I am going to write the same warning I have written to rationalist friends in relation to the Great Filter Hypothesis and almost everything on Overcoming Bias: BEWARE OF MODELS WITH NO CAUSAL COMPONENTS! I repeat: BEWARE NONCAUSAL MODELS!!! In fact, beware of nonconstructive mental models as well, while we’re at it! Beware classical logic, for it is nonconstructive! Beware noncausal statistics, for it is noncausal and nonconstructive! All these models, when they contain true information, and accurately move that information from belief to belief in strict accordance with the actual laws of statistical inference, still often fail at containing coherent propositions to which belief-values are being assigned, and at corresponding to the real world.
Ok, so the old definition of “knowledge” was “justified true belief”. Then it turned out that there were times when you could believe something true, but have the justification be mere coincidence. I could believe “Someone is coming to see me today” because I expect to see my adviser, but instead my girlfriend shows up. The statement as I believed it was correct, but for a completely different reason than I thought. So Alvin Goldman changed this to say, “knowledge is true belief caused by the truth of the proposition believed-in.” This makes philosophers very unhappy but Bayesian probability theorists very happy indeed.
Where do causal and noncausal statistical models come in here? Well, right here, actually: Bayesian inference is actually just a logic of plausible reasoning, which means it’s a way of moving belief around from one proposition to another, which just means that it works on any set of propositions for which there exists a mutually-consistent assignment of probabilities.
This means that quite often, even the best Bayesians (and frequentists as well) construct models (let’s switch to saying “map” and “territory”) which not only are not caused by reality, but don’t even contain enough causal machinery to describe how reality could have caused the statistical data.
This happens most often with propositions of the form “There exists X such that P(X)” or “X or Y” and so forth. These are the propositions where belief can be deduced without constructive proof: without being able to actually exhibit the object the proposition applies to. Unfortunately, if you can’t exhibit the object via constructive proof (note that constructive proofs are isomorphic to algorithms for actually generating the relevant objects), I’m fairly sure you cannot possess a proper description of the causal mechanisms producing the data you see. This means that not only might your hypotheses be wrong, your entire hypothesis space might be wrong, which could make your inferences Not Even Wrong, or merely confounded.
(I can’t provide mathematics showing any formal tie between causation/causal modeling and constructive proof, but I think this might be because I’m too much an amateur at the moment. My intuitions say that in a universe where incomputable things don’t generate results in real-time and things don’t happen for no reason at all, any data I see must come from a finitely-describable causal process, which means there must exist a constructive description of that process—even if classical logic could prove the existence of and proper value for the data without encoding that constructive decision!)
What can also happen, again particularly if you use classical logic, is that you perform sound inference over your propositions, but the propositions themselves are not conceptually coherent in terms of grounding themselves in causal explanations of real things.
So to use my former example of the Great Filter Hypothesis: sure, it makes predictions, sure, we can assign probabilities, sure, we can do updates. But nothing about the Great Filter Hypothesis is constructive or causal, nothing about it tells us what to expect the Filter to do or how it actually works. Which means it’s not actually telling us much at all, as far as I can say.
(In relation to Overcoming Bias, I’ve ranted on similarly about explaining all possible human behaviors in terms of signalling, status, wealth, and power. Paging /u/Quirinus_Quirrell… If they see a man flirting with a woman at a party, Quirrell and Hanson will seem to explain it in terms of signalling and status, while I will deftly and neatly predict that the man wants to have sex with the woman. Their explanation sounds until you try to read its source code, look at the causal machine working, and find that it dissolves into cloud around the edges. My explanation grounds itself in hormonal biology and previous observation of situations where similar things occurred.)
So Alvin Goldman changed this to say, “knowledge is true belief caused by the truth of the proposition believed-in.” This makes philosophers very unhappy but Bayesian probability theorists very happy indeed.
