TECHNICAL NOTE—Decentralized Inventory Sharing with Asymmetric Information
Bibliographic record
Abstract
We study the information asymmetry issues in a decentralized inventory-sharing system consisting of a manufacturer and two independent retailers, who privately hold demand information, noncooperatively place their orders, but cooperatively share inventories with each other. We find that although the manufacturer needs retailers' mean demand and standard deviation for her wholesale price decision, each retailer only needs to know the other retailer's demand standard deviation for his order quantity decision. However, an incentive compatibility analysis shows that retailers have incentives to share their demand information untruthfully. Although a truth-inducing scheme can be developed for a system with symmetric retailers who share information between themselves, no such scheme can be developed to ensure truth-telling to the manufacturer. Further, we develop a coordination mechanism (CIS) for the decentralized inventory-sharing system, considering information asymmetry. We show that CIS coordinates the manufacturer-retailers system and leads to an all-win situation under complete information. More importantly, CIS minimizes the value of information such that each party can obtain expected profits very close to their first-best profits even under asymmetric information and hence indirectly solves the information asymmetry problem. To our knowledge, this work is the first to study decentralized inventory sharing and its coordination considering asymmetric information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".