Uncertain Search with Knowledge Transfer
Bibliographic record
Abstract
We consider a sequential search over a group of similar alternatives. The individual value of an alternative contains two components: an observable utility and an idiosyncratic value. Observable utilities share an unknown population distribution, which captures the similarity across the alternatives and allows for knowledge transfer within the group. Once a decision maker encounters an alternative, its utility is revealed immediately, whereas the idiosyncratic value is unobservable and needs to be learned by sampling. The goal is to select an alternative with the highest individual value, accounting for the sampling and search costs. A novel feature of this problem is the combination of the individual and population levels of learning. We formulate the problem as a Bayesian dynamic program and characterize the optimal policy by a threshold structure. We show that it depends on the difference between the mean estimates of the current alternative and the population. It is optimal to continue sampling if the difference is between a threshold pair; otherwise, accept the current alternative if it exceeds the upper threshold, and switch to a new one if it is below the lower threshold. Other structural properties are also derived to shed light on the effects of the two levels of learning. A key insight is that more uncertainty is preferable at the individual level, but less uncertainty is preferable at the population level. Various practical variants of the problem are also considered. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Funding: W. T. Huh acknowledges support from the NSERC Discovery Grants (RGPIN 2020-04213) and the Canada Research Chair Program. The work of M. J. Kim was supported by the Natural Sciences and Engineering Research Council of Canada [NSERC Discovery Grant RGPIN-2024-05213]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00309 .
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".