If I am insane and think I’m the Roman emperor Nero, and then reason “I know that according to the history books the emperor Nero is insane, and I am Nero, so I must be insane”, do I have knowledge that I am insane?
Note that this also messes up counterfactual accounts of knowledge as in “A is true and I believe A; but if A were not true then I would not believe A”. (If I were not insane, then I would not believe I am Nero, so I would not believe I am insane.)
We likely need some notion of “reliability” or “reliable processes” in an account of knowledge, like “A is true and I believe A and my belief in A arises through a reliable process”. Believing things through insanity is not a reliable process.
Gettier problems arise because processes that are usually reliable can become unreliable in some (rare) circumstances, but still (by even rarer chance) get the right answers.
The insanity example is not original to me (although I can’t seem to Google it up right now). Using reliable processes isn’t original, either, and if that actually worked, the Gettier Problem wouldn’t be a problem.
Interesting thought but surely the answer is no. If I take the word “knowledge” in this context to mean having a model that reasonably depicts reality in its contextually relevant features, then the same model of what the word “insane” in this specific instance depicts two very different albeit related brain patterns.
Simply put the brain pattern (wiring + process) that makes the person think they are Nero is a different though surely related physical object than the brain pattern that depicts what that person thinks “Nero being insane” might actually manifest like in terms of beliefs and behaviors. In light of the context we can say the person doesn’t have any knowledge about being insane, since that person’s knowledge does not include (or take seriously) the belief that depicts the presumably correct reality/model of that person not actually being Nero.
Put even simpler we use the same concept/word to model two related but fundamentally different things. Does that person have knowledge about being insane? It’s the tree and the sound problem, the word insane is describing two fundamentally different things yet wrongfully taken to mean the same. I’d claim any reasonable concept of the word insane results in you concluding that that person does not have knowledge about being insane in the sense that is contextually relevant in this scenario, while the person might have actually roughly true knowledge about how Nero might have been insane and how that manifested itself. But those are two different things and the latter is not the contextually relevant knowledge about insanity here.
I don’t think that explanation works. One of the standard examples of the Gettier problem is, as eli described, a case where you believe A, A is false, B is true, and the question is “do you have knowledge of (A OR B)”. The “caused by the truth of the proposition” definition is an attempt to get around this.
So your answer fails because it doesn’t actually matter that the word “insane” can mean two different things—A is “is insane like Nero”, B is “is insane in the sense of having a bad model”, and “A OR B” is just “is insane in either sense”. You can still ask if he knows he’s insane in either sense (that is, whether he knows “(A OR B)”, and in that case his belief in (A OR B) is caused by the truth of the proposition.
So to use my former example of the Great Filter Hypothesis: sure, it makes predictions, sure, we can assign probabilities, sure, we can do updates. But nothing about the Great Filter Hypothesis is constructive or causal, nothing about it tells us what to expect the Filter to do or how it actually works. Which means it’s not actually telling us much at all, as far as I can say.
Yes it is causal in the same sense that mathematics of physical laws are causal.
In relation to Overcoming Bias, I’ve ranted on similarly about explaining all possible human behaviors in terms of signalling, status, wealth, and power. Paging /u/Quirinus_Quirrell… If they see a man flirting with a woman at a party, Quirrell and Hanson will seem to explain it in terms of signalling and status, while I will deftly and neatly predict that the man wants to have sex with the woman.
You do realize the two explanations aren’t contradictory and are in fact mutually reinforcing? In particular, the man wants to have sex with here and is engaging in status signalling games to accomplish his goal. Also his reasons for wanting to have sex with her may also include signaling and status.
So to use my former example of the Great Filter Hypothesis: sure, it makes predictions, sure, we can assign probabilities, sure, we can do updates. But nothing about the Great Filter Hypothesis is constructive or causal, nothing about it tells us what to expect the Filter to do or how it actually works. Which means it’s not actually telling us much at all, as far as I can say.
?
