Dirk Meulenbelt
@dirk-meulenbelt·Joined Aug 2024·Ideas
#4285·Dirk MeulenbeltOP, 4 months agoCriticism 1: The Decomposition is Arbitrary
The objection: The entire theorem rests on splitting a hypothesis h into (h ∨ e) and (h ∨ ¬e) and then showing the second part gets negative support. But why split it that way?
Critics argue this is a choice, not a necessity. Define "the part that goes beyond the evidence" differently and you get different results.
This is the most common objection in the literature. Ellery Eells argued the key assumption has been "almost uniformly rejected," because the propositions generated by Popper and Miller's decomposition contain content from both the evidence and the hypothesis tangled together, so they don't cleanly capture "the part that goes beyond the evidence." (Eells, 1988, British Journal for the Philosophy of Science 39, 111–116 — PDF)
Chihara and Gillies proposed "a new condition on what constitutes 'the part of a hypothesis that goes beyond the evidence' that is incompatible with Popper and Miller's condition, "arguing this refutes the impossibility of inductive support. (Chihara & Gillies, Philosophical Studies 58, 1990 — PDF)
Placeholder Criticism: The Decomposition is NOT Arbitrary
Deutsch argues the decomposition is not arbitrary: it follows necessarily from the probability calculus itself. He and Leonardis have been working on a paper to make this clearer, noting that "Popper and Miller's two papers on this are very condensed and mathematical and use special terminology they created," which has made the result difficult for others to evaluate fairly. The difficulty of presentation has been mistaken for a flaw in the argument. (Joseph Walker Podcast, Ep. 139)
Deutsch never actually explains why the decomposition is necessary. Therefore this criticism is a placeholder and to be updated once someone finds out his reasoning.
#4280·Dirk MeulenbeltOP revised 4 months agoThe Popper-Miller Theorem
Bayesian epistemology says that knowledge works like this: you have a theory, you see evidence, and the evidence raises your confidence in the theory. That's how you learn. The math behind this is Bayes' theorem, a formula for updating probabilities when new information arrives.
In 1983, Karl Popper and David Miller published a paper in Nature titled "A proof of the impossibility of inductive probability" that used this exact math to prove something uncomfortable: the part of a theory that goes beyond the evidence never gets supported by that evidence. It actually gets negative support. In their words: "probabilistic support in the sense of the calculus of probability can never be inductive support." (Popper & Miller, 1983)
They expanded on this in a second paper: "although evidence may raise the probability of a hypothesis above the value it achieves on background knowledge alone, every such increase in probability has to be attributed entirely to the deductive connections that exist between the hypothesis and the evidence." (Popper & Miller, 1987)
Here's what that means concretely. Say your theory is "all swans are white." You see a white swan. Your overall confidence in the theory goes up. But Popper and Miller split the theory into two pieces:
- The deductive piece: "this particular swan I'm looking at is white." The evidence directly confirmed that.
- The inductive piece: "and all the other swans I haven't looked at are also white." This is the part that would actually represent learning something new about the world.
They proved mathematically that piece 2, the inductive piece, the part that matters always receives zero or negative support from the evidence. The only work the evidence ever does is confirm what it directly touched. It never reaches beyond itself.
The Math
What follows is a simplified sketch of the proof. For the full formal treatment, see the original paper.
Step 1: Define "support."
The support that evidence e gives to hypothesis h is defined as the change in probability:
s(h|e) = p(h|e) − p(h)
If this number is positive, the evidence raised the probability of the hypothesis. Bayesians call this "confirmation."
Step 2: Decompose the hypothesis.
Popper and Miller split h into two components:
The deductive component: (h ∨ e), meaning "h or e." This is the part of h that is logically connected to the evidence. If e is true, then (h ∨ e) is automatically true, so evidence trivially supports it.
The inductive component: (h ∨ ¬e), meaning "h or not-e." This is the part of h that goes beyond the evidence, the part that would still need to be true even if the
evidence hadn't occurred.The hypothesis h is logically equivalent to the conjunction of these two components: h ⟺ (h ∨ e) ∧ (h ∨ ¬e).
Step 3: Calculate the support for each component.
Using standard probability rules, the support for the deductive component is:
s(h ∨ e | e) = 1 − p(h ∨ e)
This is always ≥ 0, since p(h ∨ e) ≤ 1. The evidence always supports the deductive part. No surprise, the evidence is logically contained in it.
