This paper presents karma mechanisms, a novel approach to the repeated allocation of a scarce resource among competing agents over an infinite time. Examples include deciding which ride hailing trip requests to serve during peak demand, granting the right of way in intersections or lane mergers, or admitting internet content to a regulated fast channel. We study a simplified yet insightful formulation of these problems where at every instant two agents from a large population get randomly matched to compete over the resource. The intuitive interpretation of a karma mechanism is "If I give in now, I will be rewarded in the future." Agents compete in an auction-like setting where they bid units of karma, which circulates directly among them and is self-contained in the system. We demonstrate that this allows a society of self-interested agents to achieve high levels of efficiency without resorting to a (possibly problematic) monetary pricing of the resource. We model karma mechanisms as dynamic population games and guarantee the existence of a stationary Nash equilibrium. We then analyze the performance at the stationary Nash equilibrium numerically. For the case of homogeneous agents, we compare different mechanism design choices, showing that it is possible to achieve an efficient and ex-post fair allocation when the agents are future aware. Finally, we test the robustness against agent heterogeneity and propose remedies to some of the observed phenomena via karma redistribution.

翻译：本文提出了一种名为“业力机制”的新方法，用于在无限时间范围内重复分配稀缺资源给相互竞争的智能体。示例包括：在需求高峰期决定服务哪些网约车行程请求、在交叉路口或车道合并中授予通行权，或允许互联网内容进入受监管的快速通道。我们研究了这些问题的简化但有洞察力的形式化版本：在每个时刻，大量智能体中的两个随机匹配，争夺该资源。业力机制的直观解释是：“如果我此刻让步，未来将获得回报。”智能体在类似拍卖的环境中竞争，其中他们出价的是“业力”单位——这种业力直接在智能体之间流通，并在系统内自给自足。我们证明，这使得一个由自利智能体组成的社会能够实现高效率，而无需诉诸（可能存在问题）的对资源的货币定价。我们将业力机制建模为动态群体博弈，并保证存在一个平稳纳什均衡。随后，我们通过数值方法分析了该均衡下的性能表现。对于同质智能体的情形，我们比较了不同的机制设计选择，表明当智能体具有未来意识时，可以实现高效且事后公平的分配。最后，我们测试了机制对异质智能体的鲁棒性，并提出了通过业力再分配来纠正某些观察到的现象的补救措施。