Arguments Should Be Decisive Criticisms
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.
(Im going to make some references to Critical Rationalism, since I don’t know where the line is drawn between CR and CF).
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.
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.
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.
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.
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.
Then none are decisive!
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.
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 evidence...is evidence, not argument
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.
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.
The goal is basically logical relevance, not perfection.
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.
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.
My link discusses dimension conversion (like from apples to value) being problematic. That’s covered.
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).
Popper knew that and wrote about it.
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 don’t see how that connects to the ordinary meaning of “decisive”.
In fact passages like this
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.
Is this all about Hempel’s paradox?
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?
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 simplest pattern is “what will.happen before will happen again”. Simple organisms can implement that...ve”
...while.machine learning can implement more complex versions. https://en.wikipedia.org/wiki/Rule_induction
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.
You need to argue that it cannot work.”CF’s main motivation is logical arguments showing that various other approaches cannot possibly work”.
I don’t see the relevance.
I don’t see the alternative. If arguments aren’t infallible, you would need to count and weight them.
Problematic is far short of “couldn’t possibly work”.
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.
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.
It’s obvious that C.F. Is better than arguments that are irrelevant. Its not obviou s it’s better than weighting and induction.
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:
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:
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
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.
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
You cannot conclude it is certain, but you can conclude it is likely and.calculate a likeliihood.
Induction
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.
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).
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.
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
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
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.
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?”
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.
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?
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.
Which patterns are simplest? What’s the rule to judge that? Does applying the rule require intelligence as a prerequisite?
But you can’t expect any given context to supply you with a set of decisive criteria that narrow your options to one.
It uses an arbitrary threshold of decisiveness.
The examples you have given look.qualitative.
I don’t see how.
Epistemology quite possibly can’t be general purpose, in the sense that the same techniques apply to different kinds of problem.
I mean with subjective criteria.
They can do that. Asking how they do it doesn’t mean it’s impossible.
Different problems require different approaches. I’m not saying subjective weighting is the answer to everything
Non objective and made-up are not the same thing.
People do. I am not saying there is one method to rule them all.
If a thing is worth doing , it is worth doing with made up numbers.
Which is why it is difficult to show none of them could possibly work.
I have picked probabilistic prediction, which can be shown to work directly, without needing a theoretical justification.
You know the “aaaaa” pattern is simpler than the others. Its no great mystery.
People here like Kolmogorov complexity. That isn’t some unanswerable question.
You don’t need much intelligence to do simple induction, since simple organisms can do it.
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.
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”.
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.
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).
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.
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.
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.
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.
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.
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.
Yes I have: better than chance prediction.
That doesn’t mean C&R is the only possible mechanism.
I’m not a fan myself, but it’s not like no one has any clue about how simplicity works.
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?
When I said that, I was using standard definitions that excluded C&R.
“better than chance prediction” with the predictions done by what method? What is the math algorithm or flowchart? You’re still not providing specifics.
This is jumping ahead. Humans do something. Whether or not its induction depends on what induction is, which is a current conversation topic.
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.
Of course , there are multiple implementations , not a single essence. Eg. for next letter prediction:-
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.
Can you provide a source which argues for induction and gives this, which I could have found?
I’m certainly not claiming its how the whole of science works.
Which question?
How can anything be a standards part of induction, when induction doesn’t exist at all?
Anyway, I have shown that it exists, works and can be simple. You’re bringing in other issues now.
All you have to do is look up “inductive algorithm”. The sources aren’t going to argue for the existence of induction, for the same reason that a list of sorting algorithms won’t start with a philosophical argument that sorting exists.
Then what are you claiming? CR works universally, and induction works in limited cases where CR would also have worked? CR doesn’t work and a combination of induction and deduction is universal? Something else? I want to understand epistemology, intelligence and knowledge creation generally; I expected that you did too. What is your complete answer and what role does induction play in it?
I meant a standard part of claims/theories/ideas/models of/about induction.
I tried that and found different claims.
Don’t jump to the conclusion that you’ve won the debate when we’re in the middle. You haven’t yet defined induction (or given any source that does), so you haven’t established that the algorithm you brought up qualifies as induction. This is especially problematic when some of your claims about induction contradict standard claims about induction. You also haven’t established what the algorithm you gave allegedly works for and that it does work for that. You’ve acknowledged there are limits on what it works for (first section of this post) but haven’t yet detailed them.
Works at what ? And how do you tell?
