  Objective: This paper proposes a framework to support the scientific research
of standards so that they can be better measured, evaluated, and designed.
Methods: Beginning with the notion of common models, the framework describes
the general standard problem - the seeming impossibility of creating a
singular, persistent and definitive standard which is not subject to change
over time in an open system. Results: The standard problem arises from
uncertainty driven by variations in operating context, standard quality,
differences in implementation, and drift over time. As a result, fitting work
using conformance services is needed to repair these gaps between a standard
and what is required for real-world use. To guide standards design and repair,
a framework for measuring performance in context is suggested, based on signal
detection theory and technomarkers. Based on the type of common model in
operation, different conformance strategies are identified: (a) Universal
conformance (all agents access the same standard); (b) Mediated conformance (an
interoperability layer supports heterogeneous agents) and (c) Localized
conformance (autonomous adaptive agents manage their own needs). Conformance
methods include incremental design, modular design, adaptors, and creating
interactive and adaptive agents. Discussion: Machine learning should have a
major role in adaptive fitting. Research to guide the choice and design of
conformance services may focus on the stability and homogeneity of shared
tasks, and whether common models are shared ahead of time or adjusted at task
time. Conclusion: This analysis conceptually decouples interoperability and
standardization. While standards facilitate interoperability, interoperability
is achievable without standardization.