The support for the inductive component is:
s(h ∨ ¬e | e) = −(1 − p(e))(1 − p(h|e))
Both (1 − p(e)) and (1 − p(h|e)) are ≥ 0 (assuming we're not dealing with certainties), so their product is ≥ 0, and the negative sign means the whole expression is always ≤ 0.
Step 4: The result.
The total support decomposes as:
s(h|e) = s(h ∨ e | e) + s(h ∨ ¬e | e)
The first term (deductive) is always non-negative. The second term (inductive) is always non-positive. The evidence never positively supports the part of the hypothesis that goes beyond the evidence. Whatever "boost" h gets from e is entirely accounted for by the deductive connection between them. The inductive component, the part that would represent genuine learning about the unobserved, is always counter-supported.
Implication
The implication is devastating for Bayesian epistemology: the entire framework of "updating beliefs with evidence" is an illusion. The number goes up, but the going-up is entirely accounted for by deduction. There is no induction hiding inside Bayes' theorem. The Bayesians' own math proves it.
David Deutsch, who has been working with colleague Matjaž Leonardis on a more accessible presentation of the theorem (Deutsch on X/Twitter, 2020), puts it this way: "There's a deductive part of the theory whose credence goes up. But the instances never imply the theory. So you want to ask: “The part of the theory that's not implied logically by the evidence – why does our credence for that go up?” Well, unfortunately it goes down." (Joseph Walker Podcast, Ep. 139, "Against Bayesianism")
The Conjunction Problem
Deutsch also offers a separate, more intuitive argument: take quantum mechanics and general relativity, our two best physics theories. They contradict each other.
- T₁ = quantum mechanics
- T₂ = general relativity
Both are spectacularly successful. A Bayesian should assign high credence to each. But T₁ and T₂ contradict each other, and probability theory is absolute about contradictions:
p(T₁ ∧ T₂) = 0
Zero. The combined understanding that lets us build GPS satellites, which need both relativity for orbital corrections and quantum mechanics for atomic clocks is worth literally nothing under the probability calculus.
Meanwhile, the negation ¬T₁ ("quantum mechanics is false") tells you nothing about the world. It's the infinite set of every possible alternative, mostly nonsensical. Yet the probability calculus ranks it higher than the theory that lets us build lasers and transistors.
A framework that assigns zero value to our best knowledge is, Deutsch argues, not capturing what knowledge actually is. Instead: "What science really seeks to ‘maximise’ (or rather, create) is explanatory power." (Deutsch, "Simple refutation of the 'Bayesian' philosophy of science," 2014)
Criticism 1: The Decomposition is Arbitrary
The objection: The entire theorem rests on splitting a hypothesis h into (h ∨ e) and (h ∨ ¬e) and then showing the second part gets negative support. But why split it that way?
Critics argue this is a choice, not a necessity. Define "the part that goes beyond the evidence" differently and you get different results.
This is the most common objection in the literature. Ellery Eells argued the key assumption has been "almost uniformly rejected," because the propositions generated by Popper and Miller's decomposition contain content from both the evidence and the hypothesis tangled together, so they don't cleanly capture "the part that goes beyond the evidence." (Eells, 1988, British Journal for the Philosophy of Science 39, 111–116 — PDF)
Chihara and Gillies proposed "a new condition on what constitutes 'the part of a hypothesis that goes beyond the evidence' that is incompatible with Popper and Miller's condition, "arguing this refutes the impossibility of inductive support. (Chihara & Gillies, Philosophical Studies 58, 1990 — PDF)
I removed the implicit link, and I turned the Conjunction Problem into a subheader
The Popper-Miller Theorem
Bayesian epistemology says that knowledge works like this: you have a theory, you see evidence, and the evidence raises your confidence in the theory. That's how you learn. The math behind this is Bayes' theorem, a formula for updating probabilities when new information arrives.
In 1983, Karl Popper and David Miller published a paper in Nature titled "A proof of the impossibility of inductive probability" that used this exact math to prove something uncomfortable: the part of a theory that goes beyond the evidence never gets supported by that evidence. It actually gets negative support. In their words: "probabilistic support in the sense of the calculus of probability can never be inductive support." (Popper & Miller, 1983)
They expanded on this in a second paper: "although evidence may raise the probability of a hypothesis above the value it achieves on background knowledge alone, every such increase in probability has to be attributed entirely to the deductive connections that exist between the hypothesis and the evidence." (Popper & Miller, 1987)
Here's what that means concretely. Say your theory is "all swans are white." You see a white swan. Your overall confidence in the theory goes up. But Popper and Miller split the theory into two pieces:
- The deductive piece: "this particular swan I'm looking at is white." The evidence directly confirmed that.
