Risk aversion and framing effects
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".