Bibliographic record
Abstract
We consider a benchmark static incentive scheme, i.e. a per unit subsidy, that induces a monopoly to produce a target output level. We show that the same output level can be achieved by a continuum of dynamic subsidy rules based on a performance indicator. The subsidy rules require only local information. The present value of the subsidies paid under anyone of our dynamic schemes is smaller than the amount paid under the static subsidy. Moreover, each of the dynamic subsidy rules results, at each moment, in a lower per unit subsidy than the static subsidy. The subsidy rate depends on a state variable that reflects the monopolist's history of performance. This variable depreciates over time, therefore requiring a permanent effort of the monopolist to maintain it at an optimal level. In an example with a linear demand, the subsidy costs of inducing efficiency are reduced by almost fifty per cent.We show that the cost of sorting and the network effects jointly determine the rate of participation of consumers in the process of recycling. The dominant producer of virgin material takes into account the recycling activities when it makes its pricing decision. The network effects can create multiplicity of steady-state equilibria. The government can improve welfare by influencing equilibrium selection.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".