- The inductive piece: "and all the other swans I haven't looked at are also white." This is the part that would actually represent learning something new about the world.
They proved mathematically that piece #2, the inductive piece, the part that matters always receives zero or negative support from the evidence. The only work the evidence ever does is confirm what it directly touched. It never reaches beyond itself.
The Math
What follows is a simplified sketch of the proof. For the full formal treatment, see the original paper.
Step 1: Define "support."
The support that evidence e gives to hypothesis h is defined as the change in probability:
s(h|e) = p(h|e) − p(h)
If this number is positive, the evidence raised the probability of the hypothesis. Bayesians call this "confirmation."
Step 2: Decompose the hypothesis.
Popper and Miller split h into two components:
The deductive component: (h ∨ e), meaning "h or e." This is the part of h that is logically connected to the evidence. If e is true, then (h ∨ e) is automatically true, so evidence trivially supports it.
The inductive component: (h ∨ ¬e), meaning "h or not-e." This is the part of h that goes beyond the evidence, the part that would still need to be true even if the
evidence hadn't occurred.
The hypothesis h is logically equivalent to the conjunction of these two components: h ⟺ (h ∨ e) ∧ (h ∨ ¬e).
Step 3: Calculate the support for each component.
Using standard probability rules, the support for the deductive component is:
s(h ∨ e | e) = 1 − p(h ∨ e)
This is always ≥ 0, since p(h ∨ e) ≤ 1. The evidence always supports the deductive part. No surprise, the evidence is logically contained in it.
The support for the inductive component is:
s(h ∨ ¬e | e) = −(1 − p(e))(1 − p(h|e))
Both (1 − p(e)) and (1 − p(h|e)) are ≥ 0 (assuming we're not dealing with certainties), so their product is ≥ 0, and the negative sign means the whole expression is always ≤ 0.
Step 4: The result.
The total support decomposes as:
s(h|e) = s(h ∨ e | e) + s(h ∨ ¬e | e)
The first term (deductive) is always non-negative. The second term (inductive) is always non-positive. The evidence never positively supports the part of the hypothesis that goes beyond the evidence. Whatever "boost" h gets from e is entirely accounted for by the deductive connection between them. The inductive component, the part that would represent genuine learning about the unobserved, is always counter-supported.
Implication
The implication is devastating for Bayesian epistemology: the entire framework of "updating beliefs with evidence" is an illusion. The number goes up, but the going-up is entirely accounted for by deduction. There is no induction hiding inside Bayes' theorem. The Bayesians' own math proves it.
David Deutsch, who has been working with colleague Matjaž Leonardis on a more accessible presentation of the theorem (Deutsch on X/Twitter, 2020), puts it this way: "There's a deductive part of the theory whose credence goes up. But the instances never imply the theory. So you want to ask: “The part of the theory that's not implied logically by the evidence – why does our credence for that go up?” Well, unfortunately it goes down." (Joseph Walker Podcast, Ep. 139, "Against Bayesianism")
The Conjunction Problem
Deutsch also offers a separate, more intuitive argument: take quantum mechanics and general relativity, our two best physics theories. They contradict each other.
- T₁ = quantum mechanics
- T₂ = general relativity
Both are spectacularly successful. A Bayesian should assign high credence to each. But T₁ and T₂ contradict each other, and probability theory is absolute about contradictions:
p(T₁ ∧ T₂) = 0
Zero. The combined understanding that lets us build GPS satellites, which need both relativity for orbital corrections and quantum mechanics for atomic clocks is worth literally nothing under the probability calculus.
Meanwhile, the negation ¬T₁ ("quantum mechanics is false") tells you nothing about the world. It's the infinite set of every possible alternative, mostly nonsensical. Yet the probability calculus ranks it higher than the theory that lets us build lasers and transistors.
A framework that assigns zero value to our best knowledge is, Deutsch argues, not capturing what knowledge actually is. Instead: "What science really seeks to ‘maximise’ (or rather, create) is explanatory power." (Deutsch, "Simple refutation of the 'Bayesian' philosophy of science," 2014)
The Popper-Miller Theorem
Bayesian epistemology says that knowledge works like this: you have a theory, you see evidence, and the evidence raises your confidence in the theory. That's how you learn. The math behind this is Bayes' theorem, a formula for updating probabilities when new information arrives.
