Fiscal Geometry as Representational Infrastructure for AI-Assisted Institutional Analysis

This technical white paper introduces Fiscal Geometry (FG) as a representational infrastructure for AI-assisted institutional analysis in digitized, document-dense environments. Contemporary institutions generate large volumes of narrative artefacts—policies, budgets, disclosures, compliance manuals—but lack a stable representation layer that enables repeatable comparison, auditability, and structured analysis prior to computation.

FG is presented as an interface layer that renders narrative institutional materials into geometry-ready analytical objects without performing evaluation, scoring, or decision-making. The paper specifies a minimal object schema (events, interfaces, movements), evidence-chain requirements, and hard separation rules that distinguish representation from interpretation and evaluation. It further defines the appropriate division of labor between human judgment and AI-assisted tools, explicitly constraining automation to operate only on structured, traceable inputs.

Applied technical examples illustrate how FG renders structural patterns in cross-border tax coordination, intergenerational family office structures, and domestic education finance systems, without naming or assessing specific institutions. Across these contexts, FG functions as enabling infrastructure: it does not explain outcomes, infer causality, or assign normative meaning, but stabilizes institutional structure so that analysis—human or tool-assisted—can proceed coherently.

The contribution of this paper lies in formalizing representation as a prerequisite for computation and automation, positioning Fiscal Geometry as infrastructure rather than a method, model, or metric.