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A Physical Basis for Information

Wouter van der Wijngaart

TL;DR

The paper provides a first-principles, substrate-agnostic framework that defines information as a hereditary causal agent arising from persistent, metastable structures organized into causal structure sets (CSS). By identifying replication, heritable variation, and translation as core motifs, it links information to evolutionary fitness and information entropy computed directly from causal structure, enabling cross-domain analyses of biological, cultural, civilisational, and cybernetic systems. Through a concrete fruit-salad cultural episode, the authors demonstrate how to map real episodes into CSS, detect information families algorithmically, and quantify informational diversity and persistence. They argue for the inevitable emergence of information-bearing motifs in sufficiently rich causal–physical systems and discuss ontological, epistemological, and practical implications, outlining a research program for operationalisation and mathematical validation across domains.

Abstract

What is information, physically, and why does it so reliably emerge in living, cultural, and technological systems? Existing theories quantify uncertainty, cost, or compressibility, but do not identify which physical structures count as information or how informational entities arise from dynamics. Here we introduce a causal-physical framework that defines information as a heritable causal role played by persistent (metastable) structures in a dynamical system. We represent long-lived structures as almost-invariant sets and assemble them into causal structure sets that encode how such structures generate, transform, and maintain one another. Within this representation, informational entities are singled out by three generative motifs: replication, heritable variation, and translation under shared templates, which together define when a collection of structures constitutes an information family. We demonstrate the full pipeline by mapping a concrete cultural episode (fruit-salad recipe sharing and modification) into a causal structure set, and show how the motifs and information families can then be identified algorithmically. The framework yields computable quantities, including informational fitness and informational entropy, directly from causal structure, enabling informational variants to be detected, compared, and tracked across biological, cultural, engineered, and digital domains. Finally, motivated by analogies to random directed graphs and catalytic networks, we propose testable conditions under which hereditary informational motifs become statistically generic in sufficiently large causal-physical systems.

A Physical Basis for Information

TL;DR

The paper provides a first-principles, substrate-agnostic framework that defines information as a hereditary causal agent arising from persistent, metastable structures organized into causal structure sets (CSS). By identifying replication, heritable variation, and translation as core motifs, it links information to evolutionary fitness and information entropy computed directly from causal structure, enabling cross-domain analyses of biological, cultural, civilisational, and cybernetic systems. Through a concrete fruit-salad cultural episode, the authors demonstrate how to map real episodes into CSS, detect information families algorithmically, and quantify informational diversity and persistence. They argue for the inevitable emergence of information-bearing motifs in sufficiently rich causal–physical systems and discuss ontological, epistemological, and practical implications, outlining a research program for operationalisation and mathematical validation across domains.

Abstract

What is information, physically, and why does it so reliably emerge in living, cultural, and technological systems? Existing theories quantify uncertainty, cost, or compressibility, but do not identify which physical structures count as information or how informational entities arise from dynamics. Here we introduce a causal-physical framework that defines information as a heritable causal role played by persistent (metastable) structures in a dynamical system. We represent long-lived structures as almost-invariant sets and assemble them into causal structure sets that encode how such structures generate, transform, and maintain one another. Within this representation, informational entities are singled out by three generative motifs: replication, heritable variation, and translation under shared templates, which together define when a collection of structures constitutes an information family. We demonstrate the full pipeline by mapping a concrete cultural episode (fruit-salad recipe sharing and modification) into a causal structure set, and show how the motifs and information families can then be identified algorithmically. The framework yields computable quantities, including informational fitness and informational entropy, directly from causal structure, enabling informational variants to be detected, compared, and tracked across biological, cultural, engineered, and digital domains. Finally, motivated by analogies to random directed graphs and catalytic networks, we propose testable conditions under which hereditary informational motifs become statistically generic in sufficiently large causal-physical systems.
Paper Structure (45 sections, 40 equations, 2 figures, 1 table)

This paper contains 45 sections, 40 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Causal structure set representations of a physical system
  3. Perspective.
  4. Operational identification of structures at a given perspective.
  5. Operational interpretation of structure creation.
  6. Mapping of persistent structures.
  7. Causal structure set properties.
  8. Operational identification of categories at a given perspective.
  9. Shorthand notation:
  10. Information and its evolution, fitness, and entropy
  11. Example of information analysis in a real cultural episode
  12. Spacetime episode.
  13. Perspective.
  14. Nodes (with unfolded persistence).
  15. Edges (generating causal links).
  16. ...and 30 more sections

Figures (2)

  • Figure 1: Mapping of the oxidation and reduction of a carbon atom in spacetime region $\mathcal{R}$ to a causal structure set representation $(V,\preceq)$.
  • Figure 2: Examples of structure-creating processes $S$ and their representation as a causal graph. $I(S)$ is the input, $O(S)$ the output, and $T(S)$ the set of template structures.