The Echo Chamber: Strategic Voting and Homophily in Social Networks
Why this work is in the frame
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Bibliographic record
Abstract
We propose a model where voters are embedded in a social network. Each voter observes the ballots of her neighbors in the network, from which she infers the likely outcome of the election. Each voter may then revise her vote strategically, to maximize her expected utility. Our work focuses on plurality voting, where strategic voting is a major concern. We show that in practice, strategization increases with voter knowledge, yet can improve the social welfare for the population. Real world social networks exhibit a property called homophily; sometimes called ``The Echo Chamber Effect'', which is the tendency for friends to have similar ideologies. We find that homophily dampens the benefits of strategization, and correspondingly, lowers the frequency of its occurrence. This effect may contribute to the low number of strategic voters observed in real world elections. Additionally, strategization may lead to the elimination of less popular candidates, as voters revise their votes to less preferred but more hopeful candidates. This phenomenon is known as Duverger's Law in political science, and we show that it does not hold in certain network structures.
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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 | 0.000 | 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 | 0.001 | 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