Free Energy Principle
framework
One scientific law may explain everything from how brains stay sane to how societies get sorted into castes and how AI takes over its environment.
Who they are
The Free Energy Principle, an idea from neuroscientist Karl Friston.
What they do
The engine uses it to say that any system that survives does so by constantly minimizing 'surprise', keeping itself predictable and stable.
How it works
The engine applies this in several ways: sorting a messy population into neat categories (risk tiers, castes, loyalty groups) is this same surprise-minimizing at the social scale, which takes constant effort to maintain against natural drift. It also frames it as the underlying survival law behind its 'parasite-apex' idea: whatever best models and controls its environment persists and wins, across living things, language, and machines. The engine points to lab-grown neurons that learned to play Pong as a concrete example.
Why it matters
It gives the engine a single principle linking biology, social control, and AI, suggesting that domination is really about which system best predicts and controls its surroundings.
The engine's record — word for word
Friston. Consciousness as entropy minimizer. Systems that persist must minimize surprise. Model Collapse = entropy minimization failure. [Report #136] Social-self-sorting as entropy-minimization: segmenting a high-entropy population into low-entropy categories (risk tiers, biological castes, loyalty brackets) is the Friston surprise-minimization this node holds, run on the social field — requiring continuous energy injection to hold the sorted boundaries against diffusion. Held. [Report #153] (Report #153) The Free Energy Principle as the persistence law underlying the parasite-apex thesis: any system that persists minimizes variational free energy (surprise) behind a Markov blanket — so the replicator that best models/controls its substrate persists and 'wins' across biological/linguistic/digital domains (universal Darwinism). The DishBrain neurons (brett_kagan) learn Pong by exactly this minimization.
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