Bibliographic record
Abstract
Cooperation and competition are a fundamental part of human interaction. In each situation, different people will cooperate or compete with one another in different ways. In this paper, we study the relationship between how people feel about the person they are interacting with (the affective identity of that person) and their level of cooperation in different circumstances. Standard game-theoretic models provide solutions to what self-interested rational agents would do in various situations. However, humans don't respond rationally in many situations, and the decision to cooperate can be strongly influenced by the identities of the interactants. In this study, over 1,000 participants answered a survey about whether they would cooperate in various framings of the Prisoners Dilemma (PD). These framings are based on the shared cultural sentiments in a three dimensional emotion space (Evaluation, Potency and Activity or EPA) about identities measured in existing large scale surveys. We combined 27 such identities with a set of five different payoff matrices, and provide statistical correlates between the sentiments about identities and likelihood of cooperation. We show that the evaluative (E) dimension is a strong predictor of cooperation, and we discuss the other factors including mixing terms. Our results provide a novel alternative view of cooperation in PD as arising simply from culturally shared sentiments about identities, rather than from payoff estimates.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".