LW - My thesis (Algorithmic Bayesian Epistemology) explained in more depth by Eric Neyman

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: My thesis (Algorithmic Bayesian Epistemology) explained in more depth, published by Eric Neyman on May 10, 2024 on LessWrong.
In March I posted a very short description of my PhD thesis, Algorithmic Bayesian Epistemology, on LessWrong. I've now written a more in-depth summary for my blog, Unexpected Values. Here's the full post:
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In January, I defended my PhD thesis. My thesis is called Algorithmic Bayesian Epistemology, and it's about predicting the future.
In many ways, the last five years of my life have been unpredictable. I did not predict that a novel bat virus would ravage the world, causing me to leave New York for a year. I did not predict that, within months of coming back, I would leave for another year - this time of my own free will, to figure out what I wanted to do after graduating. And I did not predict that I would rush to graduate in just seven semesters so I could go work on the AI alignment problem.
But the topic of my thesis? That was the most predictable thing ever.
It was predictable from the fact that, when I was six, I made a list of who I might be when I grow up, and then attached probabilities to each option. Math teacher? 30%. Computer programmer? 25%. Auto mechanic? 2%. (My grandma informed me that she was taking the under on "auto mechanic".)
It was predictable from my life-long obsession with forecasting all sorts of things, from hurricanes to elections to marble races.
It was predictable from that time in high school when I was deciding whether to tell my friend that I had a crush on her, so I predicted a probability distribution over how she would respond, estimated how good each outcome would be, and calculated the expected utility.
And it was predictable from the fact that like half of my blog posts are about predicting the future or reasoning about uncertainty using probabilities.
So it's no surprise that, after a year of trying some other things (mainly auction theory), I decided to write my thesis about predicting the future.
If you're looking for practical advice for predicting the future, you won't find it in my thesis. I have tremendous respect for groups like Epoch and Samotsvety: expert forecasters with stellar track records whose thorough research lets them make some of the best forecasts about some of the world's most important questions. But I am a theorist at heart, and my thesis is about the theory of forecasting. This means that I'm interested in questions like:
How do I pay Epoch and Samotsvety for their forecasts in a way that incentivizes them to tell me their true beliefs?
If Epoch and Samotsvety give me different forecasts, how should I combine them into a single forecast?
Under what theoretical conditions can Epoch and Samotsvety reconcile a disagreement by talking to each other?
What's the best way for me to update how much I trust Epoch relative to Samotsvety over time, based on the quality of their predictions?
If these sorts of questions sound interesting, then you may enjoy consuming my thesis in some form or another. If reading a 373-page technical manuscript is your cup of tea - well then, you're really weird, but here you go!
If reading a 373-page technical manuscript is not your cup of tea, you could look at my thesis defense slides (PowerPoint, PDF),[1] or my short summary on LessWrong.
On the other hand, if you're looking for a somewhat longer summary, this post is for you! If you're looking to skip ahead to the highlights, I've put a * next to the chapters I'm most proud of (5, 7, 9).
Chapter 0: Preface
I don't actually have anything to say about the preface, except to show off my dependency diagram.
(I never learned how to make diagrams in LaTeX. You can usually do almost as well in Microsoft Word, with way less effort!)
Chapter 1: Introduction
"Algorithmic Bayesian epistemology" (the title of the...