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Record W1592024835 · doi:10.1002/mde.2723

Strategic Experimentation in the Lab

2015· article· en· W1592024835 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

VenueManagerial and Decision Economics · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPessimismSpillover effectWeightingPrior probabilityOutcome (game theory)Nash equilibriumStrategic interactionEconomicsExperimental economicsProspect theoryMathematical economicsStrategic complementsEconometricsComplete informationGame theoryMicroeconomicsStatisticsMathematics

Abstract

fetched live from OpenAlex

This paper reports the results of experimental tests of the Nash equilibrium predictions in a one‐armed bandit game with information spillover. Players learn the probability that a risky prospect pays by either taking draws from the distribution themselves or observing the outcome of another player's choice. Our experiment is designed to learn whether players experiment strategically, anticipating the opportunity to free‐ride on others' information and doing so. While error rates exhibit a bias toward under‐experimentation, we observe a significant strategic effect. Structural parameter estimates suggest the lack of experimentation observed is due to decision error and somewhat pessimistic priors, rather than risk preferences or probability weighting. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.117
GPT teacher head0.363
Teacher spread0.246 · 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