IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2609.29905.html

Decision-Relevant Information in Partially Observed Production Networks

Author

Listed:
  • Shaowen Luo
  • Kwok Ping Tsang
  • Zichao Yang

Abstract

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.

Suggested Citation

  • Shaowen Luo & Kwok Ping Tsang & Zichao Yang, 2026. "Decision-Relevant Information in Partially Observed Production Networks," Papers 2609.29905, arXiv.org.
  • Handle: RePEc:arx:papers:2609.29905
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2609.29905
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2609.29905. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.