This article reframes pricing as a negotiation rather than a prediction, showing how price emerges from tensions between product reality, market dynamics, and buyer behavior. It introduces negotiation-aware ML, value decomposition, and equilibrium modeling to build transparent, human-aligned pricing systems.

This article explores the theory behind explainable car pricing using value decomposition, showing how machine learning models can break a predicted price into intuitive components such as brand premium, age depreciation, mileage influence, condition effects, and transmission or fuel-type adjustments.

A scientific exploration of Metabolic Tokenomics, the framework that models token economies as living metabolic systems. Examines how tokens burn, store, and circulate energy like biological cells, mapping catabolism, anabolism, liquidity flow, yield dynamics, and system health to design resilient, adaptive on-chain ecosystems.

A deep exploration of the economic physics governing DeFi crashes, AMM decay, liquidity spirals, and liquidation cascades. This article models decentralized finance as a nonlinear system driven by invariants, thresholds, and feedback loops, revealing why crashes follow predictable laws of motion.

A deep research study introducing the concept of Economic DNA Repair for smart contracts, designing self-correcting tokenomics that detect anomalies, repair unstable parameters, rebalance incentives, and restore economic equilibrium. Explores adaptive rewards, automated governance, liquidity healing, and resilience in decentralized systems.

An experimental ERC20 token implementing adaptive tokenomics through an auto-adjusting transfer tax. The contract monitors recent transfer volume in a configurable time window and dynamically increases or decreases fees based on market activity. A clean, dependency-free demonstration of self-regulating, volume-responsive economic design.

A model which simulates coastal flooding, and evaluated the economic benefits of climate adaptation strategies.

An interpretable system that models the future of work as an equilibrium under AI-driven forces. Instead of predicting job loss, it decomposes workforce disruption into automation pressure, adaptability, skill transferability, demand, and AI augmentation to explain stability, tension, and transition paths by 2030.

A Python workflow implementing the classical IS–LM framework, allowing you to compute equilibrium output and interest rates, simulate fiscal (G) and monetary (M) shocks, and visualize results across the goods, money, and aggregate demand–supply markets.

Open repository for collaborative research on economic models, integrating theory, simulations, and data. Contribute papers, code, and datasets to advance understanding of economic modeling and policy analysis.

End-to-End Python framework implementing bias-adjusted LLM agents for human-like decision-making in economic games (Kitadai et al., 2025). Features persona-conditioned agent populations using Econographics data, multi-provider API integration, and Wasserstein distance validation against empirical benchmarks.

🚗 Decode car values using a transparent machine learning system that enhances price understanding through explainable methods.

NDA-safe excerpts of math & economics modeling tasks for LLM reasoning evaluation and numerical verification.

Detect U.S. housing market bubbles using macroeconomic signals. Forecast HPI, score speculative risk, and visualize insights using a fully modular, cloud-native GCP pipeline.

End-to-End Python implementation of the research methodology, from "Geometric Dynamics of Consumer Credit Cycles", by Sudjianto & Setiawan (2025). Implements Clifford Algebra embeddings and Linear Attention for explanatory macroeconomic analysis; i.e. economic regime analysis.

🤝 Explore negotiation-driven pricing with a simulation engine that applies behavioral economics for smarter, real-world pricing strategies.

⚡ Innovate on-chain systems that mimic living cells to efficiently burn, store, and use energy, enhancing decentralized finance and token metabolism.

A Python script using Plotly to analyze and visualize the economic impact and deadweight loss of the Section 232 steel tariffs.

End-to-End quantitative (Python) decision support system for optimizing economic resilience against disasters. Implements updated MRIA model using multi-regional supply-use tables, three-step optimization algorithm, and comprehensive impact assessment to identify vulnerabilities from production concentration and logistical constraints.

🔍 Explore DeFi through a physical lens, modeling crashes, liquidations, and AMM decay with mathematical precision for deeper insights into its dynamics.