Homophily and Social Norms in Experimental Network Formation Games
Bibliographic record
Abstract
Field studies of networks have uncovered a preference to befriend people we perceive as similar according to some dimensions of our identity (“homophily”). Lab studies of network formation games have found that adherence to social norms of reciprocity and inequity aversion are also drivers of network choices. No study so far has attempted to investigate the role of both homophily and social norms in a controlled environment. At the beginning of our experiment, each player fills in a personal profile. Each player then views the profile of all other players and expresses a degree of perceived similarity between his/her profile and the profile of the other player. At this point, a repeated network formation game ensues. We find that: (1) potential homophily considerations triggered by the profile rating task did not measurably change the players’ behavior compared to the baseline; (2) reciprocity plays a significant role in the formulation of the players’ strategies, in particular lowering the probability that the player naively best responds to the network observed in the previous period. We speculate that reciprocation of past choices might be a more “available” aid in strategy-formulation than considerations related to the similarity of the other players.
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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 | 0.005 | 0.022 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".