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.
.
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()}
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:-