If a system attains a general steady-state, it will appear to behave in a Bayes optimal fashion, both in terms of optimal Bayesian design (exploration) and Bayesian decision theory (exploitation).

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• Behavior prescribed by active inference dynamics is approximately Bayes-optimal.claim0.823
  Process theory outcomes produce normatively sound decision-making.
• Bayes-optimal Behaviorconcept0.819
  Behavior that minimizes expected free energy under the generative model, balancing exploration and exploitation in a principled manner.
• Bayes-optimal perception inherently produces self-organised instability (local Lyapunov exponents fluctuating around zero), so FEP drives the system toward criticality (Friston et al. 2012)finding0.816
  Establishes the mechanistic link between lower VFE and critical dynamics, supporting the paper's criticality prediction
• Active inference achieves Bayes-optimal behavior in non-stationary environments through online belief updating.hypothesis0.788
  Tested via FrozenLake experiments; predicts superior performance when environment dynamics change.
• Bayes-optimal conditional inferenceconcept0.780
  The ideal limit of self-supervised learning as modeling the true conditional distribution.
• Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model.quote0.779
  Opening sentence defining self-evidencing.
• Optimal Bayesian Designframework0.768
  Selecting actions to maximize expected information gain.
• Any system minimizing free energy will appear to engage in implicit Bayesian inference of hidden external causes.hypothesis0.767
  Predicts that internal states encode posterior beliefs about external world through gradient descent on free energy.