发表机构
International College of Economics and Finance, National Research University Higher School of Economics; Toulouse School of Economics（国际经济与金融学院，国立研究型大学高等经济大学; 图卢兹经济学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究任务架构如何影响组织从项目成败中学习能力，发现捆绑任务在外部技术不可靠时占优，并推导出阈值与审计边界。
AI 中文摘要
组织通常仅从项目层面的成功或失败中了解能力，即使一个项目包含多个互补任务。我们通过Blackwell序比较任务架构。一个能力未知但固定的专家可以执行一个捆绑项目中的所有任务、一个由外部技术完成的项目中的单个任务，或跨独立项目的相同数量的任务。在外部可靠性阈值以下，捆绑优于单一狭窄任务分配，否则两者不可比较。保持专家工作量固定会严格降低此阈值，但不会推翻结果：当外部技术足够不可靠时，捆绑仍然占优；仅当外部技术完美时，独立项目才占优；否则实验不可比较。我们推导了任意任务数量的阈值，并表明固定工作量阈值随任务范围扩大而降至零。显式的后验方差公式衡量了粗粒度聚合对特定决策的成本。最后，在双任务情形下，偶尔的阶段级审计根据精确边界扩展了捆绑占优区域。
英文摘要
Organizations often learn about competence only from project-level success or failure, even when a project contains several complementary tasks. We compare task architectures by the Blackwell order. An expert of unknown fixed competence can perform all tasks in one bundled project, one task in a project completed by an outside technology, or the same number of tasks across separate projects. Bundling dominates a single narrow assignment below an outside-reliability threshold and is otherwise incomparable with it. Holding the expert's workload fixed strictly lowers this threshold but does not overturn the result: bundling still dominates when the outside technology is sufficiently unreliable, separate projects dominate only when that technology is perfect, and the experiments are otherwise incomparable. We derive the thresholds for any number of tasks and show that the fixed-workload threshold decreases to zero as task scope grows. Explicit posterior-variance formulas measure the cost of coarse aggregation for particular decisions. Finally, in the two-task case, occasional stage-level audits expand the bundling-dominance region according to an exact frontier.
