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New Developments in Aggregation Economics

2011· article· en· W2142081703 on OpenAlex
Pierre Chiappori, Ivar Ekeland

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Economics · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAggregate (composite)IdentifiabilityAggregation problemTestabilityConsumption (sociology)Group (periodic table)Differential (mechanical device)Mathematical economicsEconomicsSimple (philosophy)Pareto principleExternalityAggregate behaviorAggregate demandMicroeconomicsFunction (biology)Computer scienceMathematical optimizationMathematics

Abstract

fetched live from OpenAlex

The goal of this article is to provide a general characterization of the aggregate behavior of a group in a market environment. We allow for public and private consumption, intragroup production, and consumption externalities within a group; we only assume that the group always reaches Pareto-efficient decisions. We show that aggregation problems involve a simple mathematical structure: The aggregate demand of the group, considered as a vector field, can be decomposed into a sum of gradients. We briefly introduce exterior differential calculus as a tool to study this structure. We analyze two main issues. One is testability: What restrictions (if any) on the aggregate demand function characterize the efficient behavior of the group? The second issue relates to identifiability; we investigate the conditions under which it is possible to recover the underlying structure—namely, individual preferences, the decision process, and the resulting intragroup transfers—from the group's aggregate behavior.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.036
GPT teacher head0.211
Teacher spread0.175 · 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