Designing contests for data science competitions: Number of stages and the prize structures
Bibliographic record
Abstract
Firms have been proactively holding data science competitions via online contest platforms to look for innovative solutions from the crowd. When firms are designing such competitions, a key question is “What should be a better contest design to motivate contestants to exert more effort?” We model two commonly observed contest structures ( one stage and two stage ) and two widely adopted prize structures ( high spread and low spread ). We employ economic experiments to examine how contest design affects contestants’ effort level. The results reject the base model with rationality assumption. We find that contestants exert significantly more effort in both the first stage and the second stage of the two‐stage contest. Moreover, it is better to assign most prizes to the winner in the two‐stage contest while it does not matter in one stage . To explain the empirical regularities, we develop a behavioral economics model that captures contestants’ psychological aversion to falling behind and continuous exertion of effort. Our findings demonstrate that it is important for contest organizers to account for the nonpecuniary factors that can influence contestants’ behavior in designing a competition.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".