In decentralized systems, it is often necessary to select an 'active' subset of participants from the total participant pool, with the goal of satisfying computational limitations or optimizing resource efficiency. This selection can sometimes be made at random, mirroring the sortition practice invented in classical antiquity aimed at achieving a high degree of statistical representativeness. However, the recent emergence of specialized decentralized networks that solve concrete coordination problems and are characterized by measurable success metrics often requires prioritizing performance optimization over representativeness. We introduce a simple algorithm for 'merit-based sortition', in which the quality of each participant influences its probability of being drafted into the active set, while simultaneously retaining representativeness by allowing inactive participants an infinite number of chances to be drafted into the active set with non-zero probability. Using a suite of numerical experiments, we demonstrate that our algorithm boosts the quality metric describing the performance of the active set by $>2$ times the intrinsic stochasticity. This implies that merit-based sortition ensures a statistically significant performance boost to the drafted, 'active' set, while retaining the property of classical, random sortition that it enables upward mobility from a much larger 'inactive' set. This way, merit-based sortition fulfils a key requirement for decentralized systems in need of performance optimization.

翻译：在去中心化系统中，通常需要从全体参与者池中选出一个“活跃”子集，其目标在于满足计算限制或优化资源效率。这种选择有时可以随机进行，这模仿了古典时期发明的抽签实践，旨在实现高度的统计代表性。然而，近期出现的专门化去中心化网络解决了具体的协调问题，并以可度量的成功指标为特征，往往需要将性能优化置于代表性之上。我们提出了一种简单的“基于功绩的抽签”算法，其中每个参与者的质量会影响其被选入活跃集合的概率，同时通过允许非活跃参与者拥有无限次以非零概率被选入活跃集合的机会来保留代表性。通过一系列数值实验，我们证明该算法将描述活跃集合性能的质量指标提升了超过其内在随机性的$>2$倍。这意味着基于功绩的抽签确保了被选中的“活跃”集合在统计上显著的性能提升，同时保留了经典随机抽签的特性，即允许从更大的“非活跃”集合中实现向上流动性。通过这种方式，基于功绩的抽签满足了需要性能优化的去中心化系统的一项关键要求。