Bibliographic record
Abstract
We take a behavioral approach to decision-making and, apply it across species.First we review quantitative theories that provide good accounts of both non-human and human choice, as, for example, in operant analogues to foraging (including the optimal diet model and delay-reduction theory).Second we show that for all species studied, organisms will acquire observing responses, whose only function is to produce stimuli correlated with the schedule of reinforcement in effect.Observing responses are maintained only by "good news": "no news" is preferred to "bad news".We then review two areas of decision-making in which human participants (but not necessarily non-humans) tend to make robust errors of judgment or to approach decisions non-optimally.The first area is the sunk-cost effect in which participants persist in a losing course of action, ignoring the currently operative marginal utilities.The second area is base-rate neglect in which participants overweight case cues (such as witness testimony or medical diagnostic tests) and underweight information about the base rates or probabilities of the events in question.In both cases we argue that the poor decisions we make are affected by the misapplication of previously learned rules and strategies that have utility in other situations.These conclusions are strengthened both by the behavioral approach taken and by the data revealed in cross-species comparisons.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".