标题： 在区块链上我们合作：一种演化博弈的视角 显示英文标题

标题： On Blockchain We Cooperate: An Evolutionary Game Perspective

摘要： 合作对于人类的繁荣至关重要。 区块链作为一种信任机器，是网络空间中通过分布式信任和共识协议支持合作的协作机构。 虽然计算机科学领域的研究集中在使用共识算法解决容错问题，经济研究则利用激励设计来分析代理人的行为。 为了在区块链上实现合作，新兴的跨学科研究引入了理性与博弈论解概念，以研究各种共识协议的均衡结果。 然而，现有研究没有考虑代理人从历史观察中学习的可能性。 因此，我们将一个通用的共识协议抽象为动态博弈环境，应用有限理性的解概念来建模代理人的行为，并解决了三种不同稳定均衡的初始条件。 在我们的博弈中，代理人在向均衡演进的过程中仿效性地学习全局历史，我们从计算和经济两个角度评估了安全、活跃性、有效性和社会福利的结果。 我们的研究对多个学科的文献做出了贡献，包括计算机科学中的分布式共识、经济学中关于区块链共识的博弈论、生物学与经济学交叉领域的演化博弈论、心理学与经济学相互作用中的有限理性，以及结合计算与社会科学的协作人工智能。 最后，我们讨论了未来协议设计可以通过增加奖励惩罚比例以及降低成本惩罚比例和关键性率，更好地实现我们诚实稳定均衡的最期望结果。 显示英文摘要

摘要： Cooperation is fundamental for human prosperity. Blockchain, as a trust machine, is a cooperative institution in cyberspace that supports cooperation through distributed trust with consensus protocols. While studies in computer science focus on fault tolerance problems with consensus algorithms, economic research utilizes incentive designs to analyze agent behaviors. To achieve cooperation on blockchains, emerging interdisciplinary research introduces rationality and game-theoretical solution concepts to study the equilibrium outcomes of various consensus protocols. However, existing studies do not consider the possibility for agents to learn from historical observations. Therefore, we abstract a general consensus protocol as a dynamic game environment, apply a solution concept of bounded rationality to model agent behavior, and resolve the initial conditions for three different stable equilibria. In our game, agents imitatively learn the global history in an evolutionary process toward equilibria, for which we evaluate the outcomes from both computing and economic perspectives in terms of safety, liveness, validity, and social welfare. Our research contributes to the literature across disciplines, including distributed consensus in computer science, game theory in economics on blockchain consensus, evolutionary game theory at the intersection of biology and economics, bounded rationality at the interplay between psychology and economics, and cooperative AI with joint insights into computing and social science. Finally, we discuss that future protocol design can better achieve the most desired outcomes of our honest stable equilibria by increasing the reward-punishment ratio and lowering both the cost-punishment ratio and the pivotality rate.