Acculturation drives the evolution of intergroup conflict
Bibliographic record
Abstract
Conflict between groups of individuals is a prevalent feature in human societies. A common theoretical explanation for intergroup conflict is that it provides benefits to individuals within groups in the form of reproduction-enhancing resources, such as food, territory, or mates. However, it is not always the case that conflict results from resource scarcity. Here, we show that intergroup conflict can evolve, despite not providing any benefits to individuals or their groups. The mechanism underlying this process is acculturation: the adoption, through coercion or imitation, of the victor's cultural traits. Acculturation acts as a cultural driver (in analogy to meiotic drivers) favoring the transmission of conflict, despite a potential cost to both the host group as a whole and to individuals in that group. We illustrate this process with a two-level model incorporating state-dependent event rates and evolving traits for both individuals and groups. Individuals can become "warriors" who specialize in intergroup conflicts, but are costly otherwise. Additionally, groups are characterized by cultural traits, such as their tendency to engage in conflict with other groups and their tendency for acculturation. We show that, if groups engage in conflicts, group selection will favor the production of warriors. Then, we show that group engagement can evolve if it is associated with acculturation. Finally, we study the coevolution of engagement and acculturation. Our model shows that horizontal transmission of culture between interacting groups can act as a cultural driver and lead to the maintenance of costly behaviors by both individuals and groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".