Algorithmic Monetary Policies for Blockchain Participation Games
Abstract
A central challenge in blockchain tokenomics is aligning short-term performance incentives with long-term decentralization goals. We propose a framework for algorithmic monetary policies that navigates this tradeoff in repeated participation games. Agents, characterized by type (capability) and stake, choose to participate or abstain at each round; the policy (probabilistically) selects high-type agents for task execution (maximizing throughput) while distributing rewards to sustain decentralization. We analyze equilibria under two agent behaviors: myopic (short-term utility maximization) and foresighted (multi-round planning). For myopic agents, performance-centric policies risk centralization, but foresight enables stable decentralization with some volatility to the token value. We further discuss virtual stake--a hybrid of type and stake--as an alternative approach. We show that the initial virtual stake distribution critically impacts long-term outcomes, suggesting that policies must indirectly manage decentralization.
Cite
@article{arxiv.2512.16514,
title = {Algorithmic Monetary Policies for Blockchain Participation Games},
author = {Diodato Ferraioli and Paolo Penna and Manvir Schneider and Carmine Ventre},
journal= {arXiv preprint arXiv:2512.16514},
year = {2025}
}