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I think it helps to look at the mind as a probabilistic survival engine than some truth engine. If there appears to be a cow in a random field the odds are ext

I think it helps to look at the mind as a probabilistic survival engine than some truth engine. If there appears to be a cow in a random field the odds are extremely low that someone put a papier mache cow there. If there’s something that has 50 % chance of being a snake you panic and run because that’s a 50 % chance of dying. In the case of the authors bug yes the change he introduced had a good probability of being the cause. However he could have increased the probability by going back over commits and confirming that his exact commit introduced the bug. Now the probability goes even higher. But it could still be machine specific a cosmic ray or whatever but the odds are over whelmingly low. In practice causal reasoning also works in a probabilistic fashion. I have a simple model saying that if a planes engine is on then it’s flying. Its a single step probability and so it’s not very accurate in the real world. I do a bunch of experiments and say a plane is flying if the engine is on and air is going over it’s wings faster than a certain speed. Now we have two correlations connected by a causal model that works in many other cases. Hence the probability of it being correct rises. But at the same time we should never mistake direct correlation for causality. But in daily life it’s “good enough”.

TuringTest 8y ago
I like the term "probabilistic survival engine". It explains a lot of phenomena in social trends, religion, politics, and even the practice of science.
- elijahbear 8y ago
  The first time I heard to a brain referred to in this way it was in the scifi book Blindsight by Peter Watts. The book is pretty solid and deals with a lot interesting ideas from cognitive science.
sevensor 8y ago
I'm sad that I had to read so far down the thread to find this comment, which beautifully cuts right through the argument. The problem is the idea of absolute knowledge -- knowledge is probabilistic and contextual. If I'm driving by the field in a car, I may be duped by the cow, but my perception that I'm surrounded by pasture land is more than adequate to support the activity of driving. If on the other hand, I'm Farmer Jones and I'm searching my pasture for Susie the cow, well, I'm not going to be taken in by a statue of a cow for more than a split second anyway.
cicero 8y ago
I think what you're saying is correct when it comes to perception with our senses, the kind of knowledge we share with the other higher animals. However, there is a higher level of knowledge that is unique to humans, which is rational in nature and can come up with certain knowledge. We can know certain mathematical and logical truths with certainty, such as 2 + 2 = 4 and that A and not A cannot both be true. We may not get an accurate count of cows in the field because one is hidden from view, but we can know with certainty that if we have X cows in field A and Y cows in field B, we have a total of X + Y cows in the two fields.
- strgcmc 8y ago
  I agree with your main points, but I think it's worth pointing out that even things like 2 + 2 = 4 and X + Y cows in fields A and B, all rest on certain definitions and sets of deductions, many of which go back down to basic axioms which have to be assumed (e.g. for arithmetic, see Peano axioms as an example: https://en.wikipedia.org/wiki/Peano_axioms https://en.wikipedia.org/wiki/Peano_axioms). - We have a definition of what a cow is, and we know that cows are discrete/physical objects, and have relatively fixed locations (i.e. that they are not like an electron cloud with a probabilistic location). - We assume that fields A and B in your hypothetical have clear, non-overlapping boundaries. - We assume that we are working in a fairly normal universe with a fairly standard model of physics, and that due to the way time works in this universe, a cow cannot simultaneously be located in both fields A and B. - ... - (this could get really pedantic and go on forever) The point is, even the things "we can know with certainty", are only as certain as the framework of observations/deductions/axioms/etc. that they rest upon. Almost nothing is certain on its own, without any further layers of reasoning behind it.
irq-1 8y ago
Denying knowledge makes your arguments work, but knowledge does exist and is a real thing. Are you sitting on a chair? The answer isn't probabilistic, you KNOW the answer. What's your name? Have you ever been to the moon?
- n4r9 8y ago
  That's all well and good until the supposed "chair" falls apart and is revealed to be nothing but a book and some bamboo. I see OP's comment not as denying knowledge, but rather as clarifying what is meant when people speak to each other of knowledge.
dTal 8y ago
This is an excellent comment - I'm just disappointed you managed to say it all without the word "Bayesian". The base rate for paper mache cows in fields is very low - one is perfectly justified in assigning a decent probability that a field contains a cow, if one sees a cow-shaped object in it. If you are in a part of the world that has a lot of cows in fields, you will presumably assign an even higher probability. You might even say you're "sure" there's a cow in the field, and act as such for everyday purposes. But don't be fooled - you're not really sure. If someone offers to bet you ten dollars versus your life that there is a cow in the field, you'll likely not take the bet. It seems that the philosophers were grasping towards a definition of "know" that encapsulated the idea of assigning 100% probability to something, after incorporating all the evidence. From a Bayesian standpoint, this is impossible. You can never be 100% certain of anything. To "know" something in the sense of "a justified, true belief" is impossible, because a belief is never both 100% and justified. (Note that it is entirely possible to see the paper mache cow and conclude that it is likely only paper mache and that there is not a real cow in the field. Is this belief "justified"?)
- dTal 8y ago
  I've thought a bit more about it, and concluded that while the above is a neat answer, it doesn't explain the question, and [thewarrior]'s remarks were nearer the mark on that. So here goes. It's tempting to think of "knowledge" as some relationship between the mind and a single fact. But when we use the word "knowledge", what we actually mean is "an accurate world model" - a set of beliefs. This is the disconnect that Gettier cases are designed to expose - they construct scenarios where someone's mental model is inaccurate or incomplete, yet by sheer luck produce a single, cherry-picked correct prediction. We are uncomfortable calling these correct predictions "knowledge" because as soon as you start probing the rest of the mental model, it falls apart. Sure, they think there's a cow in the field, and there really is one. Ask them any more questions about the scenario though ("what color is the cow?") and they'll give wrong answers. From this perspective, "knowledge" as a "justified, true belief" is a perfectly coherent concept - the problem lies with the inadequacy of the word "justified" to describe the output of a complex decision procedure that incorporates many beliefs about the world, such that it could be expected to yield many other correct predictions in addition to the one in question, up to some arbitrary threshold. A thought experiment - suppose you tell the observer that the cow they see is made of paper mache. They no longer believe there is a cow in the field. Intuitively, has their knowledge increased or decreased?
briantakita 8y ago
An unforeseen issue with probabilistic assessments is the model of the probability space is probably inaccurate. For example, Nassim Taleb has an argument of IQ being a single-dimentional assessment to a multi-dimensional domain. I think it's more practical to have a possibility space (where unknown unknowns is a possibility). This removes the need to assess probabilities (which will probably be incorrect) while having being able to per-mutate through the list of possibilities. One can also do logical deductions, based on the possibility space, to assess possible strategies to explore/solve the issues at hand.

