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Ideas and formalisms from far-from-equilibrium thermodynamics are ported tothe context of stochastic computational processes, via following and extendingTadaki's algorithmic thermodynamics. A Principle of Maximum Algorithmic Caliberis proposed, providing guidance as to what computational processes one shouldhypothesize if one is provided constraints to work within. It is conjecturedthat, under suitable assumptions, computational processes obeying algorithmicMarkov conditions will maximize algorithmic caliber. It is proposed that inaccordance with this, real-world cognitive systems may operate in substantialpart by modeling their environments and choosing their actions to be(approximate and compactly represented) algorithmic Markov networks. Theseideas are suggested as potential early steps toward a general theory of theoperation of pragmatic generally intelligent systems.

Maximal Algorithmic Caliber and Algorithmic Causal Network Inference: General Principles of Real-World General Intelligence?