L’exploration des ressources extractives non renouvelables : théorie économique, processus stochastique et vérification
Bibliographic record
Abstract
Most of the literature devoted to the "theory of the mine" has been developed under certainty. It has been unable to explain the activity of exploration. The stochastic models of exploration were developed quasi-independently from economic theory. The purpose of this article is to survey both the mining and economic literature related to the "theory of the mine" under uncertainty and the exploration models since the turn of the century. The survey is complementary to the one made in this journal by F. Peterson and A.C. Fisher. The first part defines exploration as being essentially a search and information gathering activity. It reviews the contributions to the economic theory of exploration and resource stock uncertainty. It compares the optimal extraction path and the life cycle of the mine under stock uncertainty and stock certainty. It shows in particular that increasing the rate of discount is generally inappropriate to take account of stock uncertainty. Some partial equilibrium results on exploration are given as well. The presence of stock uncertainty or exploration in a general equilibrium model is shown to jeopardize the optimality of competitive allocations. The second part points out the wealth of the theoretical and empirical analysis of exploration as a stochastic process. It first reviews the literature on size distributions of reserves which gives strong theoretical and empirical support to the lognormal hypothesis. It then goes on to the exploration models which roughly speaking can be broken down into two groups. The Allais type models, better suited for relatively unexplored regions, which combine a Poisson or negative binomial process for discovery with a lognormal distribution for sizes. The Arps-Roberts-Kaufman type models, more adequate for "mature" regions, assume exhaustive sampling with probability proportional to size of discovery. Generally the treatment of the discovery process, to be distinguished from the sampling for sizes, and the handling of geological information are still woefully inadequate. The third and last part of the survey points out the gap which exists in the microeconomic literature about the study of random inputs. It suggests that the theory of dams and insurance and the theory of search especially adaptive search could be fruitfully used. Problems which remain to be tackled are the influence of stock uncertainty on grade of ore mined and on investment in capacity.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".