So it exists and works? Remember, I never said it could do everything.
Nothing is proveably universal. All we have is a mishmash of imperfect methods.
Generally doesn’t mean there has to be one weird trick that solves it all, whether that C&R or Bayes.
I don’t have a complete answer, and it would take a long time to summarise my doubts.
Someone who claims to have a single complete answer to everything, that the mainstream experts have never heard of, is probably bullshitting … in the same way as the people who used to sell cure-all medicines.
That’s a selective demand for rigour. I’ve defined induction as well as you’ve defined C&R
Why are you quoting it like this and asking me? Did you misread this as an assertion by me?
Are you paying attention? Read the paragraph again.
So you overall position is that you think some unspecified form of induction may work for some limited parts of intelligence and knowledge creation, and unspecified other things work for other parts, and you don’t really know anything definitive and don’t claim to have answers? If that’s right, I don’t think saying basically “you didn’t 100% rule out all variations of induction in a way I understand” is the most productive way to engage with CF when you don’t want to make strong positive claims yourself. It’d be more productive to try to understand what CF claims or to point out a serious mistake in CF’s positive claims or to make a strong claim yourself that doesn’t fit the CF viewpoint, not to argue “it isn’t all 100% proven” when to the best of your knowledge no currently available position meets that standard.
There is more than one form of induction that works, and I have specified one of them exactly , by showing the code.
I wasnt claiming to have a complete account of knowledge creation.
If you are saying that induction works in a limited, partial way, you are conceding for point
I have no further interest in engaging in CF.
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.
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.
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?
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?
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 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.
“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.
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:
I should not go to Taco Bell because I don’t want Mexican food. (Decisive in many contexts. In our best but fallible understanding, we can see a contradiction here, even though we haven’t rigorously proved it.)
I should go to Taco Bell because tacos are yummy. (Indecisive because Taco Bell might be out of budget or too far away despite being yummy.)
“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.
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?”
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.
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.
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?
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.
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?
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.
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.
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.
Let’s use “standard-true” to refer to what people usually mean by “true” when they say “[Statement] is true”. I don’t have a robust description of what I think that meaning is but in many cases “[Statement] corresponds with reality” is a good description.
I think what you mean by “true” overlaps with standard-true but overall it is different. The standard-true meaning doesn’t have “for a purpose” clause. With that said, the standard-true can be framed as “true for a purpose of corresponding with reality” (read “corresponding with reality” as a stand-in for a robust criterion of standard-true which I don’t have). But it’s just a frame, it’s not a part of the meaning itself. And, unlike your meaning for “true”, the purpose of this frame is always the same. Saying “[Statement] is standard-true” never produces ambiguity in form of “Which purpose [statement] is standard-true for?”.
By “statement” I don’t mean just an utterance. I mean the final intended meaning communicated by the utterance. Sometimes the final intended meaning is communicated using another, intermediate, meaning which serves as a kind of utterance. This is what happens in your example about speaking in code. I’d also argue the same happens in your example about sleep if both participants understand that the recipient knows the speaker is exaggerating. You also mention cases when factual correctness is not important. If any of these examples were intended as reasons why you attach “for a purpose” clause for “true” in “[Statement] is true”, then I’m not seeing what makes these good reasons.
For future reference let’s label the purpose you describe here as “Generic” purpose.
There are purposes for which the argument and the negation of its conclusion both are true. What purposes do you mean the argument and the negation of its conclusion both can’t be true for?
Can this be rephrased as “If you accept the argument is true, you shouldn’t accept the thing it contradicts is true, because they’re incompatible.”? What purpose each of the “true”s is for?
Throughout the post you use the words “contradiction”, “contradicts”. I understand “contradiction” as a situation when there is an implication that a statement is standard-true and standard-false at the same time. Could you describe what you mean by “contradiction” in terms of “true/false” and specify the purpose for each “true/false”?
Whether “It’s a dog.” is a true statement depends on the context and the question asked. The same applies to every sentence – you can take any sentence and come up with a question and context that makes it the right answer and another question and context that makes it the wrong answer. My conception of truth is the standard one, perhaps explained better than some explanations, and doesn’t require rethinking what contradiction is. You haven’t even attempted to give a counter-example where you think my conclusion and the standard conclusion diverge on whether something is true, false or contradictory.
