A Bargaining Framework in Supply Chains: The Assembly Problem
Bibliographic record
Abstract
We examine a decentralized supply chain in which a single assembler buys complementary components from n suppliers and assembles the final product in anticipation of demand. Players take actions in the following sequence. First (stage 1), the suppliers form coalitions among themselves. Second (stage 2), the coalitions compete for a position in the negotiation sequence. Finally (stage 3), the coalitions negotiate with the assembler on allocations of the supply chain's profit. We model the multilateral negotiations between the suppliers and the assembler sequentially, i.e., the assembler negotiates with one coalition at a time. Each of these negotiations is modeled using the Nash bargaining concept. Further, in forming coalitions we assume that players are farsighted. We then predict at equilibrium the structure of the supply chain as a function of the players' relative negotiation powers. In particular, we show that the assembler always prefers the outcome where suppliers do not form coalitions. However, when the assembler is weak (low negotiation power) the suppliers join forces as a grand coalition, but when the assembler is powerful the suppliers stay independent, which is the preferred outcome to the assembler.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".