Ongoing Research in AI Evaluation, Economics, Financial Systems & Expert Systems
My research spans four interconnected domains: AI evaluation methodologies, economic systems analysis, quantitative financial modeling, and symbolic reasoning frameworks. Each study combines theoretical rigor with practical implementation, drawing from decades of experience in systems architecture and data analysis.
Click on any research area below to explore detailed methodologies, current findings, and practical applications. Each study represents months of investigation with real-world implications for policy, technology, and decision-making systems.
Comprehensive analysis revealing why the Turing Test has become obsolete for evaluating modern AI systems. This study examines empirical data showing GPT-4's 49.7% success rate versus humans' 66%, explores the academic consensus on evaluation limitations, and presents six revolutionary paradigms that have replaced conversational assessment in AI research and deployment.
Documents the field's decisive move away from conversational evaluation toward safety-first, capability-focused assessment. Reveals massive performance gaps on abstract reasoning (humans 75% vs. AI <5%) that conversational tests obscure, with direct implications for AI safety and regulation.
Empirical Analysis: Systematic review of 47 peer-reviewed studies from 2020-2024 examining Turing Test performance across multiple AI systems, revealing consistent patterns of diminishing diagnostic value as AI capabilities advance.
Benchmark Comparison: Comprehensive evaluation framework comparing conversational assessment against six modern paradigms: standardized benchmarking (MMLU, ARC), adversarial testing (red teaming), formal verification, psychometric evaluation, participatory assessment, and real-world impact measurement.
Statistical Framework: Multi-dimensional analysis incorporating performance metrics, safety considerations, scalability factors, and regulatory compliance requirements to establish evidence-based evaluation hierarchies.
View complete methodology with performance data at assets/ai-evaluation-methodology.pdf
Comprehensive analysis of expert systems for legal and regulatory text interpretation combining deterministic reasoning with domain expertise. This research evaluates hierarchical document parsing, formal rule engines, and knowledge representation for precise rule following in legal applications.
Bridges the gap between academic research and production implementation for legal AI. Provides actionable framework selection criteria and architectural patterns for building reliable, auditable expert systems that handle complex regulatory text with mathematical precision.
Hierarchical Structure Preservation: Advanced parsing methodologies that maintain constitutional articles → statutory chapters → regulatory sections → subsection rules hierarchy, preserving critical contextual information that flat NLP approaches lose.
Framework Evaluation: Comprehensive analysis of Drools vs. CLIPS vs. Prolog implementations, LKIF ontology integration, and enterprise-grade parsing solutions including DocParser and Neo4j for large-scale regulatory text processing.
Formal Verification: Mathematical consistency checking using Alloy and Z3 theorem provers to prevent contradictory requirements in production legal AI deployment.
Complete framework comparison and implementation guides available at assets/hierarchical-text-systems.pdf
Comprehensive scientific analysis revealing fundamental flaws in multi-currency basket stablecoins and proposing evidence-based commodity frameworks for true purchasing power preservation. Mathematical modeling demonstrates how proposed SDR-like baskets create "average inflation coins" rather than stable value storage.
The proposed multi-currency basket would create 4.62% annual purchasing power erosion. Scientific analysis of global inflation hedging performance demonstrates that commodities provide 7% real return gains per 1% inflation surprise, while currency baskets converge toward collective debasement during hyperinflationary scenarios.
Empirical Evidence: Goldman Sachs analysis of five major inflationary episodes showing commodities gained 7% per 1% inflation surprise while traditional assets declined, providing mathematical basis for commodity allocation strategies.
Market Structure Analysis: $254B stablecoin market facilitating $32T annually with USDT/USDC duopoly controlling 88.5% through network effects. Quantitative assessment of market entry barriers and regulatory compliance costs ($29M capital requirement for viable enterprise launch).
Technical Implementation: Oracle infrastructure analysis using Chainlink and Truflation for real-time inflation data, smart contract architecture for commodity basket rebalancing, and regulatory framework compliance across MiCA and US fragmented approach.
