How long to eat a cake of unknown size? Optimal time horizon under uncertainty
Bibliographic record
Abstract
This paper is concerned with the determination of the optimal time horizon for the cake–eating problem under uncertainty. It is shown that if the uncertain exhaustible resource stock is a discrete random variable admitting at most a finite number of values, the optimal planning horizon is infinite (finite) according as the marginal utility of extraction–cum–consumption is infinite (a finite positive value) as the latter approaches zero, thereby extending the scope of the similar result under perfect certainty. Other results show that uncertainty will generally lengthen the planning horizon, implying a more conservative extraction policy under uncertainty, and that the extraction policy aimed at extracting an amount equal to the expected value of the uncertain resource stock takes longer than the expected value of the optimal planning horizon. JEL Classification: D81 and Q31 Combien de temps pour manger un gâteau de taille inconnue? L’horizon temporel optimal en régime d’incertitude. Ce mémoire s’attaque à la détermination de l’horizon temporel optimal dans le cas du problème du gâteau–à–manger en régime d’incertitude. On montre que si le stock incertain de la ressource épuisable est une variable aléatoire discontinue qui ne peut prendre qu’un nombre fini de valeurs, l’horizon temporel est infini (fini) selon que l’utilité marginale de l’extraction–cum–consommation est infinie (prend une value finie positive) quand celle–ci approche zéro, et ce faisant élargit la portée d’un résultat similaire obtenu en régime de certitude parfaite. D’autres résultats montrent que l’incertitude accroît généralement l’horizon temporel, ce qui suggère qu’une politique d’extraction plus conservatrice va prévaloir en régime d’incertitude, et que la politique d’extraction visant à extraire une quantitéégale à la valeur anticipée d’un stock de ressource incertain prend plus de temps que la valeur anticipée de l’horizon temporel optimal.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".