Founded in 2017, Algorand is one of the world's first carbon-negative, public blockchains inspired by proof of stake. Algorand uses a Byzantine agreement protocol to add new blocks to the blockchain. The protocol can tolerate malicious users as long as a supermajority of the stake is controlled by non-malicious users. The protocol achieves about 100x more throughput compared to Bitcoin and can be easily scaled to millions of nodes. Despite its impressive features, Algorand lacks a reward-distribution scheme that can effectively incentivize nodes to participate in the protocol. In this work, we study the incentive issue in Algorand through the lens of game theory. We model the Algorand protocol as a Bayesian game and propose a novel reward scheme to address the incentive issue in Algorand. We derive necessary conditions to ensure that participation in the protocol is a Bayesian Nash equilibrium under our proposed reward scheme even in the presence of a malicious adversary. We also present quantitative analysis of our proposed reward scheme by applying it to two real-world deployment scenarios. We estimate the costs of running an Algorand node and simulate the protocol to measure the overheads in terms of computation, storage, and networking.

翻译：Algorand成立于2017年，是全球首批受权益证明启发的碳负排放公共区块链之一。Algorand采用拜占庭协议机制向区块链添加新区块。只要绝大多数权益由非恶意用户控制，该协议即可容忍恶意用户的存在。其吞吐量约为比特币的100倍，且可轻松扩展至百万级节点。尽管功能卓越，Algorand仍缺乏能有效激励节点参与协议的奖励分配方案。本研究通过博弈论视角探讨Algorand的激励问题，将Algorand协议建模为贝叶斯博弈并提出新型奖励方案以解决激励问题。我们推导出必要约束条件，确保在存在恶意攻击者时，参与协议仍能成为所提奖励方案下的贝叶斯纳什均衡。通过将其应用于两种真实部署场景，我们对该奖励方案进行了量化分析，估算运行Algorand节点的成本，并通过协议仿真衡量其在计算、存储和网络方面的开销。