Consequences of multiple simultaneous opportunities to exploit others’ efforts on free riding
Bibliographic record
Abstract
Individuals within a group do not all act in the same way: Typically, the investors (or producers) put efforts into producing resources while the free riders (or scroungers) benefit from these resources without contributing. In behavioral ecology, the prevalence of free riders can be predicted by a well-known game-theoretical model-the producer-scrounger (PS) model-where group members have the options to either search for resources (producers) or exploit the efforts of others (scroungers). The PS model has received some empirical support, but its predictions, surprisingly, are based on the strict assumption that only one resource can be exploited at a time. Yet, multiple simultaneous opportunities to exploit others' efforts should frequently occur in nature. Here, we combine analytic and simulation approaches to explore the effect of multiple simultaneous scrounging opportunities on tactic use. Our analyses demonstrate that scrounging rates should increase with the number of simultaneous opportunities. As such, the amount and spatial distribution (i.e., clumped vs. dispersed) of resources as well as the risk of predation are key predictors of scrounging behavior. Because scroungers contribute to reducing the speed of resource exploitation, the model proposed here has direct relevance to the exploitation and sustainability of renewable resources.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".