In 1983, Karl Popper and David Miller published a paper in Nature titled "A proof of the impossibility of inductive probability" that used this exact math to prove something uncomfortable: the part of a theory that goes beyond the evidence never gets supported by that evidence. It actually gets negative support. In their words: "probabilistic support in the sense of the calculus of probability can never be inductive support." (Popper & Miller, 1983)
They expanded on this in a second paper: "although evidence may raise the probability of a hypothesis above the value it achieves on background knowledge alone, every such increase in probability has to be attributed entirely to the deductive connections that exist between the hypothesis and the evidence." (Popper & Miller, 1987)
Here's what that means concretely. Say your theory is "all swans are white." You see a white swan. Your overall confidence in the theory goes up. But Popper and Miller split the theory into two pieces:
- The deductive piece: "this particular swan I'm looking at is white." The evidence directly confirmed that.
- The inductive piece: "and all the other swans I haven't looked at are also white." This is the part that would actually represent learning something new about the world.
They proved mathematically that piece 2, the inductive piece, the part that matters always receives zero or negative support from the evidence. The only work the evidence ever does is confirm what it directly touched. It never reaches beyond itself.
The Math
What follows is a simplified sketch of the proof. For the full formal treatment, see the original paper.
Step 1: Define "support."
The support that evidence e gives to hypothesis h is defined as the change in probability:
s(h|e) = p(h|e) − p(h)
If this number is positive, the evidence raised the probability of the hypothesis. Bayesians call this "confirmation."
Step 2: Decompose the hypothesis.
Popper and Miller split h into two components:
The deductive component: (h ∨ e), meaning "h or e." This is the part of h that is logically connected to the evidence. If e is true, then (h ∨ e) is automatically true, so evidence trivially supports it.
The inductive component: (h ∨ ¬e), meaning "h or not-e." This is the part of h that goes beyond the evidence, the part that would still need to be true even if the
evidence hadn't occurred.
The hypothesis h is logically equivalent to the conjunction of these two components: h ⟺ (h ∨ e) ∧ (h ∨ ¬e).
Step 3: Calculate the support for each component.
Using standard probability rules, the support for the deductive component is:
s(h ∨ e | e) = 1 − p(h ∨ e)
This is always ≥ 0, since p(h ∨ e) ≤ 1. The evidence always supports the deductive part. No surprise, the evidence is logically contained in it.
The support for the inductive component is:
s(h ∨ ¬e | e) = −(1 − p(e))(1 − p(h|e))
Both (1 − p(e)) and (1 − p(h|e)) are ≥ 0 (assuming we're not dealing with certainties), so their product is ≥ 0, and the negative sign means the whole expression is always ≤ 0.
Step 4: The result.
The total support decomposes as:
s(h|e) = s(h ∨ e | e) + s(h ∨ ¬e | e)
The first term (deductive) is always non-negative. The second term (inductive) is always non-positive. The evidence never positively supports the part of the hypothesis that goes beyond the evidence. Whatever "boost" h gets from e is entirely accounted for by the deductive connection between them. The inductive component, the part that would represent genuine learning about the unobserved, is always counter-supported.
Implication
The implication is devastating for Bayesian epistemology: the entire framework of "updating beliefs with evidence" is an illusion. The number goes up, but the going-up is entirely accounted for by deduction. There is no induction hiding inside Bayes' theorem. The Bayesians' own math proves it.
David Deutsch, who has been working with colleague Matjaž Leonardis on a more accessible presentation of the theorem (Deutsch on X/Twitter, 2020), puts it this way: "There's a deductive part of the theory whose credence goes up. But the instances never imply the theory. So you want to ask: “The part of the theory that's not implied logically by the evidence – why does our credence for that go up?” Well, unfortunately it goes down." (Joseph Walker Podcast, Ep. 139, "Against Bayesianism")
The Conjunction Problem
Deutsch also offers a separate, more intuitive argument: take quantum mechanics and general relativity, our two best physics theories. They contradict each other.
- T₁ = quantum mechanics
- T₂ = general relativity
Both are spectacularly successful. A Bayesian should assign high credence to each. But T₁ and T₂ contradict each other, and probability theory is absolute about contradictions:
p(T₁ ∧ T₂) = 0
Zero. The combined understanding that lets us build GPS satellites, which need both relativity for orbital corrections and quantum mechanics for atomic clocks is worth literally nothing under the probability calculus.
