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Record W4395704285 · doi:10.1093/oep/gpae015

Welfare-maximizing patent length in a dynamic general equilibrium model

2024· article· en· W4395704285 on OpenAlexfundno aff
Tatsuro Iwaisako

Bibliographic record

VenueOxford Economic Papers · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceInstitute of Social and Economic Research, Memorial University of NewfoundlandJapan Center for Economic Research
KeywordsGeneral equilibrium theoryDynamic equilibriumEconomicsWelfareMathematical economicsMicroeconomicsThermodynamicsPhysicsMarket economy

Abstract

fetched live from OpenAlex

Abstract This article examines analytically the welfare-maximizing patent length in a dynamic general equilibrium model which is based on a variety expansion R&D-based growth model. The results show that if the elasticity of substitution among the intermediate goods is higher, that is, the price elasticity of demand is higher, the welfare-maximizing patent length is shorter. This article also examines the welfare-maximizing mix of patent length and breadth, and numerically obtains similar results, that is, a higher elasticity of substitution shortens the welfare-maximizing length.

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 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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.079
GPT teacher head0.221
Teacher spread0.142 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations7
Published2024
Admission routes1
Has abstractyes

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