Contextual Theory Systems: From Paper-Bound Knowledge to Callable Scientific Theory

Abstract

Scientific judgment about physical systems that available theories and models describe only partly usually depends on human interpretation and reasoning. Communication delays, inaccessible environments or time-critical streams of observations can prevent that human inquiry from being completed when it is needed. If an inquiry needs a new explanatory account before people can supply it, a computational system must undertake that work, coordinating observation, construction, testing and use. When communication delay also rules out timely remote computation, that capability must be local. Space exploration illustrates the demand. Newtonian mechanics can suffice for low-speed motion in weak gravitational fields, while relativistic effects can require a broader description. These choices concern applicability and required accuracy; Newtonian predictions remain useful within their appropriate regime. Observations beyond the reliable reach of available descriptions can require new variables, relations or organizing principles. We propose contextual theory systems to connect such development with the evidence needed to use and revise its results. Their standard is contextual adequacy: sufficient support for the explanation, prediction or decision at hand, with room for later revision. Mathematical deductions remain reusable under stated assumptions as empirical descriptions evolve. A worked numerical example and a constructed discovery scenario show how these forms of support enter a continuing inquiry. Existing work on machine-checked proofs, automated discovery and autonomous observation supplies components; integration requires further engineering of formalization, evidence, reuse and response time. Scientific history and the constraints on timely human participation support the prediction that sustained civilization-scale exploration will require local theory-development infrastructure. Creating systems capable of extending scientific understanding can itself express human intellectual achievement, alongside recognition of the systems' actual contributions and human responsibility for their development and use.