The Sensitivity of the U.S. Presidential Election to Coordinated Voter Relocation
Bibliographic record
Abstract
U.S. presidential elections are determined by the Electoral College. In all but two states, a statewide winner-take-all system for electors can lead to decisive outcomes based on narrow victories in key states. Small groups of voters can significantly impact results, not only through turnout but also through a less-explored mechanism: the strategic relocation of a relatively small number of dedicated voters across state lines. The extent to which election outcomes are sensitive to such coordinated movements has not been thoroughly investigated. We introduce an analytical framework that integrates forecasting, simulation, and optimization to identify these pivotal voter shifts. Our findings show that small-scale relocations can meaningfully alter election probabilities under a range of parameter settings and polling data sources. Furthermore, we examine how the optimization-based recommendations align with actual election results, demonstrating that the suggested movements would have been beneficial in the 2024 U.S. presidential election—even when based on pre-election data. Given the remarkably small number of individuals required and the fact that electoral residency in many states can be established within about a month, our results have direct implications for policymaking and campaign strategy. Moreover, they highlight new opportunities for applying operations research methods to political science. History: Accepted by Alice E. Smith, Editor-in-Chief. Funding: This work was supported by an Natural Sciences and Engineering Research Council of Canada Discovery Grant. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0990 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0990 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .
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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 | 0.006 | 0.050 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".