that it was raining yesterday.", do you always imply a specific purpose for which you think the statement is "true", i. e. the statement succeeds at this purpose? What is this purpose?
I don't know if you're waiting in a queue for moderators to approve your posts
That's exactly it. I guess this is standard for new users here and I'm a new user. My previous comment took especially long. I guess it was approved moments before you received the notification.
If you want to talk much more it might work better to sort out the issue with the mods or join my forum.
I don't think there is anything I can do to shorten the time before my comments get auto-approved. I just hope it won't take too long. If it does take too long, I don't mind joining your forum and talking there. Also I myself may take a few days or even weeks before I generate a followup I'd be satisfied with. I'm posting this comment roughly 6 days after I saw the corresponding comment of yours.
Edit: This comment was auto-approved.
When you say you "accept/believe/think that" followed by a statement, like for example, "I think that it was raining yesterday.", do you always imply a specific purpose for which you think the statement is "true", i. e. the statement succeeds at this purpose? What is this purpose?
I'm guessing the purpose is a factually accurate description of the weather which will be correctly understood by the person you're talking to. The description should meet some standard, generic truth criteria like: corresponds to reality, no logical errors, the statement should be based on evidence not a blind guess (even if it's true, if you didn't know that, you shouldn't have said it and hoped to get lucky), etc. It should also meet some generic communication criteria like being in a language that the other person knows.
Many statements have generic or obvious (in our culture) purposes that aren't very interesting, but sometimes a purpose is more important to consider explicitly. They could be spies talking in code, so it's not about the weather. Or sometimes people talk about the weather primarily for social reasons in which case factual correctness might not be important to them. Similar to people who say "I didn't get any sleep last night" but they actually mean they didn't get a lot of sleep.
Critical Fallibilism (CF) is a philosophy I developed which deals with rationality, knowledge, and critical thinking and discussion. It builds most on Karl Popper's Critical Rationalism, which says we learn (create knowledge, solve problems) by an evolutionary process of conjectures and refutations. Popper rejected positive arguments (justifications) and induction. He advocated fallibilism and error correction.
CF's most important idea involves distinguishing between decisive and indecisive arguments. CF evaluates ideas by whether they succeed or fail at a purpose (achieve a goal, solve a problem, answer a question). Ideas are for something and can't be evaluated in isolation. The same idea can succeed at one purpose and fail at another (and indeed all ideas do that). Negative arguments (criticisms) argue that one or more ideas fail at one or more purposes. A purpose could be avoiding dehydration, having true ideas, or both. Any group of purposes joined with "and" (or other logical operators like "or") is a purpose.
A decisive argument (or group of arguments) contradicts the negation of its conclusion, so both can't be true. A decisive positive argument contradicts its target being false (it says the idea must succeed at its purpose). A decisive negative argument contradicts its target being true (it says the idea must fail at its purpose). If you accept the argument, you shouldn't accept the thing it contradicts, because they're incompatible.
Decisive negative arguments are reasonably common and accessible. We can find and point out errors such as counter examples or flawed logic. One error is often enough to fail at a purpose.
Decisive positive arguments are either rare or entirely inaccessible. Pointing out 1000 good things isn't enough to prove an idea will succeed at its purpose. Despite all those merits, there could still be an error that causes failure. Basically, errors have logical priority over positive traits. No matter how many good traits an airplane has, a single mistake in the engine can cause a crash.
Karl Popper argued that negative arguments are better than positive arguments. While he made several good points, I think there's another issue that Popper missed. Decisive arguments are better than indecisive arguments, and most or all of our decisive arguments are negative. Why? Decisive positive arguments require 100% proof; otherwise you could accept the argument, and accept that its conclusion is false, without contradiction. I think fallibilism excludes 100% guarantees against error in math, logic and every field. Even if you disagree about how broad fallibility is, you could still accept CF for science, philosophy and most fields.
Criticism is easier than proof because proof requires addressing all possibilities. A single counter-example or error can be a decisive criticism without considering vast numbers of possibilities. Popper discussed this with examples like "all ravens are black", which is refutable with one contradictory observation, but still not proven with a million compatible observations.
