Experimenting with Contests for Experimentation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We report an experimental test of alternative rules in innovation contests when success may not be feasible and contestants may learn from each other. Following Halac, Kartik, and Liu (in press), the contest designer can vary the prize allocation rule from Winner‐Take‐All (WTA) in which the first successful innovator receives the entire prize to Shared in which all successful innovators during the contest duration share in the prize. The designer can also vary the information disclosure policy from Public in which at each period, all information about contestants' past successes and failures is publicly available, to Private, in which contestants only know their own histories. In our setting, the optimal contest design in terms of maximizing the probability that at least one innovator is successful depends on the probability of successful innovation, given that innovation is feasible. Under some parameters the designer will prefer a WTA‐Public contest; while, under others he will prefer Shared‐Private. Our experiments provide evidence that Private disclosure contests behaviorally dominate Public disclosure, regardless of the prize allocation rule, and moreover that Shared‐Private contests dominate WTA‐Private contests.
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.
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 | 0.000 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it