Promoting collective cooperation through temporal interactions
Bibliographic record
Abstract
Collective cooperation maintains the function of many natural and social systems, making understanding the evolution of cooperation a central question of modern science. Although human interactions involve complex contact networks, current explorations are limited to static networks, where social ties are permanent and do not change over time. In reality, human activities often involve temporal interactions, where links are impermanent, and understanding the evolution of cooperation on such temporal networks is an open problem. Here, we systematically analyze how cooperation spreads on arbitrary temporal networks, and we distill our results down to a concise condition, which integrates evolutionary game dynamics with both static and temporal interactions. We find that the emergence of cooperation is facilitated by a simple rule of thumb: Hubs (individuals with many social ties) should be temporally deprioritized in interactions. For empirical applications, we further provide a quantitative metric capturing the priority of hubs, which is validated on empirical datasets based on its effectiveness in orchestrating the ordering of interactions to best promote cooperation. Our findings unveil the fundamental advantages conferred by temporal interactions for promoting collective cooperation, transcending the specific insights gleaned from studying static networks.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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 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".