12 ms·

Against Bayesianism – David Deutsch

121 points by jger15 5y ago

jessenichols 5y ago
For the clearest and most precise view of Deutsch's philosophy of science in the context of quantum theory, read his paper The logic of experimental tests, particularly of Everettian quantum theory: https://www.sciencedirect.com/science/article/pii/S135521981530023X https://www.sciencedirect.com/science/article/pii/S135521981... There's a breakdown of the paper here: https://www.youtube.com/watch?v=DHLTBOJ5hoQ&t=42s https://www.youtube.com/watch?v=DHLTBOJ5hoQ&t=42s A quote mentioning Bayesian Epistemology: Prevailing discussions (e.g. Dawid & Thébault, 2014; Greaves & Myrvold, 2010) of the testability of various versions of quantum theory have approached the matter indirectly, in terms of support or confirmation – asking how our credence (degree of belief) for a theory should be changed by experiencing results of experiments. However, experimental confirmation is a philosophically contentious concept. Notably, it is rejected root and branch by Popper (1959). I shall present an account of the nature and methodology of scientific testing that closely follows Popper׳s. It differs from his, if at all,3 by regarding fundamental science as exclusively explanatory. That is to say, I take a scientific theory to be a conjectured explanation4 (explanatory theory) of some aspects of the physical world – the explicanda of the theory – that is testable (I shall elaborate what that means below) by observation and experiment. A scientific explanation is a statement of what is there in reality, and how it behaves and how that accounts for the explicanda. Neither confirmation nor credence nor ‘inductive reasoning’ (from observations to theories or to justifications of theories as true or probable) appear in this account. So in this view the problem described in Section 1 is about testing theories. This contradicts the ‘Bayesian’ philosophy that rational credences obey the probability calculus and that science is a process of finding theories with high rational credences, given the observations. It also contradicts, for instance, instrumentalism and positivism, which identify a scientific theory with its predictions of the results of experiments, not with its explanations. My argument here, that Everettian quantum theory is testable, depends on regarding it as an explanatory theory, and on adopting an improved notion of experimental testing that takes account of that. Scientific methodology, in this conception, is not about anyone׳s beliefs or disbeliefs. Rather, it assumes that someone has conjectured explanatory theories (though it says nothing about how to do that), and it requires those who know (i.e. are aware of) those theories and want to improve them, to attempt to locate specific flaws and deficiencies and to attempt to correct those by conjecturing new theories or modifications to existing theories. Explicanda in the sciences usually involve appearances of some sort (e.g. the perceived blueness of the sky). Theoretical matters can also be explicanda (e.g. that classical gravity and electrostatics both have an inverse-square force law), but those will not concern us here. Explanations of appearances typically account for them in terms of an unperceived, underlying reality (e.g. differential scattering of photons of different energies) that brings about those appearances (though not only them).
- im3w1l 5y ago
  I believe you can actually get Popperian falsification out of Bayesianism if you squint right. Consider an falsification experiment. It disproves some theories while not changing our relative beliefs in other theories. this would be basically for all i in S1 p(evidence | theory i) = 0 for all i in S2 p(evidence | theory i) = k * p(evidence) I would say that most scientific evidence is of this sort, except that the probabilities for the "falsified" theories can also be a little bit above 0 to account for measurement error. Edit: Actually it may be fruitful to introduce a distinction similar to the one probability theory has... In probability theory there is a difference between sample and event. An event is a set of samples. In our case, I believe we want a distinction between a theory... and a lets call it micro-theory. To pick a funny and memorable example a theory could be something like "there is a Loch Ness monster". Now a micro-theory could be something like "there is a Loch Ness monster, that is invisble, and makes no sounds, but it can be detected by radar... and... and...". So it would include all these additional constraints. So the theory it's composed of a lot of these micro theories right. Now if we take photos of every inch of Loch Ness, and we don't find any Loch Ness monster, we make it less likely that there is a monster right. We may say "oh we were careless, and just missed it" but if we keep looking eventually that becomes an impossibility. So we disprove a bunch of micro theories, but some will remain. Our previous micro theory that among other thing says that the monster is invisible remains. And whats worse the relative likelyhood of them is unchanged compared to the no monster. p(cant get monster on photo | no monster) = k p (cant get monster on photo) and p(cant get monster on photo | invisible monster) = k p (cant get monster on photo) Now if we want to further increase our belief in "no monster" we would have to go after these wacky micro-theories and disprove them, using e.g. radar. But given that a sensible person assigns those micro-theories low prior likelyhood we may be satisfied with the situation and not bother. So basically these was just Popperianism in Bayesianism clothing right? Almost... Notice the very last point. We allowed ourselves not to bother with theories of invisible monsters. Because of our prior likelyhood.
