Judgment Analysis in a Dynamic Multitask Environment: Capturing Nonlinear Policies Using Decision Trees
Why this work is in the frame
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Bibliographic record
Abstract
Policy capturing is a judgment analysis method that typically uses linear statistical modeling to estimate expert judgments. A variant to this technique is to capture decision policies using data-mining algorithms designed to handle nonlinear decision rules, missing attributes, and noisy data. In the current study, we tested the effectiveness of a decision-tree induction algorithm and an instance-based classification method for policy capturing in comparison to the standard linear approach. Decision trees are relevant in naturalistic decision-making contexts since they can be used to represent “fast-and-frugal” judgment heuristics, which are well suited to describe human cognition under time pressure. We examined human classification behavior using a simulated naval air defense task in order to empirically compare the C4.5 decision-tree algorithm, the k-nearest neighbors algorithm, and linear regression on their ability to capture individual decision policies. Results show that C4.5 outperformed the other methods in terms of goodness of fit and cross-validation accuracy. Decision-tree models of individuals’ judgment policies actually classified contacts more accurately than their human counterparts, resulting in a threefold reduction in error rates. We conclude that a decision-tree induction algorithm can yield useful models for training and decision support applications, and we discuss the application of judgmental bootstrapping in real time in dynamic environments.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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