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Record W2013473227 · doi:10.1111/1468-2354.00119

Cost Manipulation Games in Oligopoly, With Costs of Manipulating

2001· article· en· W2013473227 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

VenueInternational Economic Review · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsMcGill UniversityCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsOligopolyMarginal costMicroeconomicsEconomicsOutcome (game theory)Redistribution (election)Ex-anteResource allocationResource (disambiguation)Marginal productSequential gameGame theoryMathematical economicsComputer scienceCournot competitionProduction (economics)

Abstract

fetched live from OpenAlex

We analyze a class of two‐stage games where rival firms incur real resource costs in manipulating their marginal costs, so as to influence the outcome of the game they want to play in stage two. Marginal costs may be manipulated by various means, such as redistribution of productive assets, choice of location, or creation of an internal input market. A general formulation of the game is provided, and several applications are analyzed. We show that the optimal allocation of resources within an oligopoly can be asymmetric, even for ex‐ante symmetric firms. This is an additional explanation of heterogeneity in oligopoly.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score0.996

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)0.0050.000

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 head0.064
GPT teacher head0.290
Teacher spread0.226 · 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