Meanwhile, the negation ¬T₁ ("quantum mechanics is false") tells you nothing about the world. It's the infinite set of every possible alternative, mostly nonsensical. Yet the probability calculus ranks it higher than the theory that lets us build lasers and transistors.
A framework that assigns zero value to our best knowledge is, Deutsch argues, not capturing what knowledge actually is. Instead: "What science really seeks to ‘maximise’ (or rather, create) is explanatory power." (Deutsch, "Simple refutation of the 'Bayesian' philosophy of science," 2014)
The Popper-Miller Theorem
Bayesian epistemology says that knowledge works like this: you have a theory, you see evidence, and the evidence raises your confidence in the theory. That's how you learn. The math behind this is Bayes' theorem, a formula for updating probabilities when new information arrives.
In 1983, Karl Popper and David Miller published a paper in Nature titled "A proof of the impossibility of inductive probability" that used this exact math to prove something uncomfortable: the part of a theory that goes beyond the evidence never gets supported by that evidence. It actually gets negative support. In their words: "probabilistic support in the sense of the calculus of probability can never be inductive support." (Popper & Miller, 1983)
They expanded on this in a second paper: "although evidence may raise the probability of a hypothesis above the value it achieves on background knowledge alone, every such increase in probability has to be attributed entirely to the deductive connections that exist between the hypothesis and the evidence." (Popper & Miller, 1987)
Here's what that means concretely. Say your theory is "all swans are white." You see a white swan. Your overall confidence in the theory goes up. But Popper and Miller split the theory into two pieces:
- The deductive piece: "this particular swan I'm looking at is white." The evidence directly confirmed that.
- The inductive piece: "and all the other swans I haven't looked at are also white." This is the part that would actually represent learning something new about the world.
They proved mathematically that piece #2, the inductive piece, the part that matters always receives zero or negative support from the evidence. The only work the evidence ever does is confirm what it directly touched. It never reaches beyond itself.
The Math
What follows is a simplified sketch of the proof. For the full formal treatment, see the original paper.
Step 1: Define "support."
The support that evidence e gives to hypothesis h is defined as the change in probability:
s(h|e) = p(h|e) − p(h)
If this number is positive, the evidence raised the probability of the hypothesis. Bayesians call this "confirmation."
Step 2: Decompose the hypothesis.
Popper and Miller split h into two components:
The deductive component: (h ∨ e), meaning "h or e." This is the part of h that is logically connected to the evidence. If e is true, then (h ∨ e) is automatically true, so evidence trivially supports it.
The inductive component: (h ∨ ¬e), meaning "h or not-e." This is the part of h that goes beyond the evidence, the part that would still need to be true even if the
evidence hadn't occurred.
The hypothesis h is logically equivalent to the conjunction of these two components: h ⟺ (h ∨ e) ∧ (h ∨ ¬e).
Step 3: Calculate the support for each component.
Using standard probability rules, the support for the deductive component is:
s(h ∨ e | e) = 1 − p(h ∨ e)
This is always ≥ 0, since p(h ∨ e) ≤ 1. The evidence always supports the deductive part. No surprise, the evidence is logically contained in it.
The support for the inductive component is:
s(h ∨ ¬e | e) = −(1 − p(e))(1 − p(h|e))
Both (1 − p(e)) and (1 − p(h|e)) are ≥ 0 (assuming we're not dealing with certainties), so their product is ≥ 0, and the negative sign means the whole expression is always ≤ 0.
Step 4: The result.
The total support decomposes as:
s(h|e) = s(h ∨ e | e) + s(h ∨ ¬e | e)
The first term (deductive) is always non-negative. The second term (inductive) is always non-positive. The evidence never positively supports the part of the hypothesis that goes beyond the evidence. Whatever "boost" h gets from e is entirely accounted for by the deductive connection between them. The inductive component, the part that would represent genuine learning about the unobserved, is always counter-supported.
Implication
The implication is devastating for Bayesian epistemology: the entire framework of "updating beliefs with evidence" is an illusion. The number goes up, but the going-up is entirely accounted for by deduction. There is no induction hiding inside Bayes' theorem. The Bayesians' own math proves it.
