Not just noise: A goal pursuit interpretation of stochastic choice.
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
This paper contributes to our understanding of individual decision making by testing the proposal that differential weighting of 2 (or more) goals can be an important factor leading to stochastic (probabilistic) choice. The tested models follow from the endogenous maximum entropy program (eMEP) paradigm (Swait & Marley, 2013), which proposes that stochastic choice is (partially or entirely) a consequence of balancing multiple goals. That framework leads to an interpretation of the scale factor in classic random utility models (such as the multinomial logit [MNL]) as an endogenous property of a decision maker—an interpretation that is in stark contrast to the standard interpretation of the scale as due to heterogeneity or other “noise.” The new perspective is supported by data from a task that manipulates (by a prime) an individual’s propensity to be either consistent or to seek variety, suggesting that balanced pursuit of exploitation and exploration goals is a reasonable interpretation of stochastic choice.
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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 | 0.001 |
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