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This study introduces a formal theory of semantic information, defining it as causally necessary information for a system

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Area of Science:

  • Information Theory
  • Statistical Physics
  • Systems Biology

Background:

  • Syntactic information measures statistical correlations, but lacks 'meaning'.
  • Semantic information, crucial in biology and philosophy, lacks a formal theory.
  • Existing theories struggle with broad applicability and formal definitions.

Purpose of the Study:

  • Introduce a formal, broadly applicable theory of semantic information.
  • Define semantic information based on causal necessity for system existence.
  • Analyze semantic information using non-equilibrium statistical physics and thermodynamics.

Main Methods:

  • Define semantic information as causally necessary syntactic information for system self-preservation.
  • Operationalize 'causal necessity' via counterfactual interventions.
  • Define 'maintaining existence' through low-entropy state preservation.
  • Apply non-equilibrium statistical physics for thermodynamic analysis.

Main Results:

  • Formalized semantic information for any physical system.
  • Provided thermodynamic perspective on semantic information.
  • Established formal definitions for 'value of information', 'semantic content', and 'agency'.

Conclusions:

  • The proposed theory offers a unified framework for semantic information.
  • Applicable to diverse physical systems, bridging information theory and thermodynamics.
  • Enables formal analysis of concepts like agency and information value.