Empathetic social choice on social networks
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
Social and economic networks play a fundamental role in facilitating interactions and behaviors between individuals, businesses, and organizations. It is widely recog-nized that such networks can correlate behaviors (and arguably preferences) among connected agents. We introduce a model for social choice—specifically, consensus decision making—on such networks that reflects certain interdependencies among agent utilities. Specifically, we define an empathetic social choice framework in which agents derive utility based on both their own intrinsic preferences and the satisfac-tion of their neighbors. We show how this problem translates into a weighted form of classical preference aggregation (e.g., social welfare maximization or certain forms of voting), and develop effective algorithms for consensus decision making that we believe should scale to large-scale (online) social or economic networks. Preliminary experiments validate the effectiveness of our proposed algorithms. 1
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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