Implementable Mechanisms to Coordinate Horizontal Alliances
Bibliographic record
Abstract
Unprecedented changes in the economics of interaction, mainly as a result of advances in information and telecommunication technologies such as the Internet, are causing a shift toward more networked forms of organizations such as horizontal alliances—that is, alliances among firms in similar businesses that have positive externalities between them. Because the success of such horizontal alliances depends crucially on aligning individual alliance-member incentives with those of the alliance as a whole, it is important to find coordination mechanisms that achieve this alignment and are simple-to-implement. In this paper, we examine two simple coordination mechanisms for a horizontal alliance characterized by the following features: (i) firms in the alliance can exert effort only in their “local” markets to increase customer demand for the alliance; (ii) customers are mobile and a customer living in a given alliance member's local area may have a need to buy from some other alliance member; and (iii) the coordination rules followed by the alliance determine which firms from a large pool of potential member-firms join the alliance, and how much effort each firm joining the alliance exerts in its local market. In this horizontal alliance setup, we consider the use of two coordination mechanisms: (i) a linear transfer of fees between members if demand from one member's local customer is served by another member, and (ii) ownership of an equal share of the alliance profits generated from a royalty on each member's sales. We derive conditions on the distribution of demand externalities among alliance members to determine when each coordination mechanism should be used separately, and when the mechanisms should be used together.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".