Bibliographic record
Abstract
I analyze a bilateral bargaining model with one-sided uncertainty about time preferences. The uninformed player has the option of halting the bargaining process to obtain additional information, when it is his turn to offer. For a wide class of preference settings, the uninformed player does not collect information when he is quite sure about his opponent’s type. There exist preference settings in which the uninformed player collects information until he is sufficiently sure about his opponent’s type, as long as the information source is accurate enough. With additional assumptions, the uninformed player is more likely to draw signals and is better off, if the information is more accurate. Key words: bargaining, alternate offers, incomplete information, delay. JEL codes: C78, D82. ∗I thank Larry Samuelson for encouragement and guidance. I also wish to thank Hiroyuki Kasa-hara, Grigory Kosenok, Akos Magyar, and Lućıa Quesada for helpful comments and discussions. All possible errors in this paper are my own.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".