Things which depend on the context in “[Statement] is standard-true” are the final intended meaning of the statement and reality against which the standard-truthiness of the statement is evaluated against. The reality may be the actual reality or a hypothetical one. Standard-true and “true/correct answer to a question” overlap in some cases but they are different concepts. A statement can be standard-true and be a false answer to a question. A statement can be standard-false and be a true answer to a question. Standard-true is concerned with one and one question only: “Does the statement correspond with reality under consideration?” (the caveat from before applies). While for your “true” the question can be any.
2+2=5 is standard-false in the actual reality and it is standard-false in any hypothetical reality where arithmetic corresponds with that reality. Also it is a true answer to a question “What is a math equation which does NOT obey the laws of arithmetic?”. It is true for the purpose of NOT obeying the laws of arithmetic. It is also false for the purpose of obeying the laws of arithmetic. If to assume actual reality, then 2+2=5 is always standard-false while it is true for some purposes. Your “true/false” for a statement may change depending on the purpose while standard-true/standard-false does not have a purpose as an input in the first place.
If you’d like an example of a purpose for which the argument and the negation of its conclusion both are true:
Definition: List [A; B; C; …] question—a question which reads as follows “What is an example of a statement from the following list: A; B; C; …?”.
Definition: List [A; B; C; …] purpose—a purpose which reads as follows “be an example of a statement from the following list: [A; B; C; …]”.
Let A be an argument (which is a kind of a statement), B its conclusion. Both A and negation of B are true answers for the List[A, negation of B] question. Both A and negation of B succeed at List[A, negation of B] purpose. Therefore, A and negation of B are both true for this purpose. Which is the same as saying the argument and the negation of its conclusion both are true for this purpose.
I’d like to say that I’m using your language “true/false for a purpose” for the sake of the argument. Which is not to be taken as an endorsement of this language.
Consider the statement, A, “I know that is a dog because it’s cute.” In the context of evaluating whether A is contained in a particular list of statements, or not, A is not an argument – it’s just a literal string devoid of meaning. I check the list and conclude, B, “A is on the list”. The negation of that is “A is not on the list”. Both B and ~B can’t be true regardless of what statements are on the list.
If you want to evaluate “A is on the list” by whether the statement is itself on the list, not by whether A is on the list, then you’re defining a goal that ignores logic, so logical concepts like negation are undefined and inapplicable.
This is off topic from what my essay is about.
I can react to this but I don’t know if I should because of the following.
I disagree. Although I’m not saying this to insist on resolving the recent points.
Obviously, one of your goals must be for people to understand what your essay is about. Which, among other things, depends on what you mean by “true/false”.
You may say that what you mean by “true/false” is equivalent with standard-true/standard-false. I disagree. But regardless if I agree or disagree, it is still the case (dare I say: it is standard-true) that for your “true/false” there are purposes and any purpose is valid. I get that the purposes you imply may be deducible from the context. But they are not obvious to me. Besides that, in your essay you use words “contradiction”, “accept”, which in normal language are closely related with standard-true/standard-false which has one and only one purpose—“correspond with reality” (mind the caveat from before). But your essay suggests that “true/false” for statements may have any purpose. That makes me not sure what you mean by “contradiction”, “accept” in your essay also. All of this makes me not sure what you mean by your essay altogether.
What may help my understanding is you answering the questions I have asked you earlier which you didn’t answer yet. I’ll copy paste them below so you know what they are. I’ll remove accompanying claims I made before on the off-chance that you may categorize them as off topic:
What purposes do you mean the argument and the negation of its conclusion both can’t be true for?
Can this be rephrased as “If you accept the argument is true, you shouldn’t accept the thing it contradicts is true, because they’re incompatible.”? What purpose each of the “true”s is for?
Throughout the post you use the words “contradiction”, “contradicts”. Could you describe what you mean by “contradiction” in terms of “true/false” and specify the purpose for each “true/false”?
Logically. The context for contradiction and negation is logic. I’m using words and concepts in a normal way here. A basic, mainstream understanding of logic is all that’s needed here. I’m not saying anything new or unusual about contradiction. You’re over-complicating it.
Your reply seems to suggest that here
you mean by “true” something like what I labeled as “standard-true”. Something which is not a subject to your claim “Truth is success at a purpose”. Something such that 2+2=4 is true not for some purpose which can be selected at a whim, but instead it is just true. Something such that 2+2=5 is false and there is no slot where you can place just the right purpose and have 2+2=5 be true for that purpose. Is this what you mean by the word “true” (and implied “false”) in this quote?
As I said in my previous reply, the goal/purpose there is logical correctness.