Gondauri Index (GI): Methodology for a Clay Millennium-Problems–Driven Macro-Financial Index

The Gondauri Index (GI) is introduced as a novel macro-financial composite index grounded in the Clay Millennium Problems and applied to economic modeling. It integrates three sub-indices: the Inequality–Ricci Subindex (IRS), measuring income distribution stability through Ricci flow dynamics; the Liquidity–Navier–Stokes Resilience (LNSR), capturing systemic robustness via fluid dynamics analogies; and the Inflation FPAS+ζ Credibility (IFC), enhancing inflation forecasting through hybrid FPAS–Riemann zeta methods. Each subindex is normalized on a 0–100 scale, with the final GI computed as a weighted geometric mean (35% IRS, 35% LNSR, 30% IFC). The methodology combines statistical calibration, normalization, and error-reduction benchmarks, ensuring reliability and policy applicability. The GI provides a consolidated, forward-looking metric for evaluating inequality, financial stability, and inflation expectations, offering policymakers and researchers a robust tool for decision-making in complex socio-economic environments.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-18
DOI
https://doi.org/10.5281/zenodo.17202057
Primary Topic
Global Public Health Policies and Epidemiology
Type
article
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article

Gondauri Index (GI): Methodology for a Clay Millennium-Problems–Driven Macro-Financial Index

Zenodo (CERN European Organization for Nuclear Research)
Global Public Health Policies and Epidemiology
article

Gondauri Index (GI): Methodology for a Clay Millennium-Problems–Driven Macro-Financial Index

article en

Abstract

The Gondauri Index (GI) is introduced as a novel macro-financial composite index grounded in the Clay Millennium Problems and applied to economic modeling. It integrates three sub-indices: the Inequality–Ricci Subindex (IRS), measuring income distribution stability through Ricci flow dynamics; the Liquidity–Navier–Stokes Resilience (LNSR), capturing systemic robustness via fluid dynamics analogies; and the Inflation FPAS+ζ Credibility (IFC), enhancing inflation forecasting through hybrid FPAS–Riemann zeta methods. Each subindex is normalized on a 0–100 scale, with the final GI computed as a weighted geometric mean (35% IRS, 35% LNSR, 30% IFC). The methodology combines statistical calibration, normalization, and error-reduction benchmarks, ensuring reliability and policy applicability. The GI provides a consolidated, forward-looking metric for evaluating inequality, financial stability, and inflation expectations, offering policymakers and researchers a robust tool for decision-making in complex socio-economic environments.

Zenodo (CERN European Organization for Nuclear Research)
Business and Technology University
Openalex Percentile: Top 39%
Global Public Health Policies and Epidemiology
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