A Control Theoretic Approach to Decentralized AI Economy Stabilization via Dynamic Buyback-and-Burn Mechanisms

2026-01-15

AI Summary

This study addresses the instability of token economies in decentralized AI networks, which often suffer from extreme price volatility due to inadequate responses from existing static or threshold-based buyback mechanisms—particularly during market downturns. To overcome these limitations, this work proposes a novel dynamic buyback-and-burn mechanism grounded in control theory, incorporating solvency constraints and employing a proportional-integral-derivative (PID) controller to establish a continuous feedback loop. This approach transforms rigid, rule-based policies into a closed-loop control system operating within structural constraints. Validated through Jump-Diffusion multi-agent simulations, the method reduces token price volatility by approximately 66% under high-volatility conditions and decreases operator attrition from 19.5% to 8.1%, substantially outperforming baseline strategies and fundamentally enhancing system resilience.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Online Learning & Bandits

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The democratization of artificial intelligence through decentralized networks represents a paradigm shift in computational provisioning, yet the long-term viability of these ecosystems is critically endangered by the extreme volatility of their native economic layers. Current tokenomic models, which predominantly rely on static or threshold-based buyback heuristics, are ill-equipped to handle complex system dynamics and often function pro-cyclically, exacerbating instability during market downturns. To bridge this gap, we propose the Dynamic-Control Buyback Mechanism (DCBM), a formalized control-theoretic framework that utilizes a Proportional-Integral-Derivative (PID) controller with strict solvency constraints to regulate the token economy as a dynamical system. Extensive agent-based simulations utilizing Jump-Diffusion processes demonstrate that DCBM fundamentally outperforms static baselines, reducing token price volatility by approximately 66% and lowering operator churn from 19.5% to 8.1% in high-volatility regimes. These findings establish that converting tokenomics from static rules into continuous, structurally constrained control loops is a necessary condition for secure and sustainable decentralized intelligence networks.

Problem

• decentralized AI economy
• tokenomic volatility
• economic stabilization
• buyback mechanisms
• system dynamics

Innovation

• Dynamic-Control Buyback Mechanism
• PID controller
• decentralized AI economy
• tokenomics
• agent-based simulation