Bibliographic record
Abstract
This book deals with the theory and applications of noncooperative differential games. A noncooperative game is a strategic situation in which decision makers (from now on: players) cannot make binding agreements to cooperate. In a noncooperative game, the players act independently in the pursuit of their own best interests. Confining our interest to noncooperative games should not be seen as an indication that cooperative games are less interesting. The reason simply is that, in the area of differential games, cooperative theory is far less developed than noncooperative theory and almost all applications in economics and management science are in the noncooperative setup. We do not deal with zero-sum games (these are games in which the players have completely opposite interests, that is, the gain for one player equals the loss for another player), because the zero-sum assumption is only plausible in rather special situations in economics and management science. Differential games belong to a subclass of dynamic games called state space games. In a state space game, the modeller introduces a set of (state) variables to describe the state of a dynamic system at any particular instant during play. The hypothesis is that the payoff-relevant influence of past events is adequately summarized in the state variables. To illustrate, the state vector may consist of the current capital stocks of N oligopolistic firms and these stocks can be influenced by the firms through the choices of their individual investment rates.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.394 | 0.284 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".