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%. [...]

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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.

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Source:

Narrated by TYPE III AUDIO.

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