Harnessing the Power of Reputation: Strengths and Limits for Promoting Cooperative Behaviors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Evolutionary approaches have done much to identify the pressures that select for cooperative sentiment. This helps us understand when and why cooperation will arise, and applied research shows how these pressures can be harnessed to promote various types of cooperation. In particular, recent evidence shows how opportunities to acquire a good reputation can promote cooperation in laboratory and applied settings. Cooperation can be promoted by tapping into forces like indirect reciprocity, costly signaling, and competitive altruism. When individuals help others, they receive reputational benefits (or avoid reputational costs), and this gives people an incentive to help. Such findings can be applied to promote many kinds of helping and cooperation, including charitable donations, tax compliance, sustainable and pro-environmental behaviors, risky heroism, and more. Despite the potential advantages of using reputation to promote positive behaviors, there are several risks and limits. Under some circumstances, opportunities for reputation will be ineffective or promote harmful behaviors. By better understanding the dynamics of reputation and the circumstances under which cooperation can evolve, we can better design social systems to increase the rate of cooperation and reduce conflict.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it