The emergence of cooperative behaviors, norms, and strategies across five diverse societies
Bibliographic record
Abstract
Human cooperation involves a complex web of interconnected behaviors that develop across the lifespan in conjunction with the cultural environment. While we have learned much in recent decades about the early origins of these behaviors in Western societies, we still know relatively little about: (1) how cooperative behaviors vary across cultures, (2) how the normative environment shapes the development of different cooperative behaviors, and (3) the extent to which key cooperative behaviors relate to one another. In this investigation, we examined the development of a suite of four cooperative behaviors — those related to fairness, trustworthiness, forgiveness, and honesty — in children (N=413, 5-13 years old) from five diverse societies: urban-living children in the United States, rural-living children in Uganda, Canada, and Peru, and hunter-horticulturalist Shuar children in Amazonian Ecuador. In addition to behavioral data, we collected normative judgments from peers (N=163) and adults (N=86) in each community to culturally contextualize behavior within the larger normative environment. Examined together, we find compelling evidence for substantial cross-cultural variation in cooperative behaviors and norms among children, but that, more generally, both children’s behaviors and norms tend to converge toward community-specific norms in middle childhood. We also find three distinct cooperative strategies — Maximization, Generic Cooperation, Partner-Contingent Cooperation — show that these strategies change in prevalence across development, and find that the prevalence of each strategy varies across societies. This investigation advances our understanding of human cooperation by demonstrating how a constellation of cooperative behaviors develops across diverse societies and highlighting the underlying cultural forces that contribute to its development.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".