I think you’re mistaking Bayesian epistemology with Bayesian mathematics. Of course, no one denies Bayes’ theorem. The question is: to what should it be applied? Bayesian epistemology holds that rationality consists in updating your beliefs in accordance with Bayes’ theorem. As this LW post puts it:
Core tenet 3: We can use the concept of probability to measure our subjective belief in something. Furthermore, we can apply the mathematical laws regarding probability to choosing between different beliefs. If we want our beliefs to be correct, we must do so.
Next, it’s not that “Bayesianism is the right approach in these fields,” (I’m not sure what that means) it’s that Bayesian methods are useful for some problems. But Bayesianism falls short when it comes to explaining how we actually create knowledge. (No amount of updating on evidence + Newtonian mechanics gives you relativity.)
Despite his popularity among scientists who get given one philosophy of science class.
Love the ad hominem attack.
If you deny that observations confirm scientific theories, then you would have no reason to believe scientific theories which are supported by observational evidence, such as that smoking causes lung cancer.
Smoking causes lung cancer is a hypothesis, smoking does not cause lung cancer is another. We then discriminate between the hypotheses based on evidence (we falsify incorrect hypotheses). We slowly develop more and more sophisticated explanatory theories of how smoking causes lung cancer, always seeking to falsify them. At any time, we are left with the best explanation of a given phenomenon. This is how falsification works. (I can’t comment on your claim about Popper’s beliefs—but I would be surprised if true. His books are filled with examples of scientific progress.)
If you deny the rationality of induction, then you must be sceptical about all scientific theories that purport to be confirmed by observational evidence.
Yes. Theories are not confirmed by evidence (there’s no number of white swans you can see which confirms that all swans are white. “Swans are white” is a hypothesis, which can be refuted by seeing a black swan), they are falsified by it. Evidence plays the role of discrimination, not confirmation.
Inductive sceptics must hold that if you jumped out of a tenth floor balcony, you would be just as likely to float upwards as fall downwards.
No—because we have explanatory theories telling us why we’ll fall downwards (general relativity). These theories are the only ones which have survived scrutiny, which is why we abide by them. Confirmationism, on the other hand, purports to explain phenomenon by appealing to previous evidence. “Why do we fall downwards? Because we fell downwards before”. The sun rising tomorrow morning does not confirm the hypothesis that the sun rises every day. We should not increase our confidence in the sun rising tomorrow because it rose yesterday. Instead, we have a theory about why and when the sun rises when it does (heliocentric model + axis-tilt theory).
Observing additional evidence in favour of the theory should not increase our “credence” in it. Finding confirming evidence of a theory is easy, as evidenced by astrology and ghost stories. The amount of confirmatory evidence for these theories is irrelevant, what matters is whether and by what they can be falsified. There are more accounts of people seeing UFOs than there are of people witnessing gamma ray bursts. According the confirmationism, we should thus increase our credence in the former, and have almost none in the existence of the latter.
If you haven’t read this piece on the failure of probabilistic induction to favour one generalization over another, I highly encourage you to do so.
Anyway, happy to continue this debate if you’d like, but that was my primer.
Thanks for the engagement!
I think you’re mistaking Bayesian epistemology with Bayesian mathematics. Of course, no one denies Bayes’ theorem. The question is: to what should it be applied? Bayesian epistemology holds that rationality consists in updating your beliefs in accordance with Bayes’ theorem. As this LW post puts it:
Next, it’s not that “Bayesianism is the right approach in these fields,” (I’m not sure what that means) it’s that Bayesian methods are useful for some problems. But Bayesianism falls short when it comes to explaining how we actually create knowledge. (No amount of updating on evidence + Newtonian mechanics gives you relativity.)
Love the ad hominem attack.
Smoking causes lung cancer is a hypothesis, smoking does not cause lung cancer is another. We then discriminate between the hypotheses based on evidence (we falsify incorrect hypotheses). We slowly develop more and more sophisticated explanatory theories of how smoking causes lung cancer, always seeking to falsify them. At any time, we are left with the best explanation of a given phenomenon. This is how falsification works. (I can’t comment on your claim about Popper’s beliefs—but I would be surprised if true. His books are filled with examples of scientific progress.)
Yes. Theories are not confirmed by evidence (there’s no number of white swans you can see which confirms that all swans are white. “Swans are white” is a hypothesis, which can be refuted by seeing a black swan), they are falsified by it. Evidence plays the role of discrimination, not confirmation.
No—because we have explanatory theories telling us why we’ll fall downwards (general relativity). These theories are the only ones which have survived scrutiny, which is why we abide by them. Confirmationism, on the other hand, purports to explain phenomenon by appealing to previous evidence. “Why do we fall downwards? Because we fell downwards before”. The sun rising tomorrow morning does not confirm the hypothesis that the sun rises every day. We should not increase our confidence in the sun rising tomorrow because it rose yesterday. Instead, we have a theory about why and when the sun rises when it does (heliocentric model + axis-tilt theory).
Observing additional evidence in favour of the theory should not increase our “credence” in it. Finding confirming evidence of a theory is easy, as evidenced by astrology and ghost stories. The amount of confirmatory evidence for these theories is irrelevant, what matters is whether and by what they can be falsified. There are more accounts of people seeing UFOs than there are of people witnessing gamma ray bursts. According the confirmationism, we should thus increase our credence in the former, and have almost none in the existence of the latter.
If you haven’t read this piece on the failure of probabilistic induction to favour one generalization over another, I highly encourage you to do so.
Anyway, happy to continue this debate if you’d like, but that was my primer.