Arithmetic Power Geometry for Public Policy: A Universal Structural Intelligence Framework for Evidence-Based Governance Using Indian State Finances

Evidence-based governance depends on fiscal indicators, rankings, audit findings, and econometric analysis, but these tools do not directly measure the internal structural balance of a policy system. This study develops Arithmetic Power Geometry (APG) as an interpretable structural intelligence layer for public policy. Using a machine-readable dataset of Indian State Finances for 28 states over 2013–14 to 2022–23, derived from Comptroller and Auditor General of India (CAG) state-finance material, the study transforms six fiscal variables into APG weights, entropy, concentration, diversity, and effective-dimension indicators. The selected variables are revenue receipts, revenue expenditure, capital expenditure, committed expenditure, outstanding debt, and outstanding guarantees. Results show that APG identifies balanced, moderately balanced, watch-list, and debt-dominated fiscal structures without replacing conventional public finance analysis. In 2022–23, Sikkim, Nagaland, Manipur, Meghalaya, Chhattisgarh, and Uttar Pradesh appear as the most structurally balanced cases, whereas Tamil Nadu, Haryana, West Bengal, and Punjab appear as debt-dominated risk cases under the defined APG thresholds. Robustness checks across alternative power normalizations show that the squared APG specification gives stronger dominance separation while preserving high rank association with nearby power choices. The central contribution is not another fiscal ranking; it is a reproducible policy-MRI framework that reveals how fiscal components jointly form a governance structure. APG is proposed as a transparent, complementary, and data-driven layer for fiscal resilience monitoring, comparative state-level governance analysis, and student-oriented public policy research.