AIT Foundations of Structured Experience

Kolmogorov Theory (KT): an algorithmic-information-theoretic framework for \emph{structured experience} ($\mathcal{S}$)---the spatial, temporal, and conceptual organization of first-person experience---applicable to natural and artificial agents.

\textbf{Premise.} ``There is experience.'' KT does not solve the hard problem; it asks how \emph{structured} experience arises from primordial experience.

\textbf{Central hypothesis.} An agent has $\mathcal{S}$ to the extent it runs \emph{encompassing, compressive models} (``good models'') and successfully compares them with data. Three measurable dimensions: \begin{itemize}\itemsep -2pt \item \textbf{Simplicity}: program length, lower-bounded by Kolmogorov complexity $\mathcal{K}_T(s) = \min\{l(p) \mid T(p) = s\}$. \item \textbf{Breadth}: fraction of agent input/output accounted for. \item \textbf{Realism}: model accuracy, captured by mutual algorithmic information $\mathcal{M}(x{:}y) = \mathcal{K}(y) - \mathcal{K}(y \mid x)$ between agent and world. \end{itemize} $\mathcal{S}$ is the \emph{event} of a model successfully matching data; its qualitative features are determined by the algorithmic structure of the running program.

\textbf{Algorithmic agent.} Information membrane (sensors/effectors) $\rightarrow$ modeling engine (Model + Simulator + Updater) $\rightarrow$ Comparator (XOR locus where data meets prediction) $\rightarrow$ Objective Function (map to $[-1,+1]$, encoding valence and meta-homeostasis) $\rightarrow$ Planner. Loose neurobiological mapping: posterior cortex $\sim$ comparator/content, PFC $\sim$ planner, amygdala/insula $\sim$ objective function. Agents compose (swarms are agents).

\textbf{Why compressive models.} Occam/MDL rationale, Solomonoff universal prior (short programs are more likely generators), resource economy, and generalization. Turing-completeness (recurrence) sits high in the Chomsky--Sch"utzenberger hierarchy and therefore enables richer $\mathcal{S}$ than FFN-like systems.

\textbf{Relation to other theories.} KT is offered as an integrative scaffold: IIT (causal structure), GWT (integration), FEP (active inference / objective function), DIT (microcortical comparator), and critical-brain accounts each illuminate a facet of the same algorithmic agent.

\textbf{Empirical program.} Bridge 1P (neurophenomenology of structured reports, including altered states) with 3P measurements of dimensionality reduction, criticality, and latent-manifold geometry to estimate the simplicity/breadth/realism triple. Defense against the Unfolding Argument: algorithmic class (FSM vs.\ Turing) is an implementation-invariant property, so functional mimicry by FFNs does not trivialize the theory.

\textbf{Philosophy & ethics.} Naturally framed in panpsychism/idealism. Suffering is tied to objective-function design, not to $\mathcal{S}$ \emph{per se}; good'' and evil'' admit computational definitions via cross-coupling of agents' objective functions ($O_A'(O_B) \gtrless 0$). No special status for humans: any system that captures world-structure has $\mathcal{S}$.

\textbf{Deliverables of the research program.} (a) AIT-based $\mathcal{S}$ estimators, (b) compression-driven learning algorithms, (c) design guidelines for artificial $\mathcal{S}$, (d) ethics for human--machine coexistence.