Nudge Versus Boost: Agency Dynamics Under Libertarian Paternalism
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Thaler and Sunstein (2008) advance the concept of ‘nudge’ policies—non-regulatory and non-fiscal mechanisms designed to enlist people's cognitive biases or motivational deficits so as to guide their behaviour in a desired direction. A core assumption of this approach is that policymakers make artful use of people's cognitive biases and motivational deficits in ways that serve the ultimate interests of the nudged individual. We analyse a model of dynamic policymaking in which the policymaker's preferences are not always aligned with those of the individual. One novelty of our set-up is that the policymaker has the option to implement a ‘boost’ policy, equipping the individual with the competence to overcome the nudge-enabling bias once and for all. Our main result identifies conditions under which the policymaker chooses not to boost in order to preserve the option of using the nudge (and its associated bias) in the future—even though boosting is in the immediate best interests of both the policymaker and the individual. We extend our analysis to situations in which the policymaker can be removed (e.g., through an election) and in which the policymaker is similarly prone to bias. We conclude with a discussion of some policy implications of these findings.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.018 |
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