Title:
Macro Tokenomics Simulation Methods in Ai-based Forecasting

Authors:
Shakirova Gulnara Amangeldievna, Kazakhstan

Abstract:

The rapid expansion of tokenized assets and their growing entanglement with global macroeconomic dynamics have created a methodological gap between traditional macroeconomic modeling and digital asset analytics. Tatyana Krestnikova addresses this gap through a coherent research program that combines macroeconometric reasoning, machine learning and simulation tools under the umbrella of macro tokenomics. Building on her monographs and patent work, she proposes an AI-enhanced macro tokenomics simulator that links macroeconomic scenarios to digital asset market outcomes and extends earlier platforms for AI-based risk analytics of token portfolios. The simulator treats macro variables as primary exogenous drivers and generates forecasts for aggregate crypto indicators such as prices, volatility and market capitalization. It integrates non linear AI forecasting models with agent based market microstructure simulation and scenario generation, including large Monte Carlo ensembles. This article systematizes Krestnikova’s contribution, reconstructs the core design principles of her simulation architecture and examines how her methods handle issues of regime shifts, instability and feedback between macro factors and token markets. The analysis shows that her approach provides a structured way to isolate macro driven effects on digital assets, improves out of sample forecast performance relative to linear benchmarks and supports policy oriented stress testing of crypto markets.

DOI: https://doi.org/10.51505/IJEBMR.2026.1204