concept
active
concept:free-energyfree energy
Thermodynamic potential ΔF = ΔE − TΔS; domain walls form if ΔF < 0
Neighborhood — ranked by edge-count
Papers (2)
paper
Frameworks (2)
framework
- Active InferenceimplementsFoundational framework by Karl Friston; the paper extends it to three hierarchical levels for modeling meta-awareness.
- Free Energy Principleassociated_withA foundational variational principle from statistical physics that formalizes how self-organizing systems maintain structural integrity and adapt to their environment by minimizing free energy—a mathematical bound on surprise or prediction error. Originally developed by Karl Friston, the framework unifies action, perception, and learning as processes of active inference, where systems both update internal models of the world and act upon it to reduce the divergence between predictions and observations.
Claims (1)
claim
- Collapses three quantities into one, emphasizing their equivalence under the principle.
Methods (5)
method
- Process by which neuronal dynamics minimize free energy; produces empirically observable neural phenomena.
- Variational Bayesassociated_withMathematical framework for approximating posterior beliefs; converts exact Bayesian inference into optimization.
- Free-energy scaling under domain-wall formationassociated_withKey analytical technique used across three model systems to determine constraints on long-range order.
- Gradient Descent on Free EnergyimplementsOptimization procedure for simultaneously updating action selection and perception; uses step size ζ (default 4).
- Mean-Field ApproximationimplementsVariational technique used in active inference to tractably compute posterior beliefs.
Concepts (13)
concept
- Expected Free Energyrelated_toFree energy expected under future outcomes; guides policy selection via epistemic and extrinsic value.
- Surprise Minimizationassociated_withimplementsCore principle: acting to maximize value is equivalent to minimizing surprise by sampling environment to conform to expectations.
- Thermodynamic Free Energyrelated_toPhysical quantity sharing same minimum as variational free energy (via Jarzynski equality); proxy for computational cost
- Generative Modelassociated_withAgent's internal probabilistic model of environment; enables belief inference about hidden states given outcomes.
- PerceptionimplementsEquated with inference of past, present and future hidden states via minimization of variational free energy.
- domain wallassociated_withBoundary between regions of different order; its free-energy cost determines phase stability
- Precision Parameter (γ)associated_withCore mechanism across all three levels; inverse variance controlling how much the system trusts evidence at each hierarchical level.
- Entropyassociated_withA measurable physical quantity representing disorder; its increase is dictated by the second law of thermodynamics.
- minimum energy principlesrelated_toPrinciples like least action that produce efficient forms in nature, contributing to simplicity and good shape.
- Bayesian model evidenceassociated_withThe probability of sensory data under a generative model; negative log evidence is bounded by free energy.
- Recognition Densityassociated_withApproximate posterior probability distribution embodied in organism's internal states; organism's best guess about causes of sensations
- Surprise (Surprisal)associated_withNegative log probability of an outcome under the generative model; minimized in active inference.
- Phenotypeassociated_withExpected or most probable states organism maintains; focus of active inference minimization
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.
- KL divergence between predicted and preferred final states or outcomes.
- Expected log likelihood of data under posterior beliefs; measures fit to observations.
- Expected entropy of outcomes given states; resolved by selecting states that yield unambiguous outcomes.
- Minimizing expected free energy for planning, decision-making, and action selection.
- Unifying nature of expected free energy claimed in Section 2 and 7.
- The capacity to create wholeness and do what is right; enabled by morphogenetic, whole-seeking processes.
- Biologically plausible approximation lying between mean-field and Bethe approximations.
- Scalar potential in Helmholtz decomposition whose exponential form gives the ergodic density of dynamical systems.