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Abstraction 2020

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.

11What is Abstraction?
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11Causal Abstraction Toy Model: Medical Sensor
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11Examples of Causal Abstraction
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10Abstraction, Causality, and Embedded Maps: Here Be Monsters
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11Causal Abstraction Intro
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15Definitions of Causal Abstraction: Reviewing Beckers & Halpern
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7How to Throw Away Information in Causal DAGs
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7Example: Markov Chain
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14Logical Representation of Causal Models
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23(A -> B) -> A in Causal DAGs
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15Formulating Reductive Agency in Causal Models
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6Trace: Goals and Principles
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11Trace README
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10Abstraction = Information at a Distance
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7Mediation From a Distance
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11Noise Simplifies
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7Integrating Hidden Variables Improves Approximation
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8Intuitions on Universal Behavior of Information at a Distance
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18Motivating Abstraction-First Decision Theory
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30Writing Causal Models Like We Write Programs
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20Pointing to a Flower
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29Public Static: What is Abstraction?
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16Cartesian Boundary as Abstraction Boundary
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7Causality Adds Up to Normality
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14The Indexing Problem
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15Abstraction, Evolution and Gears
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