Synthesizing perspectives on the evolution of cooperation within and between species
Bibliographic record
Abstract
Cooperation is widespread both within and between species, but are intraspecific and interspecific cooperation fundamentally similar or qualitatively different phenomena? This review evaluates this question, necessary for a general understanding of the evolution of cooperation. First, we outline three advantages of cooperation relative to noncooperation (acquisition of otherwise inaccessible goods and services, more efficient acquisition of resources, and buffering against variability), and predict when individuals should cooperate with a conspecific versus a heterospecific partner to obtain these advantages. Second, we highlight five axes along which heterospecific and conspecific partners may differ: relatedness and fitness feedbacks, competition and resource use, resource-generation abilities, relative evolutionary rates, and asymmetric strategy sets and outside options. Along all of these axes, certain asymmetries between partners are more common in, but not exclusive to, cooperation between species, especially complementary resource use and production. We conclude that cooperation within and between species share many fundamental qualities, and that differences between the two systems are explained by the various asymmetries between partners. Consideration of the parallels between intra- and interspecific cooperation facilitates application of well-studied topics in one system to the other, such as direct benefits within species and kin-selected cooperation between species, generating promising directions for future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".