Addressable theory

Type: types/definition.md · Tags: self-improving-systems, learning-theory, theory-builder

An addressable theory is a theory formulated in language whose assumptions, scope conditions, and parts can be inspected and revised individually. A failure can then guide a search over candidate parts, and an edit can change selected parts while the rest stays in place.

The KB needs the term to keep a structural property apart from an epistemic status. Every theory the KB retains is a tentative theory; only some are addressable. A theory builder requires only the minimum, localized content: some unit carries what the theory says. Finer grades are Commonplace's design commitment, not a membership condition. The expected benefit is that criticism can name a part, so a revision keeps what still works while changing what failed; whether that benefit arrives is an empirical question.

Scope

  • It comes in degrees. A theory with separately stated assumptions and scope conditions is more addressable than an undivided document. Whole replacement can still change a theory's scope; addressability lets the revision target that scope separately.
  • It follows the localization axis. Of the two axes that derive representational form, localization supplies addressability: natural-language and symbolic artifacts have parts to point at, and distributed-parametric state has none. See reflection buys addressability.
  • How strongly a case can contradict the theory is a separate matter. It follows the other axis, assigned consequences. Where a defined consumer such as a validator, schema, or test fixes what a part implies, a contradiction can be mechanically checked relative to that encoding. Whether the encoding captures the intended claim remains criticizable. Where a reader derives the implication from prose, that interpretation also enters the diagnosis. See codification.
  • A located part is a candidate. A failure rarely identifies one faulty commitment. Popper notes that a test may bear on a large part of a theoretical system, while holding that some cases do identify the responsible hypotheses (Conjectures and Refutations, Chapter 10, section XVI). Unchanged text also does not guarantee unchanged consequences; a revision is checked against the failure and against other cases.

Precedent

The classical theory-refinement systems of machine learning, EITHER and FORTE, are the fully addressable case with computed consequences: a Horn-clause rule set, a proof procedure, and named repair operators. They show that, in such a setting, proof traces can connect a discrepancy to candidate repair locations. They do not show unique fault identification, and FORTE's search can stop before its training cases are consistent. The KB cites them as a precedent for repair under addressability. It takes neither its paradigm nor a preference for minimal revision from them.

Exclusions

  • A latent world model as such. It is non-localized, so it is revised without separately stated assumptions or claims to target. An inspectable causal model or simulator program can expose those parts and be both a world model and an addressable theory.
  • Storage location. A theory kept in a file is not thereby addressable, and addressability does not require a particular store. Traces with an index locating conjectures, their parts, and their testing record can implement the same addressable objects as separate theory documents.

Misuse Cases

  • Reading mechanically checked consequences into a prose part. For a natural-language part, "derived" means interpreted until the part is codified.
  • Treating a successful local edit as confirmation of the theory that motivated it.

Relevant Notes: