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

TL;DR

This paper introduces a control-theoretic framework called DCBM that uses PID controllers to stabilize decentralized AI token economies, significantly reducing volatility and operator churn in simulations.

Contribution

The paper presents the Dynamic-Control Buyback Mechanism (DCBM), a novel control-theoretic approach to regulate token economies in decentralized AI networks.

Findings

Reduces token price volatility by approximately 66%.

Lowers operator churn from 19.5% to 8.1% in high-volatility regimes.

Demonstrates superiority over static buyback heuristics in agent-based simulations.

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.