Acceptable Input: Using Decision Analysis to Guide Public Policy Deliberations
Bibliographic record
Abstract
Multiparty deliberative processes have become a popular way to increase public participation in public policy choices. Their legitimacy depends on participants' ability, first, to understand the issues facing them and, then, to form and express their own positions on them. These tasks pose significant cognitive and emotional challenges. This paper argues that decision analysis, informed by behavioral decision research, offers procedures and standards for creating responsible deliberative processes. These involve (a) formal analysis of decisions, identifying the kernel of most relevant information, (b) communication procedures, recognizing the strengths and weaknesses of lay understanding, and (c) interactive elicitation methods, helping individuals to articulate the implications of their values for specific settings. A construct validity criterion assesses the extent to which the resulting valuations are properly sensitive to decision features. Feasible extensions of traditional decision analysis create opportunities to formalize the aspirations of participants and ensure that the intellectual content of deliberative processes is worthy of the political hopes vested in them.
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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 | 0.050 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".