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.

翻译：通过去中心化网络实现人工智能民主化代表了计算资源配置的范式转变，然而这些生态系统的长期存续能力正因其原生经济层的极端波动性而面临严重威胁。当前主要依赖静态或基于阈值的回购启发式方法的代币经济模型，难以应对复杂的系统动态，且常呈现顺周期性，在市场下行期间加剧不稳定性。为弥合这一差距，我们提出动态控制回购机制（DCBM），这是一个形式化的控制理论框架，利用具有严格偿付能力约束的比例-积分-微分（PID）控制器，将代币经济作为动态系统进行调控。基于跳跃-扩散过程的广泛智能体仿真表明，DCBM从根本上优于静态基准模型，在高波动性环境下将代币价格波动率降低约66%，并将运营商流失率从19.5%降至8.1%。这些研究结果证实，将代币经济从静态规则转化为连续、结构受限的控制循环，是构建安全可持续的去中心化智能网络的必要条件。