mgh2 5y ago
- mnl 5y ago
  So that's the best appraisal you can give of David Deutsch: "Just an old man seeking attention"? Well, leaving aside the usual embarrassment I feel when it comes to the impromptu nonsense a fair share of HN commentators think it's worthwhile to contribute here when there's a piece of news involving physics or physicists, that's an ageist take without any content whatsoever. There's more old people who know what they're talking about than young people. That's just the obvious consequence of having been around reading and thinking about stuff more time. You'll notice it eventually because as the song goes, time waits for no one.
  - mgh2 5y ago
    Fair enough, my initial comment was "just a man", reversed. Give it a try, perhaps you are more patient. Today's oversaturated information makes listeners harsh critics.
    - mnl 5y ago
      Yep, we're getting too impatient and that's not helpful when it comes to think deeply about what we've been taught. Yet that's the most important part of any job IMO. I'll check it out, maybe you're right and he's rambling. That would be surprising to me, which is the reason I replied to your post.
      - throwanem 5y ago
        I don't think he's rambling. He isn't sure what familiarity his audience will have with the intellectual underpinnings of his argument, so he recaps those before embarking on the argument proper, in order to make sure the audience can follow. Granted, he does tell a couple of anecdotes in the process, but maybe that's his style. I think it's fair to consider impatience implicated here - for what it's worth, when I find myself feeling that way about coverage of stuff I already know but not everyone is guaranteed to, I usually just skip ahead or scroll ahead, checking in here and there, until I hit something on point or that I don't already know. (Usually the second one!) Impatience is an emotion, and while we can't help much what we feel or how we feel it, we can most of the time treat what we feel as input. Think of it, if you want, like a Datadog alert. How do we handle those? By investigating to understand the root cause and taking whatever action that requires in the context, if any. If we let them drive our behavior directly without taking the time for considered action, we easily risk causing more problems than we're likely to solve. Granted, I don't entirely love this metaphor, which is no less flawed than any. Maybe too some dork on Twitter will use this as an example of the mechanistic techbro attitude endemic to the diseased discourse of Hacker News comments, or something; it does lend itself somewhat to such misrepresentation. But despite that lossiness I think it's not wholly without use, because it does point at least vaguely toward a way in which we can manage and make valuable use of even the most unpleasant among our emotions, and one that's served me well over the years since I stumbled upon the concept in some writing or other, I've long since forgotten where. (I don't think Deutsch was rambling, but I certainly am, in an effort to distract myself from a quite unpleasant facial pain I can't do anything meaningful about until Thursday. Please excuse me.)
JoshCole 5y ago
He doesn't actually argue against the Bayesian view of statistics: > The word ‘Bayesianism’ is used for a variety of things, a whole spectrum of things at one end of which I have no quarrel with whatsoever and at the other end of which I think is just plain inductivism. So at the good end, Bayesianism is just a word for using conditional probabilities correctly. So if you find that your milkman was born in the same small village as you, and you are wondering what kind of a coincidence that is, and so on, you've got to look at the conditional probabilities, rather than the absolute probabilities. So there isn't just one chance in so many million, but there's a smaller chance. His problem seems to be the extension of it to epistemology: > At the other end of the spectrum, a thing which is called Bayesianism is what I prefer to call ‘Bayesian epistemology’, because it's the epistemology that's wrong, not Bayes’ theorem. Bayes’ theorem is true enough. But Bayesian epistemology is just the name of a mistake. It's a species of inductivism and currently the most popular species. But the idea of Bayesian epistemology is that, first of all, it completely swallows the justified true belief theory of knowledge. His problem with inductivism is that when you follow it you don't try to make theories more believable by getting rid of ones that don't fit, but by confirming instances in which your theory does fit: > It's inductivism with a particular measure of how strongly you believe a theory and with a particular kind of framework for how you justify theories: you justify theories by finding confirming instances. So that is a mistake because if theories had probabilities – which they don’t – then the probability of a theory (‘probability’ or ‘credence’, in this philosophy they’re identical, they’re synonymous)... if you find a confirming instance, the reason your credence goes up is because some of the theories that you that were previously consistent with the evidence are now ruled out. > And so 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. And that's the thing that Popper and Miller proved in the 1980s. A colleague and I have been trying to write a paper about this for several years to explain why this is so in more understandable terms. He cites this paper as an example of a proof, but claims it isn't very approachable (which is why he is working on one with more understandable terms). https://sci-hub.3800808.com/10.1038/302687a0 https://sci-hub.3800808.com/10.1038/302687a0