If the Filter is real, then its effects are what causes us to think of it as a hypothesis. That makes it “true belief caused by the truth of the proposition believed-in”, conditional on it actually being true.
If the Filter is real, then its effects are what causes us to think of it as a hypothesis.
That could only be true if it lay in our past, or in the past of the other Big Finite Number of other species in the galaxy it already killed off. The actual outcome we see is just an absence of Anyone Else detectable to our instruments so far, despite a relative abundance of seemingly life-capable planets. We don’t see particular signs of any particular causal mechanism acting as a Great Filter, like a homogenizing swarm expanding across the sky because some earlier species built a UFAI or something.
When we don’t see signs of any particular causal mechanism, but we’re still not seeing what we expect to see, I personally would say the first and best explanation is that we are ignorant, not that some mysterious mechanism destroys things we otherwise expect to see.
Hm? Why doesn’t Rare Earth solve this problem? We don’t have the tech yet to examine the surfaces of exoplanets so for all we know the foreign-Earth candidates we’ve got now will end up being just as inhospitable as the rest of them. “Seemingly life capable” isn’t a very high bar at the minute.
Now, if we did have the tech, and saw a bunch of lifeless planets that as far as we know had nearly exactly the same conditions as pre-Life Earth, and people started rattling off increasingly implausible and special-pleading reasons why (“no planet yet found has the same selenium-tungsten ratio as Earth!”), then there’d be a problem.
I don’t see why you need to posit exotic scenarios when the mundane will do.
I don’t see why you need to posit exotic scenarios when the mundane will do.
Neither do I, hence my current low credence in a Great Filter and my currently high credence for, “We’re just far from the mean; sometimes that does happen, especially in distributions with high variance, and we don’t know the variance right now.”
Well I agree with you on all of that. How is it non-causal?
Or have I misunderstood and you only object to the “aliens had FOOM AI go wrong” explanations but have no trouble with the “earth is just weird” explanation?
It isn’t. The people who affirmatively believe in the Great Filter being a real thing rather than part of their ignorance are, in my view, the ones who believe in a noncausal model.
The problem with the signaling hypothesis is that in everyday life there is essentially no observation you could possibly make that could disprove it. What is that? This guy is not actually signaling right now? No way, he’s really just signaling that he is so über-cool that he doesn’t even need to signal to anyone. Wait there’s not even anyone else in the room? Well through this behavior he is signaling to himself how cool he is to make him believe it even more.
Guess the only way to find out is if we can actually identify “the signaling circuit” and make functional brain scans. I would actually expect signaling to explain an obscene amount of human behavior… but really everything? As I said I can’t think of any possible observation outside of functional brain scans we could potentially make that could have the potential to disprove the signaling hypothesis of human behavior. (A brain scan where we actually know what we are looking at and where we are measuring the right construct obviously).
Thanks for pushing this. I nodded along to the grandparent post and then when I came to your reply I realized I had no idea what this part was talking about.
Can you explain this part more?
With pleasure!
Ok, so the old definition of “knowledge” was “justified true belief”. Then it turned out that there were times when you could believe something true, but have the justification be mere coincidence. I could believe “Someone is coming to see me today” because I expect to see my adviser, but instead my girlfriend shows up. The statement as I believed it was correct, but for a completely different reason than I thought. So Alvin Goldman changed this to say, “knowledge is true belief caused by the truth of the proposition believed-in.” This makes philosophers very unhappy but Bayesian probability theorists very happy indeed.
Where do causal and noncausal statistical models come in here? Well, right here, actually: Bayesian inference is actually just a logic of plausible reasoning, which means it’s a way of moving belief around from one proposition to another, which just means that it works on any set of propositions for which there exists a mutually-consistent assignment of probabilities.
This means that quite often, even the best Bayesians (and frequentists as well) construct models (let’s switch to saying “map” and “territory”) which not only are not caused by reality, but don’t even contain enough causal machinery to describe how reality could have caused the statistical data.
