Bayes can explain why negative, disconfirmatory evidence counts more than positive support, and so sport a version of falsificationism. But it can’t rule out positive support, so doesn’t imply the more extreme Popperian doctrine that there is no justification.
A hard-to-vary explanation is a minimal explanation, one with no redundant parts. So hardness-to-vary is a simplicity criterion, a form of Occam’s razor. Compared to the simplicity criterion favoured by Bayesians, programme length, it is rather subjective. Neither criterion answers the hard problem,the problem of why simplicity implies truth. But Deutsch is more interested in Knowledge , which is left very vaguely defined.
In theory, Bayes is is about adjusting the credences of Every Possible Hypothesis. In practice, you don’t know every possible hypothesis, so there is some truth to Deutch’s claim that not-H is a blob … you might be able to locate some hypotheses other than H, but you have no chance of specifying all infinity.
Bayesians tend to be incurious about where hypotheses come from. That’s one of Chapman’s criticisms, that Bayes isn’t a complete epistemology because it can’t generate hypotheses. Popperians , by contrast, put a lot of emphasis on hypothesis-formation as a an informal, non-mechanistic process.
Good points. There were several chapters in Rationality: A-Z dedicating to this. According to Max Tegmark’s speculations, all mathematically possible universes exist, and we happen to be in one described by a simple Standard Model. I suspect that this question about why simple explanations are so effective in this universe is unanswerable but still fun to speculate about.
Good points about the lack of emphasis on hypothesis-formation within the Bayesian paradigm. Eliezer talks about this a little in Do Scientists Already Know This Stuff?
Sir Roger Penrose—a world-class physicist—still thinks that consciousness is caused by quantum gravity. I expect that no one ever warned him against mysterious answers to mysterious questions—only told him his hypotheses needed to be falsifiable and have empirical consequences.
I long for a deeper treatment on hypothesis-formation. Any good books on that?
. I suspect that this question about why simple explanations are so effective in this universe is unanswerable but still fun to speculate about.
What does “effective” mean? If you are using a simplicity criterion to decide between theories that already known to be predictive , as in Solomonoff induction, then simplicity doesn’t buy you any extra predictiveness.
Bayes can explain why negative, disconfirmatory evidence counts more than positive support, and so sport a version of falsificationism. But it can’t rule out positive support, so doesn’t imply the more extreme Popperian doctrine that there is no justification.
A hard-to-vary explanation is a minimal explanation, one with no redundant parts. So hardness-to-vary is a simplicity criterion, a form of Occam’s razor. Compared to the simplicity criterion favoured by Bayesians, programme length, it is rather subjective. Neither criterion answers the hard problem,the problem of why simplicity implies truth. But Deutsch is more interested in Knowledge , which is left very vaguely defined.
In theory, Bayes is is about adjusting the credences of Every Possible Hypothesis. In practice, you don’t know every possible hypothesis, so there is some truth to Deutch’s claim that not-H is a blob … you might be able to locate some hypotheses other than H, but you have no chance of specifying all infinity.
Bayesians tend to be incurious about where hypotheses come from. That’s one of Chapman’s criticisms, that Bayes isn’t a complete epistemology because it can’t generate hypotheses. Popperians , by contrast, put a lot of emphasis on hypothesis-formation as a an informal, non-mechanistic process.
Good points. There were several chapters in Rationality: A-Z dedicating to this. According to Max Tegmark’s speculations, all mathematically possible universes exist, and we happen to be in one described by a simple Standard Model. I suspect that this question about why simple explanations are so effective in this universe is unanswerable but still fun to speculate about.
Good points about the lack of emphasis on hypothesis-formation within the Bayesian paradigm. Eliezer talks about this a little in Do Scientists Already Know This Stuff?
I long for a deeper treatment on hypothesis-formation. Any good books on that?
What does “effective” mean? If you are using a simplicity criterion to decide between theories that already known to be predictive , as in Solomonoff induction, then simplicity doesn’t buy you any extra predictiveness.