Capturing Non-linear Judgment Policies Using Decision Tree Models of Classification Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Policy capturing is a decision analysis method that typically uses linear statistical modeling to estimate the basis of expert judgments. Using more flexible data mining algorithms may yield more accurate models or instead result in poor functional estimations. The objective of this study is to test the effectiveness of a decision tree induction algorithm for policy capturing in comparison to the standard linear approach. We examined human classification behavior using a simulated naval air-defense task in order to empirically compare the C4.5 decision tree algorithm to linear regression on their ability to capture individual decision policies. The pattern of results shows that C4.5 outperformed linear regression in terms of goodness-of-fit and cross-validation accuracy. Results also show that the decision tree models of individuals’ judgment policies actually classified contacts more accurately than their human counterparts. We conclude that non-linear policy capturing can yield useful models for training and decision support applications.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it