This happens most often with propositions of the form “There exists X such that P(X)” or “X or Y” and so forth. These are the propositions where belief can be deduced without constructive proof: without being able to actually exhibit the object the proposition applies to. Unfortunately, if you can’t exhibit the object via constructive proof (note that constructive proofs are isomorphic to algorithms for actually generating the relevant objects), I’m fairly sure you cannot possess a proper description of the causal mechanisms producing the data you see. This means that not only might your hypotheses be wrong, your entire hypothesis space might be wrong, which could make your inferences Not Even Wrong, or merely confounded.
(I can’t provide mathematics showing any formal tie between causation/causal modeling and constructive proof, but I think this might be because I’m too much an amateur at the moment. My intuitions say that in a universe where incomputable things don’t generate results in real-time and things don’t happen for no reason at all, any data I see must come from a finitely-describable causal process, which means there must exist a constructive description of that process—even if classical logic could prove the existence of and proper value for the data without encoding that constructive decision!)
What can also happen, again particularly if you use classical logic, is that you perform sound inference over your propositions, but the propositions themselves are not conceptually coherent in terms of grounding themselves in causal explanations of real things.
So to use my former example of the Great Filter Hypothesis: sure, it makes predictions, sure, we can assign probabilities, sure, we can do updates. But nothing about the Great Filter Hypothesis is constructive or causal, nothing about it tells us what to expect the Filter to do or how it actually works. Which means it’s not actually telling us much at all, as far as I can say.
(In relation to Overcoming Bias, I’ve ranted on similarly about explaining all possible human behaviors in terms of signalling, status, wealth, and power. Paging /u/Quirinus_Quirrell… If they see a man flirting with a woman at a party, Quirrell and Hanson will seem to explain it in terms of signalling and status, while I will deftly and neatly predict that the man wants to have sex with the woman. Their explanation sounds until you try to read its source code, look at the causal machine working, and find that it dissolves into cloud around the edges. My explanation grounds itself in hormonal biology and previous observation of situations where similar things occurred.)
If I am insane and think I’m the Roman emperor Nero, and then reason “I know that according to the history books the emperor Nero is insane, and I am Nero, so I must be insane”, do I have knowledge that I am insane?
Note that this also messes up counterfactual accounts of knowledge as in “A is true and I believe A; but if A were not true then I would not believe A”. (If I were not insane, then I would not believe I am Nero, so I would not believe I am insane.)
We likely need some notion of “reliability” or “reliable processes” in an account of knowledge, like “A is true and I believe A and my belief in A arises through a reliable process”. Believing things through insanity is not a reliable process.
Gettier problems arise because processes that are usually reliable can become unreliable in some (rare) circumstances, but still (by even rarer chance) get the right answers.
The insanity example is not original to me (although I can’t seem to Google it up right now). Using reliable processes isn’t original, either, and if that actually worked, the Gettier Problem wouldn’t be a problem.
Interesting thought but surely the answer is no. If I take the word “knowledge” in this context to mean having a model that reasonably depicts reality in its contextually relevant features, then the same model of what the word “insane” in this specific instance depicts two very different albeit related brain patterns.
Simply put the brain pattern (wiring + process) that makes the person think they are Nero is a different though surely related physical object than the brain pattern that depicts what that person thinks “Nero being insane” might actually manifest like in terms of beliefs and behaviors. In light of the context we can say the person doesn’t have any knowledge about being insane, since that person’s knowledge does not include (or take seriously) the belief that depicts the presumably correct reality/model of that person not actually being Nero.
Put even simpler we use the same concept/word to model two related but fundamentally different things. Does that person have knowledge about being insane? It’s the tree and the sound problem, the word insane is describing two fundamentally different things yet wrongfully taken to mean the same. I’d claim any reasonable concept of the word insane results in you concluding that that person does not have knowledge about being insane in the sense that is contextually relevant in this scenario, while the person might have actually roughly true knowledge about how Nero might have been insane and how that manifested itself. But those are two different things and the latter is not the contextually relevant knowledge about insanity here.
