Honesty and dishonesty in gossip strategies: a fitness interdependence analysis
Bibliographic record
Abstract
Gossip, or sharing information about absent others, has been identified as an effective solution to free rider problems in situations with conflicting interests. Yet, the information transmitted via gossip can be biased, because gossipers may send dishonest information about others for personal gains. Such dishonest gossip makes reputation-based cooperation more difficult to evolve. But when are people likely to share honest or dishonest gossip? We build formal models to provide the theoretical foundation for individuals' gossip strategies, taking into account the gossiper's fitness interdependence with the receiver and the target. Our models across four different games suggest a very simple rule: when there is a perfect match (mismatch) between fitness interdependence and the effect of honest gossip, the gossiper should always be honest (dishonest); however, in the case of a partial match, the gossiper should make a choice based on their fitness interdependence with the receiver and the target and the marginal cost/benefit in terms of pay-off differences caused by possible choices of the receiver and the target in the game. Moreover, gossipers can use this simple rule to make optimal decisions even under noise. We discuss empirical examples that support the predictions of our model and potential extensions. This article is part of the theme issue 'The language of cooperation: reputation and honest signalling'.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".