Modeling the Potential Effects of Historically Formed Relations in the Generative Relational Metamodel
Year: 2026
Type: article
Abstract: A historically formed relation can retain a potential role in subsequent activity even when its effect remains unrealized. This paper develops a conditional account of that possibility within the generative relational metamodel. It distinguishes candidate histories, retained records, historical queries, and prospective observations under declared continuations. Exact query recovery is characterized by constancy on record fibres; compatible-answer sets represent incomplete recovery and its refinement. Conflict sets identify consistency-restoring retractions, while time-aligned filtering separates later evidence from changes to the past. A joint continuation record preserves both selected historical answers and future observations, with explicit update, finite-refinement, and minimality arguments. Counterexamples show why current-answer storage can omit needed memory and why a finite-valued query can require an unbounded exact record. Potential-effect tests distinguish current difference, dormant efficacy, blocked execution, and test-induced erasure. Under uncertain comparison pairs, possible efficacy, a witness for every compatible pair, and one common witness have different quantifier structures. A finite model demonstrates that sequential testing can destroy a distinction preserved by a family of fresh tests. An authorization-formation illustration separates predictive and historical records and examines a formation-sensitive review. A causal countermodel establishes the limits of observational discrimination for attribution. The philosophical discussion develops historical constitution, relational continuity, potentiality, and evidential responsibility under ontological suspension. The resulting framework coordinates historical and prospective inquiry while retaining their separate assumptions, evidence requirements, and revision conditions.
DOI: 10.17613/fz9js-gfg53
Author: HUANG, Wanhong
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212358586
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