- SubiculumCode 5y ago
comp_throw7 5y ago
This is a deeply confused set of arguments. There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". While what we ultimately care about is the territory, which is fixed, we only have our imperfect map of the world with which to model it. There is nothing inherently "probabilistic" about a shuffled deck of cards - the cards have a specific configuration; the uncertainy is only present in the observer(s). Predicting the next draw from a deck of cards is subject to "out of context" model violations the same way that any other prediction over future world states is. The proposed escape hatch seems to be to dodge the question, but of course in reality you are not agnostic over all possible future events that haven't been ruled out by being "bad explanations". You can say, "well, don't make predictions with numbers on them", but by acting in the world you are making predictions with numbers on them all the time! Bayesian epistemology doesn't claim to be perfect, it claims to be the least inaccurate way of making those predictions and updating on new evidence. Now, there's a separate question of how bounded reasoners (i.e. humans) can best accomplish their goals, and "explicitly run Bayes to decide whether to get out of bed" probably isn't it. But superforecasters are an existence proof that explicit Bayesian calculations are still extremely useful even in highly uncertain domains. Mechanistic explanations are great but even having a mechanistic explanation doesn't let you say P(x) = 1. There can be enormous differences in reasonable mitigations to take between P(x) = 0.99 and 0.999999, depending on context.
- ouid 5y ago
  >There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". These are not the same, my observables being independent of each other is not the same thing as my not knowing about the correlated observable. In a practical sense, there are lots of ways that I could reason that there is a continent in the unexplored bit, and obviously no way to know which card will come up in a shuffle.
  - 5y ago
nonrandomstring 5y ago
What a rich essay. Not sure why Bayes features in the title (and opens a distracting garden path into discussions of statistics), as the actual discussion is deeper, regarding the use of knowledge of the past to constructing "knowledge" of the future and how these are really two different epistemologies. Rather oddly I found it connected a lot with another post from today, "Why Pessimism Sounds Smart" Deutsch says; "In the 20th century, there was a sort of congealing of the intellectual climate into a very rigid pessimism, so that a prediction or prophecy could only be taken seriously if it was negative." If he unpacks this, it's in earlier statements about the faults of induction and the lack of wonder, faith, possibility and sheer mischief that a scientific method occludes if it admits strong theories and builds a probability cone of worlds that extrapolate from them. I guess, as I was trying to say in the other thread, that Deutsch calls "epitome of the wrong theory", that we've used technology to create a set of distorting optics that stop us seeing optimistic interpretations all around us. How do we get out of that parochialism of the present? Embrace radical falliblism and entertain the idea that against all probability you, I and all the smartest people we know might be hopelessly wrong - and that that's a good thing?
- dsjoerg 5y ago
  Maybe I missed it... is there an essay? Or are you talking about the transcript?
  - nonrandomstring 5y ago
    Did you watch a video? I read a... er yes a transcript is the correct word :) I think we're on the same page but in different mediums.
- resters 5y ago
  I almost didn't read it because the top HN comment was negative (and upon reading the transcript, quite irrelevant). The parent of this comment should probably be the top comment.
npalli 5y ago
Great essay, deeper than being about just Bayesianism. In the essay David talks a lot about explanations and what is good one v/s a bad one. The short TED lecture by him gives a deeper insight into his explanation of why good explanations (hard to vary theory of the world) are the key to scientific and human progress since the scientific revolution. (17 min at 1x) https://www.youtube.com/watch?v=folTvNDL08A https://www.youtube.com/watch?v=folTvNDL08A
credit_guy 5y ago
Physicists have this nice saying: "Shut up and calculate". Bayesianism, frequentism, who cares? "All models are wrong, but some are useful". Bayesianism would be a footnote in history if it weren't for Markov Chain Monte Carlo, which allows you to perform estimations when Maximum Likelihood fails. All those people who wax poetic about priors and expert knowledge and stuff, just keep away from them. You don't want to mix with that crowd.
- deleted 5y ago
  [deleted]
- goatlover 5y ago
  > Physicists have this nice saying: "Shut up and calculate". Some, but not all. Some want to know what it is that makes it so we can calculate the probability of a measurement, or whether a black hole loses information. Some are/were quite philosophical, including Feynman, despite his protestations.
  - credit_guy 5y ago
    Not some, all. Physicists earn their right to talk about deep foundational things after they first shut up and calculate. Those who are not able to calculate, don't get tenure.

