Maximal Algorithmic Caliber and Algorithmic Causal Network Inference:
  General Principles of Real-World General Intelligence?
  Ideas and formalisms from far-from-equilibrium thermodynamics are ported to
the context of stochastic computational processes, via following and extending
Tadaki's algorithmic thermodynamics. A Principle of Maximum Algorithmic Caliber
is proposed, providing guidance as to what computational processes one should
hypothesize if one is provided constraints to work within. It is conjectured
that, under suitable assumptions, computational processes obeying algorithmic
Markov conditions will maximize algorithmic caliber. It is proposed that in
accordance with this, real-world cognitive systems may operate in substantial
part by modeling their environments and choosing their actions to be
(approximate and compactly represented) algorithmic Markov networks. These
ideas are suggested as potential early steps toward a general theory of the
operation of pragmatic generally intelligent systems.
