Approximately Strategy-proof Mechanisms for (Constrained) Facility Location
Bibliographic record
Abstract
Mechanism design for facility location (or selection of al-ternatives in a metric space) has been studied for decades. While strategy-proof, efficient mechanisms exist for uncon-strained, one-dimensional, single-facility problems, guaran-tees of strategy-proofness and efficiency often break when allowing: (a) multiple dimensions; (b) multiple facilities; or (c) constraints on the feasible placement of facilities. We address these more general problems, providing several pos-sibility/impossibility results with respect to individual and group strategy-proofness in both constrained and uncon-strained problems. We also bound the incentive for manipu-lation in median-like mechanisms in settings where strategy-proofness is not possible. We complement our results with empirical analysis of both electoral and geographic facility data, showing that the odds of successful manipulation, and more importantly, the gains and impact on social welfare, are small in practice (much less than worst-case theoretical bounds).
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".