concept
active
concept:markov-propertyMarkov Property
Assumption required by IIT 3.0/4.0 and PyPhi; tested for each optimal time series derived from (C)ARR.
Neighborhood — ranked by edge-count
Methods (1)
method
- Iterative procedure searching token counts in [50,100,...,1000] to find concatenation of (C)ARR satisfying IIT's Markov and conditional independence assumptions.
Concepts (1)
concept
- Binarization of Representation Time Seriesassociated_withProcess of converting z-scored PCA-reduced (C)ARR into binary sequences (above/below mean) to satisfy IIT 3.0/4.0 discrete Markovian constraints.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- A statistical partition of states that separates internal states from external hidden states; fundamental to self-organization in the paper.
- Generative model substrate for active inference; discrete states, actions, outcomes, and temporal policies.
- Modeling framework for discrete state-space decision-making under uncertainty, used as generative model in active inference.
- The quality that makes a space or structure feel alive, whole, and wonderful; measured by the degree of wholeness.
- Core computational method used to infer pain-belief from online observations of happiness
- Alternative approach noted but dismissed as computationally intractable for the rule-learning problem
- Strategy used by transformers that recomputes relevant numeric information at each step, unlike Markovian GRU solutions; detected by MAS but not by RSA/CKA.