Bibliographic record
Abstract
Abstract In this paper, I study bilateral trade, where the seller can undertake specific investments before the binary trade transaction takes place. I identify a novel reason for hold‐up and contractual inefficiency in this canonical setting. The investing party can shirk for strategic reasons; that is, exert an effort so low that trade becomes inefficient and is being rescinded. Under a fixed‐price contract (the second‐best mechanism in the absence of the shirking problem), strategic shirking can arise regardless of the initially contracted trade price. Moreover, if a fixed‐price contract leads to strategic shirking, there exists no general revelation mechanism to restore equilibrium trade. I show that the shirking problem is more severe when the parties trade after having learned the buyer's valuation, as opposed to the case of an “experience good” where trade is finalized before this information materializes. Finally, when both buyer and seller undertake specific investments, shirking and non‐shirking equilibria are shown to coexist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".