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“Judgements: Merging Prediction & Evidence” by abramdemski

Mar 1, 2025

In this engaging discussion, abramdemski, an author well-versed in Bayesianism and radical probabilism, dives into the nuanced relationship between prediction and evidence. He explores how market dynamics reflect this interplay, shedding light on trading strategies influenced by both intrinsic and extrinsic values. The conversation also unpacks modern reasoning models in judgment and decision-making, contrasting them with traditional beliefs, and reveals how unlimited resources reshape trading behavior. A thought-provoking exploration for anyone curious about decision theory!

11:13

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abramdemski

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Quick takeaways

The podcast illustrates how radical probabilism merges prediction and evidence, challenging the rigid separation found in classical Bayesianism.

It discusses the trader continuum, highlighting the differences between collector-like and investor-like behaviors and their implications for market strategies.

Deep dives

Bridging Predictions and Evidence

The concept of judgment serves to unify prediction and evidence within decision-making contexts. In classical Bayesianism, predictions are treated distinctly from evidence, as the former reflects an agent's forecast and the latter embodies observations from the environment. Conversely, radical probabilism blurs these boundaries by considering softer forms of evidence, which can influence beliefs without resulting in absolutes. This approach allows for a more nuanced understanding of how predictions and evidence interact, suggesting that both play integral roles within judgment.

Exploring the Interplay of Prediction and Evidence in Market Dynamics

9min

Exploring Judgment and Trading Dynamics

2min

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.

The original text contained 1 image which was described by AI.

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First published:
February 23rd, 2025

Source:
https://www.lesswrong.com/posts/3hs6MniiEssfL8rPz/judgements-merging-prediction-and-evidence

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Narrated by TYPE III AUDIO.

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