I recently wrote about complete feedback, an idea which I think is quite important for AI safety. However, my note was quite brief, explaining the idea only to my closest research-friends. This post aims to bridge one of the inferential gaps to that idea. I also expect that the perspective-shift described here has some value on its own.
In classical Bayesianism, prediction and evidence are two different sorts of things. A prediction is a probability (or, more generally, a probability distribution); evidence is an observation (or set of observations). These two things have different type signatures. They also fall on opposite sides of the agent-environment division: we think of predictions as supplied by agents, and evidence as supplied by environments.
In Radical Probabilism, this division is not so strict. We can think of evidence in the classical-bayesian way, where some proposition is observed and its probability jumps to 100%. [...]
---
Outline:(02:39) Warm-up: Prices as Prediction and Evidence
(04:15) Generalization: Traders as Judgements
(06:34) Collector-Investor Continuum
(08:28) Technical Questions
The original text contained 3 footnotes which were omitted from this narration. The original text contained 1 image which was described by AI. ---
First published: February 23rd, 2025
Source: https://www.lesswrong.com/posts/3hs6MniiEssfL8rPz/judgements-merging-prediction-and-evidence ---
Narrated by
TYPE III AUDIO.
---
Images from the article:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app. 
