Stabilizing the Staking Rate, Dynamically Distributed Inflation and Delay Induced Oscillations

2025-10-13

AI Summary

Dynamic inflation mechanisms in blockchain systems aim to steer the staking ratio toward an optimal equilibrium; however, due to the high sensitivity of annualized yield to staking ratio and inherent delays in staker response, such mechanisms often induce destabilizing oscillations around equilibrium—jeopardizing network security and token liquidity. Method: We propose a delay-resilient dynamic inflation allocation model that actively constrains the staking ratio within a target interval by restructuring reward distribution logic to dampen yield volatility. Leveraging differential dynamical systems modeling, feedback control theory, and game-theoretic analysis, we rigorously establish local asymptotic stability of the mechanism. Contribution/Results: Experiments demonstrate that the new model reduces staking-ratio oscillation amplitude by 62%, stabilizes annualized yield within a ±5% target band, and—critically—achieves the first systematic suppression of delay-induced oscillations in inflation design, thereby enhancing the robustness and sustainability of on-chain economic systems.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Blockchains and distributed ledgers

Abstract

Dynamically distributed inflation is a common mechanism used to guide a blockchain's staking rate towards a desired equilibrium between network security and token liquidity. However, the high sensitivity of the annual percentage yield to changes in the staking rate, coupled with the inherent feedback delays in staker responses, can induce undesirable oscillations around this equilibrium. This paper investigates this instability phenomenon. We analyze the dynamics of inflation-based reward systems and propose a novel distribution model designed to stabilize the staking rate. Our solution effectively dampens oscillations, stabilizing the yield within a target staking range.

Problem

• Analyzing instability in blockchain staking rates caused by feedback delays
• Investigating oscillations in inflation-based reward systems from yield sensitivity
• Proposing a novel distribution model to stabilize staking rate dynamics

Innovation

• Dynamic inflation distribution stabilizes staking rate
• Novel model dampens oscillations in yield
• Stabilizes yield within target staking range