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Record W2100352932 · doi:10.1002/agr.20181

Game theory application to Fed Cattle procurement in an experimental market

2009· article· en· W2100352932 on OpenAlex

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

VenueAgribusiness · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEconLitCollusionMarket powerProcurementEconomicsConsolidation (business)MicroeconomicsMarket dataMarket structureMarket concentrationLimitingIndustrial organizationMarketingBusiness

Abstract

fetched live from OpenAlex

Abstract Consolidation in meatpacking has elicited many market power concerns and studies. A noncooperative, infinitely repeated game theory model was developed and an empirical model estimated to measure beef packing firm behavior in cattle procurement. Experimental market data from three semester‐long classes using the Fed Cattle Market Simulator (FCMS) were used. Collusive behavior was found for all three data periods though the extent of collusion varied across semester‐long data periods. Results may have been influenced by market conditions imposed on the experimental market in two of the three semesters. One was a marketing agreement between the largest packer and two feedlots and the other involved limiting the amount and type of public market information available to participants. Findings underscore the need for applying game theory to real‐world transaction‐level, fed cattle market data. [EconLit Citations: C730, L100]. © 2009 Wiley Periodicals, Inc.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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