121 points by jger15 5y ago

For the clearest and most precise view of Deutsch's philosophy of science in the context of quantum theory, read his paper The logic of experimental tests, particularly of Everettian quantum theory: https://www.sciencedirect.com/science/article/pii/S135521981530023X https://www.sciencedirect.com/science/article/pii/S135521981... There's a breakdown of the paper here: https://www.youtube.com/watch?v=DHLTBOJ5hoQ&t=42s https://www.youtube.com/watch?v=DHLTBOJ5hoQ&t=42s A quote mentioning Bayesian Epistemology: Prevailing discussions (e.g. Dawid & Thébault, 2014; Greaves & Myrvold, 2010) of the testability of various versions of quantum theory have approached the matter indirectly, in terms of support or confirmation – asking how our credence (degree of belief) for a theory should be changed by experiencing results of experiments. However, experimental confirmation is a philosophically contentious concept. Notably, it is rejected root and branch by Popper (1959). I shall present an account of the nature and methodology of scientific testing that closely follows Popper׳s. It differs from his, if at all,3 by regarding fundamental science as exclusively explanatory. That is to say, I take a scientific theory to be a conjectured explanation4 (explanatory theory) of some aspects of the physical world – the explicanda of the theory – that is testable (I shall elaborate what that means below) by observation and experiment. A scientific explanation is a statement of what is there in reality, and how it behaves and how that accounts for the explicanda. Neither confirmation nor credence nor ‘inductive reasoning’ (from observations to theories or to justifications of theories as true or probable) appear in this account. So in this view the problem described in Section 1 is about testing theories. This contradicts the ‘Bayesian’ philosophy that rational credences obey the probability calculus and that science is a process of finding theories with high rational credences, given the observations. It also contradicts, for instance, instrumentalism and positivism, which identify a scientific theory with its predictions of the results of experiments, not with its explanations. My argument here, that Everettian quantum theory is testable, depends on regarding it as an explanatory theory, and on adopting an improved notion of experimental testing that takes account of that. Scientific methodology, in this conception, is not about anyone׳s beliefs or disbeliefs. Rather, it assumes that someone has conjectured explanatory theories (though it says nothing about how to do that), and it requires those who know (i.e. are aware of) those theories and want to improve them, to attempt to locate specific flaws and deficiencies and to attempt to correct those by conjecturing new theories or modifications to existing theories. Explicanda in the sciences usually involve appearances of some sort (e.g. the perceived blueness of the sky). Theoretical matters can also be explicanda (e.g. that classical gravity and electrostatics both have an inverse-square force law), but those will not concern us here. Explanations of appearances typically account for them in terms of an unperceived, underlying reality (e.g. differential scattering of photons of different energies) that brings about those appearances (though not only them).

