Why can’t we be friends? Entitlements and the costs of conflict
Bibliographic record
Abstract
Abstract We design an experiment to explore the impact of earned entitlements on the frequency and intensity of conflicts in a two-stage conflict game where players may attempt to use non-binding side-payments to avoid conflict. In this game, Proposers make offers and Responders decide simultaneously whether to accept the offers and whether to engage in a conflict. A simple theoretical analysis suggests that Proposers should never offer side-payments because Responders should always accept them and then still choose to enter conflict; however, our experiment reveals that some individuals use this non-binding mechanism to avoid conflict. Moreover, when subjects earn their roles (Proposer or Responder), conflicts are 44% more likely to be avoided than when roles are assigned randomly. Earned entitlements impact behavior in three important ways: (1) Proposers who have earned their position persistently make larger offers; (2) larger offers lead to a lower probability of conflict; but (3) Proposers whose offers do not lead to conflict resolution respond spitefully with greater conflict expenditure. Hence, with earned rights, the positive welfare effects of reduced conflict frequency are offset by higher conflict intensity. This result differs from previous experimental evidence from ultimatum games in which earned entitlements tend to encourage agreement and increase welfare; thus, our findings highlight the important consequences of endogenizing the costs of conflict. Our analysis suggests that earned entitlements alter behavior by influencing the beliefs of Proposers about the willingness of Responders to accept a peaceful resolution. As a result, these Proposers make persistent high offers, and when their beliefs are disappointed by a Responder’s decision to accept a side-payment and still enter conflict, they retaliate.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".