部分观测生产网络中的决策相关信息

中文摘要 AI 辅助

即使其支持的经济决策已被识别，生产网络仍可能在很大程度上未被识别。我们刻画了基于暴露决策的充分测量，并计算了与已发布总量一致的网络上的尖锐最大遗憾。利用日本区域间投入产出表的早期和后期版本，我们从1995年表中选取测量，并针对2005年基准评估冻结设计。在约为完全披露所需统计量一半的情况下，所得监测集相对于基准最优仅损失平均暴露0.07个百分点，但其在兼容网络上的尖锐最大遗憾为4.83点。在2005年怀俄明州铁路中断前后的美国煤炭交付中，额外的装运测量识别出用于库存风险监测的最优工厂集合，尽管四个被监测工厂对受影响煤炭供应的暴露仍未识别。结果区分了良好的基准表现与决策保证，并表明决策可以在个体暴露之前被识别。它们建议通过其支持的经济决策来评估网络数据。

英文摘要

A production network can remain largely unidentified even when the economic decision it supports is identified. We characterize sufficient measurements for exposure-based decisions and compute sharp maximum regret over networks consistent with released totals. Using earlier and later vintages of Japan's interregional input-output accounts, we select measurements from the 1995 table and evaluate the frozen design against the 2005 benchmark. At roughly half the statistics required for full disclosure, the resulting monitoring set loses only 0.07 percentage points of average exposure relative to the benchmark optimum, yet its sharp maximum regret across compatible networks is 4.83 points. In U.S. coal deliveries surrounding a 2005 Wyoming rail disruption, additional shipment measurements identify the optimal set of plants to monitor for inventory risk, even though four monitored plants' exposures to the affected coal supply remain unidentified. The results distinguish good benchmark performance from a decision guarantee and show that decisions can be identified before individual exposures. They suggest evaluating network data by the economic decisions they support.

发表机构

• Virginia Tech（弗吉尼亚理工大学）
• Wenlan School of Business, Zhongnan University of Economics and Law（中南财经政法大学文澜商学院）

机构由 AI 辅助整理，请以论文原文为准。