Decisional enhancement and autonomy: public attitudes towards overt and covert nudges
Bibliographic record
Abstract
Abstract Ubiquitous cognitive biases hinder optimal decision making. Recent calls to assist decision makers in mitigating these biases—via interventions commonly called “nudges”—have been criticized as infringing upon individual autonomy. We tested the hypothesis that such “decisional enhancement” programs that target overt decision making—i.e., conscious, higher-order cognitive processes—would be more acceptable than similar programs that affect covert decision making—i.e., subconscious, lower-order processes. We presented respondents with vignettes in which they chose between an option that included a decisional enhancement program and a neutral option. In order to assess preferences for overt or covert decisional enhancement, we used the contrastive vignette technique in which different groups of respondents were presented with one of a pair of vignettes that targeted either conscious or subconscious processes. Other than the nature of the decisional enhancement, the vignettes were identical, allowing us to isolate the influence of the type of decisional enhancement on preferences. Overall, we found support for the hypothesis that people prefer conscious decisional enhancement. Further, respondents who perceived the influence of the program as more conscious than subconscious reported that their decisions under the program would be more “authentic”. However, this relative favorability was somewhat contingent upon context. We discuss our results with respect to the implementation and ethics of decisional enhancement.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".