Bibliographic record
Abstract
We design a multiple project-funding contract that provides optimal incentives to recipients, in a setting where externalities exist among the multiple projects and where donors and recipients may differ in their valuation of the projects. To do so, we study optimal incentive payments in a dynamic principal-agent framework with focus on two-project contracts. The principal cannot observe the agent’s investment, but only completed projects. We consider principals that cannot commit to contract termination before completion of the projects; we assume that the contract does not end until both projects are accomplished. We derive the optimal contract for each possible combination of principal-agentproject characteristics to find that projects should be undertaken simultaneously when value externalities among them are large, i.e. when completing both projects gives the recipient significantly more utility than the sum of the projects’ independent values. The principal’s utility maximizing strategy, when technical externalities among projects are important, is a sequential contract that starts with the project that generates the externality. We find that differences in project valuation between agents and recipients may, in some cases, lead to inefficient contracts, when in other situations the ability of the principal to choose the timing of the project competition may be a safety clause for him.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".