TuringTest 8y ago

I like the term "probabilistic survival engine". It explains a lot of phenomena in social trends, religion, politics, and even the practice of science.

elijahbear 8y ago
The first time I heard to a brain referred to in this way it was in the scifi book Blindsight by Peter Watts. The book is pretty solid and deals with a lot interesting ideas from cognitive science.

sevensor 8y ago

I'm sad that I had to read so far down the thread to find this comment, which beautifully cuts right through the argument. The problem is the idea of absolute knowledge -- knowledge is probabilistic and contextual. If I'm driving by the field in a car, I may be duped by the cow, but my perception that I'm surrounded by pasture land is more than adequate to support the activity of driving. If on the other hand, I'm Farmer Jones and I'm searching my pasture for Susie the cow, well, I'm not going to be taken in by a statue of a cow for more than a split second anyway.

cicero 8y ago

I think what you're saying is correct when it comes to perception with our senses, the kind of knowledge we share with the other higher animals. However, there is a higher level of knowledge that is unique to humans, which is rational in nature and can come up with certain knowledge. We can know certain mathematical and logical truths with certainty, such as 2 + 2 = 4 and that A and not A cannot both be true. We may not get an accurate count of cows in the field because one is hidden from view, but we can know with certainty that if we have X cows in field A and Y cows in field B, we have a total of X + Y cows in the two fields.

