Algorithmic Monetary Policies for Blockchain Participation Games

TL;DR

This paper introduces a framework for algorithmic monetary policies in blockchain participation games, balancing short-term incentives and long-term decentralization through strategic reward distribution and agent behavior analysis.

Contribution

It proposes a novel policy framework that manages decentralization in blockchain tokenomics by analyzing equilibria under different agent behaviors and the impact of initial virtual stake distribution.

Findings

Foresight-based policies support stable decentralization despite token value volatility.

Myopic policies tend to lead to centralization and reduced decentralization.

Initial virtual stake distribution significantly influences long-term decentralization outcomes.

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.