Empirical analysis of plurality election equilibria
Bibliographic record
Abstract
Voting is widely used to aggregate the different preferences of agents, even though these agents are often able to manip-ulate the outcome through strategic voting. Most research on manipulation of voting methods studies (1) limited solu-tion concepts, (2) limited preferences, or (3) scenarios with a few manipulators that have a common goal. In contrast, we study voting in plurality elections through the lens of Nash equilibrium, which allows for the possibility that any number of agents, with arbitrary different goals, could all be manipulators. This is possible thanks to recent advances in (Bayes-)Nash equilibrium computation for large games. Al-though plurality has numerous pure-strategy Nash equilib-ria, we demonstrate how a simple equilibrium refinement— assuming that agents only deviate from truthfulness when it will change the outcome—dramatically reduces this set. We also use symmetric Bayes-Nash equilibria to investigate the case where voters are uncertain of each others ’ preferences. This refinement does not completely eliminate the problem of multiple equilibria. However, it does show that even when agents manipulate, plurality still tends to lead to good out-comes (e.g., Condorcet winners, candidates that would win if voters were truthful, outcomes with high social welfare).
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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 | 0.010 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".