Emergence of Cooperation in ANonymous Social Networks through Social Capital
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
We study the emergence of cooperation in dynamic, anonymous social networks, such as in online communities. We examine prisoner’s dilemma played under a social matching protocol, where individuals form random links to partners with whom they can interact. Cooperation results in mutual benefits, whereas defection results in a high short-term gain. Moreover, an agent that defects can escape reciprocity by virtue of anonymity: it is always possible for an agent to abandon his history and re-enter the network as a new user. We find that cooperation is sustainable at equilibrium in such a model. Indeed, cooperation allows an individual to interact with an increasing number of other cooperators, resulting in the formation of a type of social capital. This process arises endogenously, without the need for potentially harmful social enforcement rules. Additionally, for a rich class of parameter settings, our model predicts a stable coexistence of cooperating and defecting agents at equilibrium.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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