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Record W3125200970 · doi:10.3386/w14239

Dynamics and Stability of Constitutions, Coalitions, and Clubs

2008· preprint· en· W3125200970 on OpenAlexfundno aff
Daron Acemoğlu, Georgy Egorov, Konstantin Sonin

Bibliographic record

VenueNational Bureau of Economic Research · 2008
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
FundersCanadian Institute for Advanced ResearchNational Science Foundation
KeywordsDynamics (music)Stability (learning theory)Mathematical economicsComputer scienceMathematicsSociology

Abstract

fetched live from OpenAlex

A central feature of dynamic collective decision-making is that the rules that govern the procedures for future decision-making and the distribution of political power across players are determined by current decisions. For example, current constitutional change must take into account how the new constitution may pave the way for further changes in laws and regulations. We develop a general framework for the analysis of this class of dynamic problems. Under relatively natural acyclicity assumptions, we provide a complete characterization of dynamically stable states as functions of the initial state and determine conditions for their uniqueness. We show how this framework can be applied in political economy, coalition formation, and the analysis of the dynamics of clubs. The explicit characterization we provide highlights two intuitive features of dynamic collective decision-making: (1) a social arrangement is made stable by the instability of alternative arrangements that are preferred by sufficiently many members of the society; (2) efficiency-enhancing changes are often resisted because of further social changes that they will engender.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
grokno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
opusno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.015
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.005
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.272
GPT teacher head0.420
Teacher spread0.149 · 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

Labeled directly by 3 models reading the full record.

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

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

Citations16
Published2008
Admission routes1
Has abstractyes

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