Rosas's Causal Emergence via φID

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• ΦID-based estimation of causal emergence in RL latent dynamicsmethod0.825
  The specific procedure: train RL agents, extract latent representations over time, and compute causal emergence using the Integrated Information Decomposition framework.
• Causal emergence may be a previously undisclosed axis of reorganization of neural representations in RL agents.claim0.766
  Authors' interpretive assertion that the observed alignment reveals a novel organizing principle of neural representation dynamics.
• Hoel's Causal Emergence Theoryframework0.756
  Quantitative emergence theory based on Markov dynamics and effective information (EI).
• Causal emergence measured by NIS+ increases with observational noise but decreases with dynamical noise.finding0.753
  Insight that coarse-graining filters external noise but not intrinsic noise.
• Causal Emergenceconcept0.752
  Core concept: degree to which an agent exerts unique predictive power on its future; key to cognition at all scales.
• Biological and artificial agents share causal emergence as an axis of learning and reorganization.claim0.739
  Interpretive assertion bridging Levin's biological cognition work with artificial RL; extends 'minds at all scales' thesis.
• Causal emergence alignment with learning is a shared axis comparing biological and artificial creatures.claim0.739
  Assertion that the correlation between causal emergence and learning constitutes another way biological and artificial intelligences converge.
• Causally Emergent Alignment Hypothesishypothesis0.737
  The hypothesis that successful RL agents will display causal emergence that is predictive of final reward early in training and whose representational dynamics align with reward improvement.