David Deutsch, who has been working with colleague Matjaž Leonardis on a more accessible presentation of the theorem (Deutsch on X/Twitter, 2020), puts it this way: "There's a deductive part of the theory whose credence goes up. But the instances never imply the theory. So you want to ask: “The part of the theory that's not implied logically by the evidence – why does our credence for that go up?” Well, unfortunately it goes down." (Joseph Walker Podcast, Ep. 139, "Against Bayesianism")
The Conjunction Problem
Deutsch also offers a separate, more intuitive argument: take quantum mechanics and general relativity, our two best physics theories. They contradict each other.
- T₁ = quantum mechanics
- T₂ = general relativity
Both are spectacularly successful. A Bayesian should assign high credence to each. But T₁ and T₂ contradict each other, and probability theory is absolute about contradictions:
p(T₁ ∧ T₂) = 0
Zero. The combined understanding that lets us build GPS satellites, which need both relativity for orbital corrections and quantum mechanics for atomic clocks is worth literally nothing under the probability calculus.
Meanwhile, the negation ¬T₁ ("quantum mechanics is false") tells you nothing about the world. It's the infinite set of every possible alternative, mostly nonsensical. Yet the probability calculus ranks it higher than the theory that lets us build lasers and transistors.
A framework that assigns zero value to our best knowledge is, Deutsch argues, not capturing what knowledge actually is. Instead: "What science really seeks to ‘maximise’ (or rather, create) is explanatory power." (Deutsch, "Simple refutation of the 'Bayesian' philosophy of science," 2014)
#3795·Dennis HackethalOP, 5 months agoDuring a space, starting at around 15:00, @dirk-meulenbelt suggested that Veritula suffers from underspecification: it does not specify which kinds of criticisms users can submit. But there are lots, like Occam’s razor, hard to vary, lack of testability, etc.
Since I criticize Deutsch’s ‘hard to vary’ criterion for being underspecified, Veritula shouldn’t be underspecified either.
(Correct me if I misunderstood you here, @dirk-meulenbelt.)
Huh, no. I said you found a level where the epistemology is unproblematic to specify and turned that into Veritula. I said the opposite. You misunderstood me.
This is solved by actively doing some visible stuff you'd want to do anyway as an AGI researchers.
This is solved by actively doing some visible stuff you'd want to do anyway as an AGI researcher.
#3613·Tyler MillsOP, 6 months agoA hiatus would incur a relatively heavy cost: the cost of living + the opportunity cost of lost salary. Earning money as quickly as possible, as early as possible, is important for long-term financial success.
You could spend some time in a cheap country.
#3611·Tyler MillsOP, 6 months agoA hiatus would create a "resume gap," weakening hireability in the field. This is to be avoided, but only assuming working in the field is itself desirable, which may not be the case, here, unless better opportunities arise (roles allowing more contact with physics, math and design -- i.e. "engineering"!).
This is solved by actively doing some visible stuff you'd want to do anyway as an AGI researchers.
#2284·Erik Orrje, 8 months agoGuess: We can generalise economics further and let it be subsumed by epistemology.
When we choose to try to solve certain problems, we always make trade-offs from a place of scarcity. Likewise, our conjectures wouldn't evolve without the competition enabled by scarcity in our minds.
:+1:
#2261·Edwin de Wit, 9 months agoI still see epistemology as distinct, and I'll try to make my case for it. Epistemology explains how humans create explanatory knowledge — unlike biological evolution, which also produces knowledge, but not explanations. Explanatory knowledge is special because it allows us to understand the world. Deutsch even suggests that this kind of knowledge tends toward convergence — a unified theory of everything — implying a deep connection between reality and its capacity to be explained.
Economics, on the other hand, isn’t distinct in the same way. It deals with trade-offs and scarcity — principles already fundamental to biology. Life itself is about managing limited resources and the trade-offs that come with them. Evolution, in turn, discovered increasingly effective strategies for doing so — including cooperation, exchange, and other relationships between and across lifeforms that facilitate these trades.
You say that trade-offs and scarcity are fundamental to biology. I agree, and this implies economics as a more fundamental science than biology or evolution. It still applies in our computer models, where biological details may not.
#2261·Edwin de Wit, 9 months agoI still see epistemology as distinct, and I'll try to make my case for it. Epistemology explains how humans create explanatory knowledge — unlike biological evolution, which also produces knowledge, but not explanations. Explanatory knowledge is special because it allows us to understand the world. Deutsch even suggests that this kind of knowledge tends toward convergence — a unified theory of everything — implying a deep connection between reality and its capacity to be explained.
