Bibliographic record
Abstract
This paper studies the competition between sellers who choose how much informa-tion to provide to potential buyers. We analyse the symmetric equilibria in information provision of a game in which two sellers with unit supplies compete to attract two buy-ers with unit demands. Sellers compete ex ante; they commit to a level of information provision and to a sale mechanism (e.g. a second-price auction). More informed buyers have better differentiated private valuations and trade yields them higher informational rents. Our focus is on this critical trade-off faced by sellers: promising information at-tracts buyers (traffic effect) but lowers profits-per-buyer (rents effect). When the sale mechanisms are common and exogenously fixed, we find that sellers ’ equilibrium profits can be higher under mechanisms that yield more rents to buyers. High-rent mechanisms inhibit market-stealing and soften the competition between sellers, which lowers equilib-rium levels of information provision. High rent levels may also intensify the competition for goods between the buyers, which compresses the traffic-rents trade-off and further dampens the competition between sellers. When sellers promise both information and sale mechanisms, we show that they can capture the efficiency gains of increased infor-mation so that all symmetric equilibria in a large class have full information provision. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".