Implications of Reciprocity in the Evolution of Ethnocentrism and Cooperation
Bibliographic record
Abstract
Background: Ethnocentrism is defined as an individual’s tendency to favor in-group members at the expense of out-group members. Recent computer simulations have studied its evolution by modelling cooperative and defective behaviours in a Prisoner’s Dilemma framework. Methods: This paper introduces reciprocity to the study of ethnocentrism and extends Hammond and Axelrod’s agent-based model by simulating the effects of five new genotypic strategies. (1) Results: In stable-state outcomes, although ethnocentrism still dominates, moderate ethnocentrism (in-group cooperation and out-group reciprocity) is more frequent than humanitarianism and is by far the most adaptive out of all reciprocal strategies. Because it is the only reciprocal strategy that cooperates with in-group members, we conclude that it is thanks to in-group cooperation that moderate ethnocentrism is successful, which confirms previous research findings. Additionally, throughout early and late evolutionary patterns, we see that moderate ethnocentrism benefits and suffers from the characteristics of both ethnocentrism and humanitarianism, which may explain why ethnocentrism still emerges as the dominant strategy overall. Conclusion: The strengths of the present model lie in its ability to abstractly model reciprocal behaviours in the study of ethnocentrism and may be more externally valid than Hammond and Axelrod’s original agent-based model. (1) However, this model does not take in account other factors that play a role in human decision-making, such as social context, learning, or development, which could be topics of future computational simulations on ethnocentrism.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".