All our arguments are fallible and can be reconsidered with new ideas and evidence. But if you accept an observation of a white raven and some background knowledge, that contradicts "all ravens are black". There's no comparable way to prove it, even fallibly.
You can make decisive positive arguments using universal premises (e.g., "All men are mortal.") which assert their own completeness. But that just moves the problem: how do you prove that premise since observing a million mortal men is inadequate?
Indecisive negative arguments have the same basic flaw as indecisive positive arguments: you can accept the argument but still reject the argument's conclusion without contradicting yourself. There's no logical connection between the argument and the conclusion. Logically, indecisive arguments don't do anything. Contradiction is a powerful, useful logical tool used with decisive arguments, but we don't have good alternative tools to use with indecisive arguments. Compatibility (non-contradiction) lacks logical power despite sometimes misleadingly being called "support" or "confirmation".
If I observe a purple hamster or red tree, that is compatible with "all ravens are black"; compatibility is basically worthless without also using another concept like relevance. But even with a million relevant, compatible observations – e.g., observations of black ravens – a white raven could still exist. Relevance is very difficult to define and evaluate, but even if we had perfect knowledge of what was relevant, compatibility plus relevance would still be inadequate to make indecisive arguments effective. Nothing about seeing a lot of black ravens actually means that there couldn't be a white one.
If indecisive arguments are logically flawed, then why do they often appear to work moderately well? Many of them are convertible to decisive arguments. They have a valid point which is presented imperfectly. Similarly, many positive arguments are convertible to negative arguments. I hypothesize that arguments which can't be converted to decisive, negative form are wrong. Skipping the conversion step is often reasonable in low-stakes, low-precision, friendly contexts.
How do you convert arguments? Positive arguments point out good trait(s). To convert to negative form, criticize alternatives for lacking the good trait(s). Indecisive arguments involve more creativity to convert. They point out good or bad things without logically connecting that to success or failure at a purpose. To make them decisive, clarify the purpose, figure out criteria for success and failure, and point out how the bad things (or missing good things for alternatives) cause failure.
Focusing on negative arguments can take some getting used to because it's more indirect. Instead of arguing in favor of an idea, you criticize alternatives to that idea. You also try to criticize the idea. The conclusion you reach is the idea you can't find any decisive error in, despite trying.
If you have multiple ideas that you think will work, you can use any of them, or you can aim for a more ambitious purpose/goal. CF has methods for narrowing it down to exactly one non-refuted idea, but that's often not worth the effort. If you have no ideas you think will work, you can brainstorm/research more, adjust your purpose/goal to be easier, or give up and do something else.
If you don't find an error with an idea, it could still be wrong. Why use it? Because ideas you don't know are wrong are preferable to ideas you do know are wrong (already found a decisive error in). It never makes sense to use an idea for a purpose if your best understanding is that it will fail at that purpose.
Why?
What is the motivation for CF? A reasonable first impression would be that CF doesn't sound strictly wrong (you don't see a reason it can't possibly work), but it sounds inconvenient or cumbersome. So what's the upside to make it worth pursuing?
CF's main motivation is logical arguments showing that various other approaches cannot possibly work. CF prioritizes correctness over everything else. (I actually think CF is reasonably convenient and elegant once you get used to it, and has various merits besides correctness, but I acknowledge there's an initial learning curve.)
Two main alternatives to CF are induction and weighted factors. Serious problems with these are actually well known in the academic literature and have been written about by advocates of these approaches, not just by opponents like Popper. I'm not actually saying anything very new by claiming these approaches are flawed. I think people try to use them despite the known errors because they've largely given up on finding alternatives, and errors seem somewhat ignorable to people accustomed to indecisive thinking. Some people are ignorant of the problems with their approaches, but I think many experts are instead pessimistic about finding something better. Ignorance of problems reduces motivation to consider alternatives. And experts who understand the problems may not want to spend time studying or debating a new system that they find initially counterintuitive and aren't optimistic about.
The various schools of inductive thought can be seen as attempts to say that indecisive arguments sometimes partially work. Compensatory weighted factor approaches (where a high score at one factor can compensate for low scores at other factors) also try to use multiple indecisive arguments to reach a conclusion. Non-compensatory multi-factor approaches are less common but exist and have some overlap with CF.
