Preference elicitation for risky prospects
Why this work is in the frame
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Bibliographic record
Abstract
Minimax-regret preference elicitation allows intelligent decisions to be made on behalf of people facing risky choices. Standard gamble queries, a vital tool in this type of preference elicitation, assume that people, from whom preference information is being elicited, can be modeled using expected utility theory. However, there is strong evidence from psychology that people may systematically deviate from expected utility theory. Cumulative prospect theory is an alternative model to expected utility theory which has been shown empirically, to better explain humans ’ decision making in risky settings. We show that the current minimaxregret preference elicitation techniques can fail to properly elicit appropriate information if the preferences of the user follow cumulative prospect theory. As a result, we develop a new querying method for preference elicitation that is applicable to cumulative prospect theory models. Simulations show that our method can effectively elicit information for decision making in both cumulative prospect theory and expected utility theory settings, resulting in a flexible and effective preference elicitation method.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it