Cognition in the woods: Biases in probability judgments by search and rescue planners
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
Abstract A type of emergency decision-making which has not received research attention is the police search for a lost person in a rural or wilderness area. For many such incidents, decisions concerning where to search for the lost subject are made by a planning team, each member of which assigns probabilities to the various hypotheses about where the subject might be located, including the residual hypothesis that the subject is somewhere else entirely, that is, outside of the designated search area. In the current study, 32 adult males with search planning experience were asked to assign probabilities to a fictional lost person incident. It was hypothesized, according to support theory (Tversky & Koehler, 1994), that subjects who first considered the five possible scenarios accounting for how the subject could have left the search area—i.e., unpacked the residual hypothesis—would subsequently increase their probability estimate of the global hypothesis that the missing subject was not in the designated search area, compared to those subjects who unpacked the focal hypothesis. This hypothesis was confirmed. We also found considerable evidence for subadditivity , as most subjects estimated higher summed probabilities for the individual scenarios accounting for the focal and residual hypotheses, respectively. The potential negative consequences of such unpacking effects during a lost person incident were discussed, and possible means of mitigating such effects were described.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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