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Causal Emergence: When Distortions In A Map Obscure The Territory

Talkat Learning from Interventions 2022 · 2022-02-14..02-17· [machine-learning]
links:none on file
slides:none on file

Argues that causal emergence is defined by finding a coarsening partition of a finite-state transition matrix that maximizes effective information, computed as KL divergence / mutual information under maximum entropy interventions, and searches over all coarse-grainings of I and J. Shows simple 8-state examples where seven microstates collapse despite differing effects (sensitive to changes like 0.7→0.8), proves abstraction and marginalization do not commute, and concludes the channel‑capacity analogy and uniqueness of emergent partitions are dubious.

AI-written summary · about

  1. 0:00Introduction
  2. 9:09Theory
  3. 11:48Effective Information
  4. 16:15Causal Emergence
  5. 22:35Example
  6. 25:10What bothers me
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