Formas persistentes: de Pitágoras a la Teoría de Kolmogorov
Zenodo (CERN European Organization for Nuclear Research) July 12, 2026 DOI: 10.5281/zenodo.21326169 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Experiential learning Turing Kolmogorov complexity Object grammar Scope computer science Cognition Closure psychology Tracing Halting problem Epistemology Artificial intelligence Cognitive science Section typography Through-the-lens metering Theoretical computer science Experiential knowledge |
| Key points | Proposes that mind, agency, and experience can be formally modeled as algorithmic processes using information theory and Kolmogorov complexity. |
Abstract
This work presents a structured theoretical framework for understanding mind, agency, and experience through the lens of algorithmic information theory, tracing a philosophical lineage from Pythagoras and Aristotle through Kant and Turing to Kolmogorov. The framework, designated KT-ESP, proposes that an agent can be modeled as an algorithmic entity that receives information, constructs compressed internal representations analogous to ZIP compression, and acts in accordance with structured experiential valence. Drawing on Shannon's information theory, Kolmogorov complexity, Church-Turing computability, and Wolpert's thermodynamic limits of computation, the work argues that genuine mental content and structured experience are amenable to formal, computable description. The scope is primarily theoretical and position-oriented, synthesizing philosophy of mind, computational theory, and cognitive science into a unified account of the algorithmic agent. A concluding section extends the framework toward the quantification of qualia, suggesting that subjective experiential qualities need not remain beyond the reach of rigorous formalization.