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Abstraction 2020

Dec 06, 2019 by johnswentworth

Research toward a theory of abstraction suitable for embedded agency.

Key background concepts:

Causal DAGs with symmetry as a model of computation
The "minimal map" interpretation of probability

Both of these will be leveraged very heavily in this sequence, so it's worth checking them out before diving in.

This work is supported by a grant from the long-term future fund.

37What is Abstraction?

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34Causal Abstraction Toy Model: Medical Sensor

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24Examples of Causal Abstraction

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26Abstraction, Causality, and Embedded Maps: Here Be Monsters

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24Causal Abstraction Intro

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30Definitions of Causal Abstraction: Reviewing Beckers & Halpern

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20How to Throw Away Information in Causal DAGs

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15Example: Markov Chain

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37Logical Representation of Causal Models

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48(A -> B) -> A in Causal DAGs

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33Formulating Reductive Agency in Causal Models

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13Trace: Goals and Principles

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37Trace README

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29Abstraction = Information at a Distance

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16Mediation From a Distance

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28Noise Simplifies

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15Integrating Hidden Variables Improves Approximation

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21Intuitions on Universal Behavior of Information at a Distance

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43Motivating Abstraction-First Decision Theory

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90Writing Causal Models Like We Write Programs

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61Pointing to a Flower

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97Public Static: What is Abstraction?

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35Cartesian Boundary as Abstraction Boundary

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13Causality Adds Up to Normality

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36The Indexing Problem

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29Abstraction, Evolution and Gears

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