Summary

This post provides technical definition of knowledge grounded in Information Theory and Physics of Agency framework, addressing traditional “justified true belief” problems including Gettier cases. Knowledge is pattern-encoded information within agent’s predictive structure reliably and quantifiably reducing Shannon entropy (uncertainty) regarding future events/states across quantum-branching timelines. Three components: (1) Pattern Identifier—reproducible structure (neural configuration, logical rule, algorithm, cultural convention) enabling consistent predictions; (2) Reliability—PI consistently reduces uncertainty across scenarios; (3) Entropy Reduction—quantifiable Shannon entropy decrease in bits, providing objective predictive capability metric. Definition tested against scenarios: weather forecasts (knowledge), false beliefs (fail—no reliable entropy reduction), Gettier problems (fail—no reliable predictability), quantum uncertainty (epistemic vs. ontic distinction), random guesses (fail), tacit knowledge/skills (pass), cultural knowledge (pass), conditional knowledge (context-dependent pass), QBU branching knowledge (pass), raw data without agency (fail). Integrates with Conditionalist and QBU frameworks, emphasizing agent-centricity, measurability, practical action-facilitation.

Key Concepts

  • Pattern-encoded information – Knowledge as reproducible structure within agent enabling predictions, not isolated beliefs.
  • Shannon entropy reduction – Quantifiable uncertainty decrease in bits as objective knowledge metric.
  • Reliability criterion – Consistent entropy reduction across scenarios validates knowledge status.
  • Agent-centricity – Knowledge requires agent’s predictive structure; raw data insufficient.
  • Epistemic vs. ontic uncertainty – Incomplete agent knowledge versus irreducible quantum-mechanical branching.
  • Gettier resolution – Stopped clock scenario fails reliability criterion despite justified true belief.
  • Quantum Branching Universe integration – Knowledge enables steering choices toward favorable branching outcomes.

Evolution Notes

  • Continues “What Counts as X” series with most technically sophisticated definition yet.
  • Integrates multiple earlier frameworks: Physics of Agency, Conditionalism, QBU, information theory.
  • Demonstrates cumulative theory-building: later work leveraging earlier conceptual foundations.
  • Provides formal alternative to traditional analytic epistemology using computational/physical concepts.
  • Emphasizes measurability and operationalization—knowledge assessable via entropy calculations.
  • Reflects Axio’s scientific naturalism: philosophical concepts grounded in physics/information theory.
  • Tacit knowledge inclusion challenges traditional propositional-only epistemology.

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Cross-References

Open Questions

  • How do we measure Shannon entropy reduction for non-formalized knowledge (intuitions, aesthetic judgments)?
  • Does the definition exclude knowledge of necessary truths (mathematical, logical) that don’t reduce contingent uncertainty?
  • Can collective/distributed knowledge exist, or only individual agents possess knowledge?
  • How does this framework handle self-knowledge and metacognition (knowledge about one’s own knowledge)?
  • Does the agent-centricity criterion anthropomorphize knowledge unacceptably, excluding “objective” facts?
  • How do we reconcile measurable entropy reduction with phenomenological aspects of knowing?
  • Can the definition handle knowledge of singular past events that don’t enable future predictions?