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Record W1972559425 · doi:10.1016/j.anihpc.2005.10.001

An inverse problem in the economic theory of demand

2005· article· en· W1972559425 on OpenAlexaff
Ivar Ekeland, Ngalla Djitté

Bibliographic record

VenueAnnales de l Institut Henri Poincaré C Analyse Non Linéaire · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInverse demand functionEconomicsMathematical economicsNeoclassical economicsMicroeconomicsDemand curve

Abstract

fetched live from OpenAlex

Given an exchange economy consisting of k consumers, there is an associated collective demand function, which is the sum of the individual demand functions. It maps the price system p to a goods bundle x(p) . Conversely, given a map p\rightarrow x(p) , it is natural to ask whether it is the collective demand function of a market economy. We answer that question in the case when k is less than the number of goods n . The proof relies on finding convex solutions to a strongly nonlinear system of partial differential equations. Résumé La fonction de demande agrégée d'une société composée de k individus résulte de la sommation de k fonctions de demandes individuelles. Elle fait correspondre à un système de prix p , un vecteur de biens x(p) . Inversement, étant donnée une fonction p\rightarrow x(p) , est-elle une fonction de demande agrégée ? Nous apporterons une réponse à cette question dans le cas où il y a moins de consommateurs que de biens.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.024
GPT teacher head0.296
Teacher spread0.272 · 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

Citations12
Published2005
Admission routes1
Has abstractyes

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