The post doesn’t emphasize this angle, but this is also more-or-less my abstract story for the classic puzzle of why disagreement is so prevalent, which, from a Bayesian-wannabe rather than a human perspective, should be shocking: there’s only one reality, so honest people should get the same answers. How can it simultaneously be the case that disagreement is ubiquitous, but people usually aren’t outright lying? Explanation: the “dishonesty” is mostly in the form of motivatedly asking different questions.
Possible future work: varying the model assumptions might yield some more detailed morals. I never got around to trying the diminishing-marginal-relevance variation suggested in footnote 8. Another variation I didn’t get around to trying would be for the importance of a fact to each coalition’s narrative to vary: maybe there are a few “sacred cows” for which the social cost of challenging is huge (as opposed to just having to keep one’s ratio of off-narrative reports in line).
Prior work: So, Ihappened to learn about the filtered-evidence problem from the Sequences, but of course, there’s a big statistics literature about learning from missing data that I learned a little bit about in 2020 while perusing Ch. 19 of Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and the other guy.
(Self-review.) I’ve edited the post to include the 67log27+17log221 calculation as footnote 10.
The post doesn’t emphasize this angle, but this is also more-or-less my abstract story for the classic puzzle of why disagreement is so prevalent, which, from a Bayesian-wannabe rather than a human perspective, should be shocking: there’s only one reality, so honest people should get the same answers. How can it simultaneously be the case that disagreement is ubiquitous, but people usually aren’t outright lying? Explanation: the “dishonesty” is mostly in the form of motivatedly asking different questions.
Possible future work: varying the model assumptions might yield some more detailed morals. I never got around to trying the diminishing-marginal-relevance variation suggested in footnote 8. Another variation I didn’t get around to trying would be for the importance of a fact to each coalition’s narrative to vary: maybe there are a few “sacred cows” for which the social cost of challenging is huge (as opposed to just having to keep one’s ratio of off-narrative reports in line).
Prior work: So, I happened to learn about the filtered-evidence problem from the Sequences, but of course, there’s a big statistics literature about learning from missing data that I learned a little bit about in 2020 while perusing Ch. 19 of Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and the other guy.