Democratic Competition for Rank, Cooperation, and Deception in Small Groups
Bibliographic record
Abstract
Objective Stratified groups face at least two obstacles in solving collective action problems and producing public goods. Individuals face temptation to free ride, and high‐ranking group members face incentives to protect their position at the group's expense. We introduce democratic competition for rank as a solution to the problem of cooperation in groups. We argue that democratic competition for high rank creates incentives for cooperation that are absent in nondemocratic groups. Methods In a small‐group behavioral experiment, we contrast groups in which individuals compete for a valuable high‐ranking position through democratic elections with groups in which individuals compete for high rank in resource‐based competitions. Groups faceda fluctuating external threat, and group members could invest resources in manipulating the apparent (but not actual) level of this threat. Results We find that democratic groups reward high contributors by electing them to the high‐ranking position at greater rates than low contributors. We also find evidence that individuals in democratic groups contribute more to the public good than individuals in nondemocratic groups. However, high‐ranking individuals in democratic groups exaggerate threats to the group at similar rates to high‐ranking individuals in nondemocratic groups. Conclusion The findings suggest that democratic competition increases public goods production and overall group efficiency, but does not eliminate—and may exacerbate—individuals' tendency to deceive their peers
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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 | 0.006 | 0.018 |
| 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 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".