im3w1l 5y ago
I believe you can actually get Popperian falsification out of Bayesianism if you squint right. Consider an falsification experiment. It disproves some theories while not changing our relative beliefs in other theories. this would be basically for all i in S1 p(evidence | theory i) = 0 for all i in S2 p(evidence | theory i) = k * p(evidence) I would say that most scientific evidence is of this sort, except that the probabilities for the "falsified" theories can also be a little bit above 0 to account for measurement error. Edit: Actually it may be fruitful to introduce a distinction similar to the one probability theory has... In probability theory there is a difference between sample and event. An event is a set of samples. In our case, I believe we want a distinction between a theory... and a lets call it micro-theory. To pick a funny and memorable example a theory could be something like "there is a Loch Ness monster". Now a micro-theory could be something like "there is a Loch Ness monster, that is invisble, and makes no sounds, but it can be detected by radar... and... and...". So it would include all these additional constraints. So the theory it's composed of a lot of these micro theories right. Now if we take photos of every inch of Loch Ness, and we don't find any Loch Ness monster, we make it less likely that there is a monster right. We may say "oh we were careless, and just missed it" but if we keep looking eventually that becomes an impossibility. So we disprove a bunch of micro theories, but some will remain. Our previous micro theory that among other thing says that the monster is invisible remains. And whats worse the relative likelyhood of them is unchanged compared to the no monster. p(cant get monster on photo | no monster) = k p (cant get monster on photo) and p(cant get monster on photo | invisible monster) = k p (cant get monster on photo) Now if we want to further increase our belief in "no monster" we would have to go after these wacky micro-theories and disprove them, using e.g. radar. But given that a sensible person assigns those micro-theories low prior likelyhood we may be satisfied with the situation and not bother. So basically these was just Popperianism in Bayesianism clothing right? Almost... Notice the very last point. We allowed ourselves not to bother with theories of invisible monsters. Because of our prior likelyhood.

