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Record W3124984703

Equilibrium Rejection of a Mechanism

2008· preprint· en· W3124984703 on OpenAlexaff
Gorkem Celik, Michael Peters

Bibliographic record

VenueRePEc: Research Papers in Economics · 2008
Typepreprint
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMechanism (biology)InterimComplete informationMechanism designOrder (exchange)Path (computing)Mathematical economicsRepeated gameMicroeconomicsComputer scienceEconomicsBayesian gameGame theoryPolitical science
DOInot available

Abstract

fetched live from OpenAlex

We study a mechanism design problem in which players can take part in a mechanism to coordinate their actions in a default game. By refusing to participate in the mechanism, a player can revert to playing the default game non-cooperatively. We show with an example that some allocation rules are implementable only with mechanisms which will be rejected on the equilibrium path. In our construction, a refusal to participate conveys information about the types of the players. This information causes the default game to be played under different beliefs, and more importantly under different higher order beliefs, than the interim ones. We find a lower bound on all the implementable payoffs. We use this bound to establish a condition on the default game under which all the implementable outcomes are truthfully implementable, without the need to induce rejection of the mechanism. JEL classification: C72; D82; D43

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.076
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0060.008
Open science0.0040.004
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.124
GPT teacher head0.410
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2008
Admission routes1
Has abstractyes

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