The Empirical Pattern-Reality Gap Framework (EPRGF)
Description
1 Abstract
Scientific inquiry frequently moves from repeated observations to patterns, from patterns to
predictive models, and from models to explanations of the processes that produce observed
phenomena. These transitions are related but logically distinguishable. A regularity can be
reproducible without its generating mechanism being fully identified; a model can predict
accurately within a specified domain without being the only model compatible with the
evidence; and a successful explanation need not establish every feature of an underlying
ontology.
The Empirical Pattern-Reality Gap Framework (EPRGF) is proposed as an integrated research
framework for analyzing these distinctions. Its central concern is the inferential distance
between observed regularity, justified regularity, predictive and explanatory adequacy, and
ontological interpretation. The framework combines methodological ideas from measurement
theory, probability and statistics, philosophy of science, cognitive science, causal inference,
cybernetics, complex systems, and metascience.
The proposed architecture contains twelve analytical layers organized into three broad tasks:
establishing empirical evidence, constructing and evaluating models, and assessing epistemic
and ontological claims. Eight complementary research domains specify the framework's interdis-
ciplinary scope. Eight methodological principles articulate its initial conceptual commitments,
while six research hypotheses identify candidate questions for empirical investigation. A sixteen-
step research protocol connects literature review, operationalization, formal specification, model
comparison, falsification, validation, and synthesis.
The framework does not assume that reality is unknowable, nor does it equate incomplete
evidence with permanent ignorance. Instead, it distinguishes the strength of empirical support
from the strength of claims about mechanisms and ontology. Its principal contribution, if
subsequently established, would be a coherent and operationally useful synthesis of these
inferential distinctions.
Keywords: empirical regularity; induction; scientific models; epistemic justification; causal
inference; measurement uncertainty; model underdetermination; scientific realism; falsification;
metascience.
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Additional details
References
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