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:阿尔戈兰德(Algorand)是全世界第一个受利益证明启发的碳阴性公共链条。 阿尔戈兰德(Algorand)使用拜占庭协议协议协议协议协议协议协议在链条上添加新块块。 该协议可以容忍恶意用户, 只要该股份的超级多数由非恶意用户控制。 协议比比比特可林(Bitcoin)高出大约100倍,并且可以很容易地扩展到数百万个节点。 尽管它具有令人印象深刻的特征, 但阿尔戈兰和缺乏一个奖励分配计划,能够有效地激励节点参与协议。 在这项工作中,我们通过游戏理论的透镜研究阿尔戈兰的奖励问题。 我们把阿尔戈兰协议作为游戏的模型,并提出一个新的奖励计划,以解决阿尔戈兰德(Algorand)的奖励问题。 我们创造必要的条件,确保参与协议是我们提议的奖励计划下的巴耶西亚纳什均衡,即使存在恶意仇敌。 我们还通过将它应用于两个现实世界部署设想,来对我们拟议的奖励计划进行定量分析。 我们在这项工作中研究阿尔哥(Al)的奖赏计划,我们将它用于模拟协议的存储的存储成本和计算。