I don’t think that explanation works. One of the standard examples of the Gettier problem is, as eli described, a case where you believe A, A is false, B is true, and the question is “do you have knowledge of (A OR B)”. The “caused by the truth of the proposition” definition is an attempt to get around this.
So your answer fails because it doesn’t actually matter that the word “insane” can mean two different things—A is “is insane like Nero”, B is “is insane in the sense of having a bad model”, and “A OR B” is just “is insane in either sense”. You can still ask if he knows he’s insane in either sense (that is, whether he knows “(A OR B)”, and in that case his belief in (A OR B) is caused by the truth of the proposition.
Yes it is causal in the same sense that mathematics of physical laws are causal.
You do realize the two explanations aren’t contradictory and are in fact mutually reinforcing? In particular, the man wants to have sex with here and is engaging in status signalling games to accomplish his goal. Also his reasons for wanting to have sex with her may also include signaling and status.
?
If the Filter is real, then its effects are what causes us to think of it as a hypothesis. That makes it “true belief caused by the truth of the proposition believed-in”, conditional on it actually being true.
I don’t get it.
That could only be true if it lay in our past, or in the past of the other Big Finite Number of other species in the galaxy it already killed off. The actual outcome we see is just an absence of Anyone Else detectable to our instruments so far, despite a relative abundance of seemingly life-capable planets. We don’t see particular signs of any particular causal mechanism acting as a Great Filter, like a homogenizing swarm expanding across the sky because some earlier species built a UFAI or something.
When we don’t see signs of any particular causal mechanism, but we’re still not seeing what we expect to see, I personally would say the first and best explanation is that we are ignorant, not that some mysterious mechanism destroys things we otherwise expect to see.
Hm? Why doesn’t Rare Earth solve this problem? We don’t have the tech yet to examine the surfaces of exoplanets so for all we know the foreign-Earth candidates we’ve got now will end up being just as inhospitable as the rest of them. “Seemingly life capable” isn’t a very high bar at the minute.
Now, if we did have the tech, and saw a bunch of lifeless planets that as far as we know had nearly exactly the same conditions as pre-Life Earth, and people started rattling off increasingly implausible and special-pleading reasons why (“no planet yet found has the same selenium-tungsten ratio as Earth!”), then there’d be a problem.
I don’t see why you need to posit exotic scenarios when the mundane will do.
Neither do I, hence my current low credence in a Great Filter and my currently high credence for, “We’re just far from the mean; sometimes that does happen, especially in distributions with high variance, and we don’t know the variance right now.”
Well I agree with you on all of that. How is it non-causal?
Or have I misunderstood and you only object to the “aliens had FOOM AI go wrong” explanations but have no trouble with the “earth is just weird” explanation?
It isn’t. The people who affirmatively believe in the Great Filter being a real thing rather than part of their ignorance are, in my view, the ones who believe in a noncausal model.
The problem with the signaling hypothesis is that in everyday life there is essentially no observation you could possibly make that could disprove it. What is that? This guy is not actually signaling right now? No way, he’s really just signaling that he is so über-cool that he doesn’t even need to signal to anyone. Wait there’s not even anyone else in the room? Well through this behavior he is signaling to himself how cool he is to make him believe it even more.
Guess the only way to find out is if we can actually identify “the signaling circuit” and make functional brain scans. I would actually expect signaling to explain an obscene amount of human behavior… but really everything? As I said I can’t think of any possible observation outside of functional brain scans we could potentially make that could have the potential to disprove the signaling hypothesis of human behavior. (A brain scan where we actually know what we are looking at and where we are measuring the right construct obviously).
Thanks for pushing this. I nodded along to the grandparent post and then when I came to your reply I realized I had no idea what this part was talking about.