The Echo Chamber: Strategic Voting and Homophily in Social Networks
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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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".