arXiv subjects

Davit Gondauri

Publications and source records attributed to Davit Gondauri.

11 recordsLinked to original sources

arXiv2026

The Agentic Economy: Humans, AI Agents, Robots, and the Measurable Transition toward Distributed Economic Action

This article develops the concept of the agentic economy and diagnoses its measurable preconditions: a transition in which economic action is increasingly distributed among humans, AI agents, industrial robots, executable protocols, compute infrastructures, and energy systems. The paper argues that classical categories such as labour, capital, firm, market, productivity, and trust remain necessary but incomplete when technologies prepare decisions, coordinate workflows, support tasks, verify transactions, and reshape responsibility. Methodologically, the study uses a conceptual-empirical quantitative diagnostic design rather than a causal econometric model. It relies on public institutional data on AI investment, AI adoption, robot installations and operational stock, data-centre electricity demand, and labour-market reallocation. The reported values are transformed through transparent indicators such as relative growth, CAGR, growth multipliers, stock-flow ratios, concentration ratios, and HHI. The results show that AI adoption is accelerating, AI investment signals broad capital allocation, industrial robots represent persistent cyber-physical action capacity, compute expansion increases data-centre electricity pressure, and labour projections are more consistent with task reallocation than labour disappearance. The article contributes an action-capacity framework linking model/software-agent capacity, robotic capacity, compute-energy coupling, protocolisation, auditable trust, and human sovereignty. It concludes that the agentic economy is not yet a completed global order, but its transition pressure is measurable enough to require a distinct economic vocabulary, reproducible diagnostics, and future sector-level measurement.

econ.EM↗

arXiv2026

A Diagnostics-First Composite Index for Macro-Financial Resilience to Socioeconomic Challenges: The Gondauri Index with Benchmarking and Scenario Evidence

In the face of socioeconomic challenges, this paper develops and empirically demonstrates the Gondauri Index (GI) as a reproducible diagnostics-first composite framework for benchmarking macro-financial resilience across heterogeneous economies on a unified 0-100 scale. The GI addresses a key limitation of conventional surveillance dashboards: resilience is multi-dimensional and only partially substitutable, so strength in one area cannot sustainably offset fragility in another. The index integrates three interpretable pillars: Inequality Resilience Score (IRS), Liquidity and Systemic Resilience (LNSR), and Inflation Forecast Coherence (IFC). Cross-country comparability is ensured through robust percentile normalization (p5-p95), a consistent annual country-year design, and explicit missing-data handling via component-level weight renormalization. Empirically, the paper provides a 2024 benchmark snapshot and dynamic evidence for 2005-2024 using 5-year rolling diagnostics and Delta log(GI) contribution decomposition, allowing transparent attribution of resilience changes to pillar-level drivers. A forward-looking extension constructs 2026-2030 scenario pathways and introduces a binding-pillar diagnostic that identifies the dominant constraint on resilience across horizons. Overall, the GI offers a scalable tool for comparative resilience assessment, early-warning diagnostics, and evidence-based policy sequencing.

econ.EM↗

arXiv2026

P vs NP Problem in Portfolio Optimization: Integrating the Markowitz-CAPM Framework with Cardinality Constraints and Black-Scholes Derivative Pricing

This paper makes the Millennium Prize problem P vs NP operational in quantitative finance by studying cardinality-constrained portfolio selection. Starting from the convex Markowitz mean-variance program with CAPM-based expected returns (Rf plus beta times ERP), we impose a hard sparsity rule that limits the portfolio to K assets out of approximately 94 industry portfolios (Damodaran). The constraint couples discrete subset selection with continuous weight optimization, yielding a mixed-integer quadratic program and an NP-hard search space that grows combinatorially with n and K. We therefore evaluate scalable approximation schemes (greedy screening, Monte Carlo sampling, and genetic algorithms) under a replication-oriented protocol with random-seed control, distributional performance summaries (median and quantiles), runtime profiling, and convergence diagnostics. Dependence structure is documented via correlation and covariance diagnostics and positive-semidefinite checks to link algorithm behavior to the geometry implied by the risk matrix. To support the title's derivatives component, we add a European call option priced by the Black-Scholes model and map it into CAPM-consistent moments using delta-based linearization, validated with a bump test and moneyness/maturity sensitivity. Results highlight how the cardinality constraint reshapes the attainable efficient frontier, why stability and computational-cost trade-offs matter more than single-best runs, and how common-factor dependence can limit diversification in K-sparse solutions. The study provides a reproducible template for NP-hard portfolio optimization with transparent inputs and extensible derivative overlays.

