Information Sharing and Bargaining in Buyer-Seller Networks
Bibliographic record
Abstract
This paper presents a model of strategic buyer-seller networks with information exchange between sellers. Prior to engaging in bargaining with buyers, sellers can share access to buyers for a negotiated transfer. We study how this information exchange affects overall market prices, volumes and welfare, given different initial market conditions and information sharing rules. In markets with homogeneous traders, sharing always increases total trade volume. The market reaches Walrasian trade volume when there are more buyers than sellers or when sellers have more bargaining power. In most cases, market surplus is completely reallocated to sellers. In the markets with heterogeneous traders, sharing may either increase or decrease total trade volume. When sellers have more bargaining power than buyers, information exchange leads to trade above the Walrasian level, thus leaving inefficiency only due to overproduction of high-cost sellers. As a result of information sharing, the buyers who value goods the least will be squeezed out from the market independent of their location and bargaining power. We also show that if, together with information exchange, sellers assign property rights on the information, exchange leads to lower volume and market prices than exchange without property rights.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".