mgh2 5y ago

mnl 5y ago
So that's the best appraisal you can give of David Deutsch: "Just an old man seeking attention"? Well, leaving aside the usual embarrassment I feel when it comes to the impromptu nonsense a fair share of HN commentators think it's worthwhile to contribute here when there's a piece of news involving physics or physicists, that's an ageist take without any content whatsoever. There's more old people who know what they're talking about than young people. That's just the obvious consequence of having been around reading and thinking about stuff more time. You'll notice it eventually because as the song goes, time waits for no one.
- mgh2 5y ago
  Fair enough, my initial comment was "just a man", reversed. Give it a try, perhaps you are more patient. Today's oversaturated information makes listeners harsh critics.
  - mnl 5y ago
    Yep, we're getting too impatient and that's not helpful when it comes to think deeply about what we've been taught. Yet that's the most important part of any job IMO. I'll check it out, maybe you're right and he's rambling. That would be surprising to me, which is the reason I replied to your post.
    - throwanem 5y ago
      I don't think he's rambling. He isn't sure what familiarity his audience will have with the intellectual underpinnings of his argument, so he recaps those before embarking on the argument proper, in order to make sure the audience can follow. Granted, he does tell a couple of anecdotes in the process, but maybe that's his style. I think it's fair to consider impatience implicated here - for what it's worth, when I find myself feeling that way about coverage of stuff I already know but not everyone is guaranteed to, I usually just skip ahead or scroll ahead, checking in here and there, until I hit something on point or that I don't already know. (Usually the second one!) Impatience is an emotion, and while we can't help much what we feel or how we feel it, we can most of the time treat what we feel as input. Think of it, if you want, like a Datadog alert. How do we handle those? By investigating to understand the root cause and taking whatever action that requires in the context, if any. If we let them drive our behavior directly without taking the time for considered action, we easily risk causing more problems than we're likely to solve. Granted, I don't entirely love this metaphor, which is no less flawed than any. Maybe too some dork on Twitter will use this as an example of the mechanistic techbro attitude endemic to the diseased discourse of Hacker News comments, or something; it does lend itself somewhat to such misrepresentation. But despite that lossiness I think it's not wholly without use, because it does point at least vaguely toward a way in which we can manage and make valuable use of even the most unpleasant among our emotions, and one that's served me well over the years since I stumbled upon the concept in some writing or other, I've long since forgotten where. (I don't think Deutsch was rambling, but I certainly am, in an effort to distract myself from a quite unpleasant facial pain I can't do anything meaningful about until Thursday. Please excuse me.)

JoshCole 5y ago

He doesn't actually argue against the Bayesian view of statistics: > The word ‘Bayesianism’ is used for a variety of things, a whole spectrum of things at one end of which I have no quarrel with whatsoever and at the other end of which I think is just plain inductivism. So at the good end, Bayesianism is just a word for using conditional probabilities correctly. So if you find that your milkman was born in the same small village as you, and you are wondering what kind of a coincidence that is, and so on, you've got to look at the conditional probabilities, rather than the absolute probabilities. So there isn't just one chance in so many million, but there's a smaller chance. His problem seems to be the extension of it to epistemology: > At the other end of the spectrum, a thing which is called Bayesianism is what I prefer to call ‘Bayesian epistemology’, because it's the epistemology that's wrong, not Bayes’ theorem. Bayes’ theorem is true enough. But Bayesian epistemology is just the name of a mistake. It's a species of inductivism and currently the most popular species. But the idea of Bayesian epistemology is that, first of all, it completely swallows the justified true belief theory of knowledge. His problem with inductivism is that when you follow it you don't try to make theories more believable by getting rid of ones that don't fit, but by confirming instances in which your theory does fit: > It's inductivism with a particular measure of how strongly you believe a theory and with a particular kind of framework for how you justify theories: you justify theories by finding confirming instances. So that is a mistake because if theories had probabilities – which they don’t – then the probability of a theory (‘probability’ or ‘credence’, in this philosophy they’re identical, they’re synonymous)... if you find a confirming instance, the reason your credence goes up is because some of the theories that you that were previously consistent with the evidence are now ruled out. > And so 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. And that's the thing that Popper and Miller proved in the 1980s. A colleague and I have been trying to write a paper about this for several years to explain why this is so in more understandable terms. He cites this paper as an example of a proof, but claims it isn't very approachable (which is why he is working on one with more understandable terms). https://sci-hub.3800808.com/10.1038/302687a0 https://sci-hub.3800808.com/10.1038/302687a0

