The “Inherent Vices” of Policy Design: Uncertainty, Maliciousness, and Noncompliance
Bibliographic record
Abstract
Policy designs must not only "work" in the sense of accomplishing their goals but must also work in their intended fashion. Most research to date has focused on the former topic and dwells on the technical aspects of how various tools and instruments could be utilized to achieve the aims and goals of policymakers. This branch of research tends to underemphasize the difficulties inherent to policy making including policy contexts that are often highly uncertain, policymakers who fall short of an idealized version of high capacity, well-intentioned decisionmakers grappling with relevant public problems, and policy-takers who fail to comply with government wishes. These "inherent vices" of policy making are factors which contribute to policy volatility or the risk of policy failure. The paper stresses the need for improved risk management and mitigation strategies in policy formulation and policy designs to take these risks into account. It sets out and develops an approach borrowed from product failure management (in manufacturing) and portfolio management (in finance) to help better assess and manage these risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.150 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| 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".