Monte carlo algorithms for expected utility estimation in dynamic purchasing
Why this work is in the frame
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Bibliographic record
Abstract
This thesis describes a theory for decision-making in a dynamic purchasing environment where one of possibly many bundles of items must be purchased from possibly many suppliers. The online combinatorial purchasing problem is defined as the problem with which a purchase agent in such an environment is faced when deciding which combination of items, from whom and at what time to buy in order to maximize overall satisfaction. Expected utility maximization is used as the criterion on which decision-making is based. To facilitate the exchange of probabilistic and temporal information between suppliers and purchasers, the PQR protocol is defined. The theory considers two distinct purchasing models, one in which only complete bundle purchases can be made at any time, and one in which partial bundle purchases are allowed. In the first model, a decision procedure that exploits future time intervals where several options will be available is developed that provably yields higher expected utility than simply pursuing the bundle with highest expected utility. For the second model, the QR-tree is defined as a decision tree that can be exponentially smaller than conventional decision trees when used to model the same system of decisions. Efficient Monte Carlo methods are developed that solve the QR-tree in linear time on the number of nodes in the tree. Results show that these methods estimate expected utility as much as 25 times more accurately than a greedy method that always pursues the bundle with the current highest expected utility.
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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 | 0.001 |
| 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