econ.EM↗

arXiv2025

Forecasting Inflation Based on Hybrid Integration of the Riemann Zeta Function and the FPAS Model (FPAS + $\zeta$): Cyclical Flexibility, Socio-Economic Challenges and Shocks, and Comparative Analysis of Models

Inflation forecasting is a core socio-economic challenge in modern macroeconomic modeling, especially when cyclical, structural, and shock factors act simultaneously. Traditional systems such as FPAS and ARIMA often struggle with cyclical asymmetry and unexpected fluctuations. This study proposes a hybrid framework (FPAS + $\zeta$) that integrates a structural macro model (FPAS) with cyclical components derived from the Riemann zeta function $\zeta(1/2 + i t)$. Using Georgia's macro data (2005-2024), a nonlinear argument $t$ is constructed from core variables (e.g., GDP, M3, policy rate), and the hybrid forecast is calibrated by minimizing RMSE via a modulation coefficient $\alpha$. Fourier-based spectral analysis and a Hidden Markov Model (HMM) are employed for cycle/phase identification, and a multi-criteria AHP-TOPSIS scheme compares FPAS, FPAS + $\zeta$, and ARIMA. Results show lower RMSE and superior cyclical responsiveness for FPAS + $\zeta$, along with early-warning capability for shocks and regime shifts, indicating practical value for policy institutions.

econ.EM↗

arXiv2025

Increasing Systemic Resilience to Socioeconomic Challenges: Modeling the Dynamics of Liquidity Flows and Systemic Risks Using Navier-Stokes Equations

Modern economic systems face unprecedented socioeconomic challenges, making systemic resilience and effective liquidity flow management essential. Traditional models such as CAPM, VaR, and GARCH often fail to reflect real market fluctuations and extreme events. This study develops and validates an innovative mathematical model based on the Navier-Stokes equations, aimed at the quantitative assessment, forecasting, and simulation of liquidity flows and systemic risks. The model incorporates 13 macroeconomic and financial parameters, including liquidity velocity, market pressure, internal stress, stochastic fluctuations, and risk premiums, all based on real data and formally included in the modified equation. The methodology employs econometric testing, Fourier analysis, stochastic simulation, and AI-based calibration to enable dynamic testing and forecasting. Simulation-based sensitivity analysis evaluates the impact of parameter changes on financial balance. The model is empirically tested using Georgian macroeconomic and financial data from 2010-2024, including GDP, inflation, the Gini index, CDS spreads, and LCR metrics. Results show that the model effectively describes liquidity dynamics, systemic risk, and extreme scenarios, while also offering a robust framework for multifactorial analysis, crisis prediction, and countercyclical policy planning.

econ.GN↗

arXiv2025

Gauging Growth: AGI Mathematical Metrics for Economic Progress

Today, the economy is greatly influenced by Artificial General Intelligence (AGI). The purpose of this paper is to determine the impact of the quantitative relations of AGI on the country's economic parameters. The authors use the analysis of historical data in the research, develop a new mathematical algorithm that refers to the level of AGI development, and conduct a regression analysis. The economic effect of AGI is deduced if it affects the growth of real GDP. As a result of the analysis, it is revealed that there is a positive Pearson correlation between the growth of AGI and real GDP; that is, to increase GDP by 1%, an average increase of 12.5% of AGI is required.

q-fin.GN↗

arXiv2025

Development of Railway Silk Road as a Platform for Promoting Georgias Economic Growth

The given paper emphasizes the importance of the Railway Silk Road for promoting Georgia's economic growth and development. The article notes that economic integration in the region increases cargo turnover in Central Asia and the Caucasus, thus boosting the volume of goods transported through Georgia and contributing to the sustainability of Georgia's macroeconomic and economic growth. The financial economic models aim to identify causal links between the sensitivity of railway cargo and the country's economic growth. The main task of the research was to use the Railway EVA and the Georgian economy to create a cargo sensitivity relationship between CAGR models. The paper analyzes key scientific problems regarding railway freight transportation studies. Calculations are provided for the share of the Railway System in the country's GDP for 2006-2017, as well as the average annual geometric (CAGR) growth of cargo volumes over a 16-year cycle, allowing Georgian Railway JSC to generate additional value in the country's overall GDP. The research shows that the added value to GDP comes in direct and indirect forms through the development and growth of various sectors of Georgia's economy, as some of the cargo shipped by railway remains in Georgia and is used in production, thereby adding value to the country's economic growth. The use of this model by foreign research centers also provides further opportunities for the economic growth of their countries.

