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Record W1644774079 · doi:10.22004/ag.econ.148589

Optimal quality choice under uncertainty on market development

2012· preprint· en· W1644774079 on OpenAlexaff
Lota D. Tamini

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2012
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAmbiguity aversionRisk aversion (psychology)DuopolyMicroeconomicsEconomicsQuality (philosophy)Stackelberg competitionProfit (economics)AmbiguityMonopolyExpected utility hypothesisComputer scienceFinancial economicsCournot competition

Abstract

fetched live from OpenAlex

This paper analyzes the impact of risk and ambiguity aversion - Knightian uncertainty - on the choice of optimal quality and timing of market entry in the agri-food sector. Irreversibility of the investment in product development is introduced in a continuous-time stochastic model applying the real option literature. We consider a market characterized by a duopoly with a Stackelberg-Nash game for quality choice. When the follower provides a higher- quality good, the level of quality is decreasing in ambiguity aversion while it is a non-monotonic function of the level of risk. For low levels of risk, the increase of product quality is an efficient response. Up to certain threshold level of risk, risk and ambiguity aversion reduce the optimal quality level and increase the value of waiting when the follower supplies a higher-quality good. The implication is that risk and ambiguity aversion allow the leader to make a sustainable monopoly pro t. When the follower supplies a lower-quality good, there is no value for it to wait. It should therefore provide the lowest-quality good possible. In a vertically integrated supply chain rms provide higher quality, and the di¤erence between vertically integrated and non-integrated rms is increasing in risk and ambiguity aversion.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.102
GPT teacher head0.271
Teacher spread0.169 · 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 designObservational
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

Citations3
Published2012
Admission routes1
Has abstractyes

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