strgcmc 8y ago
I agree with your main points, but I think it's worth pointing out that even things like 2 + 2 = 4 and X + Y cows in fields A and B, all rest on certain definitions and sets of deductions, many of which go back down to basic axioms which have to be assumed (e.g. for arithmetic, see Peano axioms as an example: https://en.wikipedia.org/wiki/Peano_axioms https://en.wikipedia.org/wiki/Peano_axioms). - We have a definition of what a cow is, and we know that cows are discrete/physical objects, and have relatively fixed locations (i.e. that they are not like an electron cloud with a probabilistic location). - We assume that fields A and B in your hypothetical have clear, non-overlapping boundaries. - We assume that we are working in a fairly normal universe with a fairly standard model of physics, and that due to the way time works in this universe, a cow cannot simultaneously be located in both fields A and B. - ... - (this could get really pedantic and go on forever) The point is, even the things "we can know with certainty", are only as certain as the framework of observations/deductions/axioms/etc. that they rest upon. Almost nothing is certain on its own, without any further layers of reasoning behind it.

irq-1 8y ago

Denying knowledge makes your arguments work, but knowledge does exist and is a real thing. Are you sitting on a chair? The answer isn't probabilistic, you KNOW the answer. What's your name? Have you ever been to the moon?

n4r9 8y ago
That's all well and good until the supposed "chair" falls apart and is revealed to be nothing but a book and some bamboo. I see OP's comment not as denying knowledge, but rather as clarifying what is meant when people speak to each other of knowledge.

dTal 8y ago

This is an excellent comment - I'm just disappointed you managed to say it all without the word "Bayesian". The base rate for paper mache cows in fields is very low - one is perfectly justified in assigning a decent probability that a field contains a cow, if one sees a cow-shaped object in it. If you are in a part of the world that has a lot of cows in fields, you will presumably assign an even higher probability. You might even say you're "sure" there's a cow in the field, and act as such for everyday purposes. But don't be fooled - you're not really sure. If someone offers to bet you ten dollars versus your life that there is a cow in the field, you'll likely not take the bet. It seems that the philosophers were grasping towards a definition of "know" that encapsulated the idea of assigning 100% probability to something, after incorporating all the evidence. From a Bayesian standpoint, this is impossible. You can never be 100% certain of anything. To "know" something in the sense of "a justified, true belief" is impossible, because a belief is never both 100% and justified. (Note that it is entirely possible to see the paper mache cow and conclude that it is likely only paper mache and that there is not a real cow in the field. Is this belief "justified"?)

dTal 8y ago
I've thought a bit more about it, and concluded that while the above is a neat answer, it doesn't explain the question, and [thewarrior]'s remarks were nearer the mark on that. So here goes. It's tempting to think of "knowledge" as some relationship between the mind and a single fact. But when we use the word "knowledge", what we actually mean is "an accurate world model" - a set of beliefs. This is the disconnect that Gettier cases are designed to expose - they construct scenarios where someone's mental model is inaccurate or incomplete, yet by sheer luck produce a single, cherry-picked correct prediction. We are uncomfortable calling these correct predictions "knowledge" because as soon as you start probing the rest of the mental model, it falls apart. Sure, they think there's a cow in the field, and there really is one. Ask them any more questions about the scenario though ("what color is the cow?") and they'll give wrong answers. From this perspective, "knowledge" as a "justified, true belief" is a perfectly coherent concept - the problem lies with the inadequacy of the word "justified" to describe the output of a complex decision procedure that incorporates many beliefs about the world, such that it could be expected to yield many other correct predictions in addition to the one in question, up to some arbitrary threshold. A thought experiment - suppose you tell the observer that the cow they see is made of paper mache. They no longer believe there is a cow in the field. Intuitively, has their knowledge increased or decreased?

briantakita 8y ago

An unforeseen issue with probabilistic assessments is the model of the probability space is probably inaccurate. For example, Nassim Taleb has an argument of IQ being a single-dimentional assessment to a multi-dimensional domain. I think it's more practical to have a possibility space (where unknown unknowns is a possibility). This removes the need to assess probabilities (which will probably be incorrect) while having being able to per-mutate through the list of possibilities. One can also do logical deductions, based on the possibility space, to assess possible strategies to explore/solve the issues at hand.