econ.GN↗

arXiv2025

The Impact of Artificial Intelligence on Gross Domestic Product: A Global Analysis

This research paper explores the impact of Artificial intelligence (AI) on the global economy, with particular emphasis on its influence on gross domestic product (GDP). The paper begins with an overview of AI, followed by a discussion of its potential benefits and Drawbacks of economic growth. Next, the The paper examines empirical evidence and case studies to Analyze the relationship between AI adoption and GDP growth across different countries and regions. Finally, The paper concludes by providing policy Recommendations for governments seeking to harness The potential of AI to foster economic growth.

econ.GN↗

arXiv2025

Deciphering the AI Economy: A Mathematical Model Perspective

The economy in the modern world is greatly influenced by artificial intelligence (AI). This paper aims to determine the impact of AI quantitative relationships on the country's economic parameters, including GDP per Capita. Historical data analysis is used in the research. A new mathematical algorithm for the magnitude of a technological level and AI factors vector has been developed. The study calculated the economic effect of AI on GDP per Capita. As a result of the analysis, it was revealed that there is a positive Pearson correlation between growth. On AI and GDP per Capita, that is, to increase GDP per Capita by 1%, an average increase of 23.9% in AI is required.

econ.GN↗

arXiv2025

The Impact of Socio-Economic Challenges and Technological Progress on Economic Inequality: An Estimation with the Perelman Model and Ricci Flow Methods

The article examines the impact of 16 key parameters of the Georgian economy on economic inequality, using the Perelman model and Ricci flow mathematical methods. The study aims to conduct a deep analysis of the impact of socio-economic challenges and technological progress on the dynamics of the Gini coefficient. The article examines the following parameters: income distribution, productivity (GDP per hour), unemployment rate, investment rate, inflation rate, migration (net negative), education level, social mobility, trade infrastructure, capital flows, innovative activities, access to healthcare, fiscal policy (budget deficit), international trade (turnover relative to GDP), social protection programs, and technological access. The results of the study confirm that technological innovations and social protection programs have a positive impact on reducing inequality. Productivity growth, improving the quality of education, and strengthening R&D investments increase the possibility of inclusive development. Sensitivity analysis shows that social mobility and infrastructure are important factors that affect economic stability. The accuracy of the model is confirmed by high R^2 values (80-90%) and the statistical reliability of the Z-statistic (<0.05). The study uses Ricci flow methods, which allow for a geometric analysis of the transformation of economic parameters in time and space. Recommendations include the strategic introduction of technological progress, the expansion of social protection programs, improving the quality of education, and encouraging international trade, which will contribute to economic sustainability and reduce inequality. The article highlights multifaceted approaches that combine technological innovation and responses to socio-economic challenges to ensure sustainable and inclusive economic development.

econ.EM↗

arXiv2024

Impact of R&D and AI Investments on Economic Growth and Credit Rating

The research and development (R&D) phase is essential for fostering innovation and aligns with long-term strategies in both public and private sectors. This study addresses two primary research questions: (1) assessing the relationship between R&D investments and GDP through regression analysis, and (2) estimating the economic value added (EVA) that Georgia must generate to progress from a BB to a BBB credit rating. Using World Bank data from 2014-2022, this analysis found that increasing R&D, with an emphasis on AI, by 30-35% has a measurable impact on GDP. Regression results reveal a coefficient of 7.02%, indicating a 10% increase in R&D leads to a 0.70% GDP rise, with an 81.1% determination coefficient and a strong 90.1% correlation. Georgia's EVA model was calculated to determine the additional value needed for a BBB rating, comparing indicators from Greece, Hungary, India, and Kazakhstan as benchmarks. Key economic indicators considered were nominal GDP, GDP per capita, real GDP growth, and fiscal indicators (government balance/GDP, debt/GDP). The EVA model projects that to achieve a BBB rating within nine years, Georgia requires $61.7 billion in investments. Utilizing EVA and comprehensive economic indicators will support informed decision-making and enhance the analysis of Georgia's economic trajectory.

econ.EM↗