Minimally Restrictive Decision Support Systems
Bibliographic record
Abstract
Decision-making behavior is heterogeneous. We therefore suggest building a decision support system for online purchase decisions that can support various different decision strategies and also allows users to mix them. We design this minimally restrictive system by decomposing different strategies into their component steps and implementing aids for supporting these steps. We empirically compare this system to a typical one, which restricts the users by supporting only one normative, utility-maximizing strategy. Users perceive the minimally restrictive system as requiring lesser effort and being more enjoyable to use than the typical decision support system. Furthermore, they exhibit a higher intention to re-use the minimally restrictive system. However, there is no difference with respect to the perceived usefulness. Our results imply that webstores should implement minimally restrictive systems not only because of high user satisfaction, but also because analyzing clicks on the aids provides information as to which strategies are being used.
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How this classification was reachedexpand
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.009 |
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; both teacher heads agree on what is shown here.
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".