Economics, on the other hand, isn’t distinct in the same way. It deals with trade-offs and scarcity — principles already fundamental to biology. Life itself is about managing limited resources and the trade-offs that come with them. Evolution, in turn, discovered increasingly effective strategies for doing so — including cooperation, exchange, and other relationships between and across lifeforms that facilitate these trades.
Undestanding does not flow from explanatory knowledge the way you imply. I understand Dutch and English, but a lot of my understanding of it is inexplicit.
#2259·Edwin de Wit, 9 months agoYes, but that inhirent in biology (evolution) right? I see it as part of the evolutionary strand for this reason.
In that same vein, why couldn't we class biology (evolution) under epistemology?
#2090·Erik Orrje, 9 months agoYeah (3) is interesting. Constructor theory is the contender I can think of for a future fifth strand. Any other suggestions?
Economics as a fundamental study of trade-offs.
#2255·Erik Orrje, 9 months agoHaha not a programmer so understood maybe half of it, but I think I see what you mean. There'll always be inexplicit parts to every explanation. My concept of explanations is that there must be at least some explicit part for it to be called an explanation. That's why genes aren't explanations.
My point is rather that it's not so clean a line between explicit and inexplicit. You're a doctor, so imagine the steps being something like:
- Extensive description of patient's symptoms, test results, conclusion, etc, in English.
- Same as above but mostly made out of quick notes by attending doctors and nurses.
- Only a collection of test names and test results. Test results accompanied by Chinese.
- Just a collection of numbers coming out of tests, without saying which test.
Arguably all the information is always there, and can be read off, but with increasing difficulty, requiring you to learn another language, or do a series of deductions.
#2230·Dennis Hackethal, 9 months agoSince you’re a doctor, Erik, let me ask: is there a possibility Alzheimer’s could be explained in terms of bad software? Correct me if I’m wrong, but it seems like the prevailing view is limited to bad hardware.
Not a doctor. But it's not hard for me to imagine untainted memory but a script with an error such that it can't manage to look up the information.
#2030·Erik Orrje, 9 months agoCan't think of how it could be otherwise. Do you have any examples of inexplicit explanations?
Let's fuck with your intuitions a little bit:
Say "stop" when it's no longer an explanation:
Didactic chapter in plain English with examples and edge cases, distilled into a concise technical note with formal definitions, invariants, and pseudocode.
Literate program interleaving prose and code, or a heavily commented Python implementation with docstrings and tests.
The same code stripped of comments/tests and then minified or obfuscated (e.g., Python one‑liner, obfuscated C), up through esolangs and formalisms (Brainfuck, untyped lambda calculus with Church numerals, SKI combinators).
Operational specifications with minimal labels (Turing machine tables), then hand‑written assembly without labels and self‑modifying tricks, down to raw machine code bytes/hex and binary blobs with unknown ISA or entry point.
The same bits recast as DNA base mapping with unknown block codec, unknown compression, encrypted archives indistinguishable from noise, arbitrary bitstrings for unspecified UTMs, or physical media (flux/RF) without modulation specs.
#2153·Dennis Hackethal, 9 months agoThe rival theories and clashes sound like competition between genes – or more precisely, between the theories those genes embody.
Basically, genes contain guesses (in a non-subjective sense) for how to spread through the population at the expense of their rivals. Those guesses are met with selection pressure and competition.
Dirk approves of your comment.
#2151·Dennis Hackethal, 9 months agoA gene doesn’t have problems in any conscious sense, but it always faces the problem of how to spread through the population at the expense of its rivals.
Maybe that answers your question, Erik.
How could we integrate that vision with Popper's definition (paraphrased): a tension, inconsistency, or unmet explanatory demand that arises when a theory clashes with observations, background assumptions, or rival theories, thereby calling for conjectural solutions and critical tests.
I turned it into a criticism per Dennis' prompt
Sure, philosophers and pedants do. But typically people use the word "know" in situations well short of being absolutely sure.
Sure, philosophers and pedants do. But typically people use the word "know" in situations well short of being absolutely sure.
#1601·Dennis Hackethal, 11 months agoWe do in every single way in which we use the term "know".
Don’t people disagree about what ‘know’ means? As in, some think it means they’re justified in their belief, others think they have corrected a sufficient amount of errors, etc…
Sure, philosophers and pedants do. But typically people use the word "know" in situations well short of being absolutely sure.