Competition and Cooperation in Decentralized Push and Pull Assembly Systems
Bibliographic record
Abstract
In this paper, we study a decentralized assembly system consisting of a single assembler who buys complementary components from independent suppliers under two contracting schemes: push and pull. In both schemes, the component suppliers are allowed to freely form coalitions (or alliances) among themselves to better coordinate their pricing or production decisions. We show that the sole driver of the inefficiency in a push system, which is due to horizontal decentralization of suppliers, is the number of alliances that were formed. Specifically, it is shown that in a push system, the assembler's profit, the total profit of all suppliers and the consumers' surplus are all decreasing in the number of coalitions, and are thus maximized when the grand coalition is formed. We further carry out a stability analysis of coalition structures to verify to what extent suppliers can reduce or eliminate the inefficiency due to their decentralization by forming alliances. We show that in a push system with more than two suppliers and a power demand distribution, myopic suppliers would act independently, resulting with a least efficient channel, which makes all channel members, as well as the end consumers, worse off. On the other hand, we prove that farsighted suppliers would form the grand coalition and thus be able to completely eliminate the inefficiency stemming from their decentralization. Finally, it is shown that, in contrast to a push system, in a pull system the suppliers can easily coordinate their production quantities to eliminate the inefficiency due to their decentralization.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".