I've written criticisms of induction and weighted factors before. For this article, I'll just say that I'm open to discussion if someone disagrees but wants to try to reach a conclusion about the matter. In a discussion, I only have to address one person's concerns instead of all potential concerns, so it can be easier and more focused (and provide valuable feedback and potentially criticism for me, too). I can provide a free account on my forum if someone accepts this invitation (email me).
Examples
The idea "go to McDonald's for a burger" succeeds at the purpose "get lunch" but fails at the purposes "get a gluten-free lunch" and "learn knitting". We should evaluate {idea, purpose} pairs rather than ideas alone. Being more precise, we can include context and evaluate a triple: {idea, purpose, context}.
Note: In other articles I've said "goal" instead of "purpose" and abbreviated the triple to IGC, but they mean the same thing. It's {idea/solution/answer/plan/explanation/argument/reasoning/option/alternative, purpose/goal/question/problem/objective/criterion, context/problem-situation/background-knowledge/scenario} triples. The triple is meant to work with any of the terms separated by slashes; you can use whichever is most natural. Sometimes answer and question work well, but in other contexts solution and problem make more sense. {idea, purpose/goal, context} is particularly generic and works well when you want to keep terminology consistent. Plurals like "goals" or "criteria" may be used because the goal is often a group: the goal could consist of multiple conjoined sub-goals or be to meet multiple criteria. Interestingly, goals and contexts are types of ideas which can be put in the first spot of their own IGC triples. Similarly, arguments are used to evaluate IGCs but they are also part of their own IGCs: arguments have goals and contexts and an argument's IGC can itself be criticized.
A criticism can apply to multiple {idea, goal} pairs. If my goal is to get a gluten-free lunch, the criticism "buns have gluten" applies to McDonald's and also to other ideas like Burger King and Wendy's. The same criticism can also work for other goals, e.g., getting a gluten free breakfast or dinner. It's often important to think using principles and make your criticisms broad enough to cover whole categories of ideas and goals instead of just an individual idea-goal pair.
An indecisive positive argument is "I'll go to McDonald's because their food tastes good". It's indecisive because the reasoning (McDonald's food tastes good) doesn't contradict "I won't go to McDonald's". Both can be true. Also, alternatives like "I should go to Burger King" aren't contradicted even though they imply not going to McDonald's (in a context where you're picking one restaurant for a specific meal). Indecisive means there's no logical problem with accepting the reasoning but also accepting a contradictory conclusion.
To convert to a negative argument, figure out what positive trait is being praised (food taste) and criticize alternatives for lacking it. E.g., "I won't go to Burger King because their food tastes bad." After converting, always check if the result is true. If you actually like the taste of Burger King's food, then it's wrong. In that case, the original indecisive, positive argument was also wrong to conclude that you should go to McDonald's specifically (not Burger King) because of taste.
To convert to a decisive argument, you often need to clarify your purpose/goal. Let's say I'm hungry now and I want to get restaurant food within five minutes of walking. Then an indecisive argument is "I'll go to McDonald's because it's close." A decisive argument is "I won't go to Burger King because it's over a five-minute walk away." More generically, "I won't go to any restaurant that's over a five-minute walk away." can rule out the idea of going to Burger King, Wendy's and more with a single criticism. To make location decisive, we figure out what proximity constitutes success or failure and we criticize ideas that fail.
Next Steps
If CF interests you, a good place to start is by categorizing arguments you make, read or hear as decisive or indecisive and positive or negative. You can do that without changing what arguments you use or like; it can start as a research project for informational purposes. A later step could be trying to convert some arguments to be decisive and negative and paying attention to how some convert well but others don't.
A more advanced issue is considering whether enough decisive, negative arguments exist (including via conversion) to do all of our reasoning with them, or if we need to use some non-convertible positive and/or indecisive arguments. Many people like decisive, negative arguments and see them as highly powerful and useful, but believe they're too scarce to use exclusively. A related issue to ponder is: Can you ask a series of yes or no questions to explore any issue, or are other types of questions (or non-questions) absolutely necessary? Yes or no questions are good at exposing decisive issues. "Does it cost under $50?" invites a decisive answer while "How expensive is it?" or "How happy am I with the price?" don't.