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Record W2479077205 · doi:10.5555/2936924.2937144

Convergence and Quality of Iterative Voting under Non-Scoring Rules: (Extended Abstract)

2016· article· en· W2479077205 on OpenAlexaff
Aaron Koolyk, Omer Lev, Jeffrey S. Rosenschein

Bibliographic record

VenueAdaptive Agents and Multi-Agents Systems · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGame Theory and Voting Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVotingConvergence (economics)Computer scienceQuality (philosophy)Iterative methodSocial choice theoryAnti-plurality votingCardinal voting systemsMathematical optimizationAlgorithmMathematical economicsMathematicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

Iterative voting is a social choice mechanism whereby voters are allowed to continually make strategic changes to their stated preferences until no further change is desired. We study the iterative voting framework for several common voting rules and show that, for these rules, an equilibrium may never be reached. We also consider several variations of iterative voting and show that with these variations equilibrium likewise may not be reached. Finally, we present an empirical analysis of the quality of candidates elected through iterative voting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.148
GPT teacher head0.304
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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