DEGREE OF DIFFICULTY AS THE OBJECTIVE OF CONTEST DESIGN
Bibliographic record
Abstract
The compensation received by economic agents reflects their performance. Usually compensation reflects performance measured cardinally, but sometimes ordinal considerations play a role. It is well established that rewards — cardinal or ordinal — can rationally motivate contestants to put forth increased effort. We ask whether rewards, and in particular their cardinal or ordinal nature, can affect agents' strategies. Specifically, if level of effort is fixed and degree of difficulty is the only choice, what strategy is optimal? For example, in a high-jump competition, level of effort is not a meaningful variable; what is of interest is the choice of strategy — the height attempted, or degree of difficulty. We study how optimal strategies reflect reward structure, assuming that rewards may depend on level of difficulty, and go only to successful candidates, or only to candidates who succeed at more difficult tasks. Basing our conclusions in part on simple probabilistic models in which optimal choices can be determined analytically, we show how the structure of competitive rewards alters contestants' rational choices. We adopt a contest-design framework: What combinations of fixed and variable prizes cause contestants to select degrees of difficulty that maximize the contest designer's expected payoff? Our general conclusion is that competition can affect strategic choices, in magnitudes and even directions that are difficult to predict.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".