MétaCan
Menu
Back to cohort
Record W3124661297

Risk aversion and framing effects

2012· preprint· en· W3124661297 on OpenAlexaff
Louis Lévy‐Garboua, Hela Maafi, David Masclet, Antoine Terracol

Bibliographic record

VenueRePEc: Research Papers in Economics · 2012
Typepreprint
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsLotteryFraming effectFraming (construction)NormativeStochastic gameRisk aversion (psychology)PsychologySocial psychologyEconomicsLoss aversionIncentiveEconometricsSalientMicroeconomicsExpected utility hypothesisMathematical economicsComputer sciencePolitical scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

We present a new experimental evidence of how framing affects decisions in the context of a lottery choice experiment for measuring risk aversion. We investigate framing effects by replicating the Holt and Laury's (Am. Econ. Rev. 92:1644-1655, 2002) procedure for measuring risk aversion under various frames. We first examine treatments where participants are confronted with the 10 decisions to be made either simultaneously or sequentially. The second treatment variable is the order of appearance of the ten lottery pairs. Probabilities of winning are ranked either in increasing, decreasing, or in random order. Lastly, payoffs were increased by a factor of ten in additional treatments. The rate of inconsistencies was significantly higher in sequential than in simultaneous treatment, in increasing and random than in decreasing treatment. Both experience and salient incentives induce a dramatic decrease in inconsistent behaviors. On the other hand, risk aversion was significantly higher in sequential than in simultaneous treatment, in decreasing and random than in increasing treatment, in high than in low payoff condition. These findings suggest that subjects use available information which has no value for normative theories, like throwing a glance at the whole connected set of pairwise choices before making each decision in a connected set of lottery pairs.

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)0.014
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.004
Research integrity0.0010.003
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 head0.091
GPT teacher head0.415
Teacher spread0.324 · 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; a candidate call from one teacher head, not a consensus.

Study designOther design
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

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicDecision-Making and Behavioral EconomicsFrench-language works237,207