This is my first post, so forgive me for it being a bit of a carelessly referenced, informal ramble. Feedback is appreciated.

As I understand it, Yudkowsky's contends that is that there exists an ideal Bayesian, with respect to which any epistemic algorithm is only 'good' insofar as it is approximating it. Specifically, this ideal Bayesian is following a procedure such that their prior is defined by a 'best' summary of their available knowledge. This is the basis for which he claims that, for instance, Einstein's reasoning must have had a strictly Bayesian component to it, otherwise he could not have been correct. He generally extends this assertion to argue for seeking that Bayesian explication for why things work as though it were the fundamental underlying reason why, and to toss away the non-Bayesian parts. For a number of reasons, it is not clear to me that such a [...]

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Outline:

(03:55) Accounts of the optimality of Bayesian reasoning

(06:45) Coherence

(08:41) Complete Class Theorems and Optimality

(10:28) Conditions for Bayesianism to work at all

(10:32) Consistency

(14:21) Calibration and Coverage

(20:05) Conclusion

(23:55) Notes

The original text contained 3 footnotes which were omitted from this narration.

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Narrated by TYPE III AUDIO.