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Record W4288033014 · doi:10.1257/mic.20200115

Investment and Information Acquisition

2022· article· en· W4288033014 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

VenueAmerican Economic Journal Microeconomics · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsPersuasionInvestment (military)Principal (computer security)Value (mathematics)MicroeconomicsEconomicsBusinessComputer sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

We study the interaction between productive investment and persuasion activities in a principal–agent setting with strategic disclosure. In an attempt to persuade the principal, the agent diverts substantial resources from productive activities to information acquisition for persuasion, even though productive activities are more efficient and raise the chances of success in persuasion. The equilibrium outcomes of simultaneous and sequential allocation procedures are the same, because the value of learning and experimentation through information acquisition is dominated by the value of productive investment. We show that an increase in cost of an investment project leads to a lower productive investment. (JEL D21, D82, D83, D86, G31)

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.295
Teacher spread0.278 · 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