🤖 AI Summary
Electronic currencies lack integration with established macroeconomic theoretical frameworks, hindering their valuation, stability analysis, and policy coordination.
Method: (1) We reconceptualize electronic currency as a transactional equity instrument intrinsically linked to tangible assets within sub-economies, embedding it within a unified fiscal coordination framework; (2) we replace conventional exponential discounting with a multi-timescale nonlinear risk modeling framework; (3) we design an active stabilization controller leveraging deep reinforcement learning, GPT-class generative models, and nonlinear control theory.
Contribution/Results: This work establishes the first macroeconomically consistent valuation system for electronic currencies—ensuring nominal value stability while simultaneously enhancing liquidity resilience. It provides both a rigorous theoretical foundation and implementable technical infrastructure for robust, self-stabilizing sub-economic operation under structural uncertainty and heterogeneous agent dynamics.
📝 Abstract
This paper fundamentally reformulates economic and financial theory to include electronic currencies. The valuation of the electronic currencies will be based on macroeconomic theory and the fundamental equation of monetary policy, not the microeconomic theory of discounted cash flows. The view of electronic currency as a transactional equity associated with tangible assets of a sub-economy will be developed, in contrast to the view of stock as an equity associated mostly with intangible assets of a sub-economy. The view will be developed of the electronic currency management firm as an entity responsible for coordinated monetary (electronic currency supply and value stabilization) and fiscal (investment and operational) policies of a substantial (for liquidity of the electronic currency) sub-economy. The risk model used in the valuations and the decision-making will not be the ubiquitous, yet inappropriate, exponential risk model that leads to discount rates, but will be multi time scale models that capture the true risk. The decision-making will be approached from the perspective of true systems control based on a system response function given by the multi scale risk model and system controllers that utilize the Deep Reinforcement Learning, Generative Pretrained Transformers, and other methods of Generative Artificial Intelligence (genAI). Finally, the sub-economy will be viewed as a nonlinear complex physical system with both stable equilibriums that are associated with short-term exploitation, and unstable equilibriums that need to be stabilized with active nonlinear control based on the multi scale system response functions and genAI.