SubiculumCode 5y ago

comp_throw7 5y ago

This is a deeply confused set of arguments. There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". While what we ultimately care about is the territory, which is fixed, we only have our imperfect map of the world with which to model it. There is nothing inherently "probabilistic" about a shuffled deck of cards - the cards have a specific configuration; the uncertainy is only present in the observer(s). Predicting the next draw from a deck of cards is subject to "out of context" model violations the same way that any other prediction over future world states is. The proposed escape hatch seems to be to dodge the question, but of course in reality you are not agnostic over all possible future events that haven't been ruled out by being "bad explanations". You can say, "well, don't make predictions with numbers on them", but by acting in the world you are making predictions with numbers on them all the time! Bayesian epistemology doesn't claim to be perfect, it claims to be the least inaccurate way of making those predictions and updating on new evidence. Now, there's a separate question of how bounded reasoners (i.e. humans) can best accomplish their goals, and "explicitly run Bayes to decide whether to get out of bed" probably isn't it. But superforecasters are an existence proof that explicit Bayesian calculations are still extremely useful even in highly uncertain domains. Mechanistic explanations are great but even having a mechanistic explanation doesn't let you say P(x) = 1. There can be enormous differences in reasonable mitigations to take between P(x) = 0.99 and 0.999999, depending on context.

ouid 5y ago
>There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". These are not the same, my observables being independent of each other is not the same thing as my not knowing about the correlated observable. In a practical sense, there are lots of ways that I could reason that there is a continent in the unexplored bit, and obviously no way to know which card will come up in a shuffle.
- 5y ago

nonrandomstring 5y ago

What a rich essay. Not sure why Bayes features in the title (and opens a distracting garden path into discussions of statistics), as the actual discussion is deeper, regarding the use of knowledge of the past to constructing "knowledge" of the future and how these are really two different epistemologies. Rather oddly I found it connected a lot with another post from today, "Why Pessimism Sounds Smart" Deutsch says; "In the 20th century, there was a sort of congealing of the intellectual climate into a very rigid pessimism, so that a prediction or prophecy could only be taken seriously if it was negative." If he unpacks this, it's in earlier statements about the faults of induction and the lack of wonder, faith, possibility and sheer mischief that a scientific method occludes if it admits strong theories and builds a probability cone of worlds that extrapolate from them. I guess, as I was trying to say in the other thread, that Deutsch calls "epitome of the wrong theory", that we've used technology to create a set of distorting optics that stop us seeing optimistic interpretations all around us. How do we get out of that parochialism of the present? Embrace radical falliblism and entertain the idea that against all probability you, I and all the smartest people we know might be hopelessly wrong - and that that's a good thing?

dsjoerg 5y ago
Maybe I missed it... is there an essay? Or are you talking about the transcript?
- nonrandomstring 5y ago
  Did you watch a video? I read a... er yes a transcript is the correct word :) I think we're on the same page but in different mediums.
resters 5y ago
I almost didn't read it because the top HN comment was negative (and upon reading the transcript, quite irrelevant). The parent of this comment should probably be the top comment.

npalli 5y ago

Great essay, deeper than being about just Bayesianism. In the essay David talks a lot about explanations and what is good one v/s a bad one. The short TED lecture by him gives a deeper insight into his explanation of why good explanations (hard to vary theory of the world) are the key to scientific and human progress since the scientific revolution. (17 min at 1x) https://www.youtube.com/watch?v=folTvNDL08A https://www.youtube.com/watch?v=folTvNDL08A

credit_guy 5y ago

Physicists have this nice saying: "Shut up and calculate". Bayesianism, frequentism, who cares? "All models are wrong, but some are useful". Bayesianism would be a footnote in history if it weren't for Markov Chain Monte Carlo, which allows you to perform estimations when Maximum Likelihood fails. All those people who wax poetic about priors and expert knowledge and stuff, just keep away from them. You don't want to mix with that crowd.

deleted 5y ago
[deleted]
goatlover 5y ago
> Physicists have this nice saying: "Shut up and calculate". Some, but not all. Some want to know what it is that makes it so we can calculate the probability of a measurement, or whether a black hole loses information. Some are/were quite philosophical, including Feynman, despite his protestations.
- credit_guy 5y ago
  Not some, all. Physicists earn their right to talk about deep foundational things after they first shut up and calculate. Those who are not able to calculate, don't get tenure.