Complete mathematical models and implementation specifications at assets/stablecoin-mathematical-analysis.pdf
Developing comprehensive frameworks for building ontology-driven expert systems that can encode complex rule sets from games, legal standards, and regulatory compliance. This research focuses on creating deterministic, explainable AI systems that bridge symbolic reasoning with natural language interfaces.
Expert systems represent the future of trustworthy AI—deterministic, explainable, and auditable. Unlike black box models, these systems provide transparent reasoning chains essential for legal, medical, and regulatory applications.
Ontology Development: Systematic methodology for converting complex rule systems into machine-readable OWL ontologies, demonstrated through game rules (Flutter Stock Exchange) as proof-of-concept for legal and regulatory applications.
Rule Engine Integration: Comparative analysis of Drools, CLIPS, and Prolog for deterministic reasoning with complete audit trails. Framework selection criteria based on scalability, performance, and maintainability requirements.
Natural Language Interface: Bridging symbolic reasoning with conversational interaction through structured query translation, enabling domain experts to interact with formal systems without technical expertise.
Complete implementation framework and case studies at assets/expert-systems-framework.pdf
Comprehensive economic analysis examining global sovereign debt patterns, sustainability metrics, and potential restructuring scenarios. This study combines macroeconomic theory with quantitative modeling to assess systemic risks and policy implications across major economies.
With global debt reaching unprecedented levels, understanding restructuring mechanisms and systemic risks is critical for policymakers, investors, and economists. This research provides data-driven insights into one of the most pressing challenges of our time.
Multi-Factor Assessment: IMF-framework based analysis incorporating debt-to-GDP ratios, debt service burden, fiscal space evaluation, and external vulnerability metrics across G20 economies with historical crisis pattern recognition.
Stress Testing Methodology: Monte Carlo simulations modeling various economic scenarios including interest rate shocks, currency crises, and growth stagnation to assess debt sustainability under adverse conditions.
Policy Recommendation Engine: Evidence-based policy framework generation using historical precedent analysis, game theory modeling, and multi-stakeholder optimization to identify viable restructuring pathways.
Complete econometric models and policy analysis at assets/global-debt-analysis.pdf
Large-scale quantitative analysis of financial market data across multiple asset classes, exchanges, and timeframes. This research applies advanced statistical methods and machine learning techniques to identify patterns, inefficiencies, and algorithmic trading opportunities in complex financial systems.
Processing massive datasets reveals market microstructure patterns invisible to traditional analysis. This research combines decades of trading experience with cutting-edge data science to understand market behavior at scale.
Multi-Exchange Data Integration: Real-time data aggregation from 12+ exchanges processing tick-by-tick market data across multiple timeframes (microsecond to daily) with latency-optimized architecture for pattern recognition and arbitrage detection.
Statistical Pattern Recognition: Advanced time series analysis using GARCH models, cointegration testing, and machine learning algorithms to identify market inefficiencies, price dislocations, and mean reversion opportunities.
Risk Management Framework: Comprehensive position sizing, correlation analysis, and real-time risk monitoring with automated circuit breakers and portfolio optimization algorithms based on Kelly criterion and modern portfolio theory.
Complete system architecture and performance analysis at assets/trading-system-analysis.pdf
All research is grounded in empirical data analysis, using statistical methods to validate hypotheses and ensure reproducible results with comprehensive documentation of methodologies and sources.
Research includes working code implementations, allowing for verification, extension, and practical application of theoretical findings with full transparency in approach and execution.
Combines insights from computer science, economics, mathematics, cognitive science, and domain expertise to address complex real-world problems requiring interdisciplinary solutions.
Each study targets actionable insights that can inform policy decisions, system design, investment strategies, or technological development with measurable real-world impact.
These research areas represent ongoing investigations with significant practical implications. I'm open to collaboration with academic institutions, policy organizations, technology companies, and researchers working on similar challenges in AI evaluation, economic analysis, and intelligent systems.
Each study includes comprehensive documentation, reproducible methodologies, and open-source implementations where applicable. Contact me to discuss findings, methodologies, or potential collaboration opportunities in advancing these research domains.