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()}
And you could have looked it up yourself, if you were interested in corroborating your theories.
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.