Teleodynamic AI as resource-bounded learning

Introduces the co-evolution of structure, parameters, and resources as a research model and identifies the evidence required before using teleodynamic terminology for software.

Research
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Introduces the co-evolution of structure, parameters, and resources as a research model and identifies the evidence required before using teleodynamic terminology for software.

Engineering interpretation

Translate the theory into explicit state: a resource vector, allowed actions, costs, rewards, update cadence, structural state, and stop conditions. Without those fields, teleodynamic language remains metaphor.

Evidence questions

  • Which resources are endogenous to the control loop?
  • Which actions change structure versus parameters?
  • How are prediction benefit and complexity cost measured?
  • What trace proves a no-op or halt was selected for the documented reason?

Boundary

This topic does not assert consciousness, selfhood, biological equivalence, or an implemented teleodynamic runtime.

Scope

This starter page defines the questions, boundaries, evidence, and failure modes that should be recorded before a capability is presented as supported.

Engineering considerations

  • Identify the source, version, target environment, and owner.
  • Separate observed values from estimates and externally reported values.
  • Record trade-offs, unsupported cases, and fallback behavior.
  • Link performance statements to a compatible benchmark methodology.

Verification questions

  • What exact artifact, revision, backend, and environment were reviewed?
  • Which assumptions could change the result?
  • Which data should be retained so another engineer can reproduce the conclusion?