Bibliographic record
Abstract
The ideas underlying game theory have appeared throughout history, in the Bible, the Talmud, the works of Descartes and Sun Tzu, and the writings of Chales Darwin. Modern game theory, however, can be considered as an outgrowth of three seminal works: Augustin Cournot's Research into the Mathematical Principles of the Theory of Wealth in 1838 gives an intuitive explanation of what would eventually be formalized as the Nash equilibrium, as well as provides an evolutionary, or dynamic notion of best-response to the actions of others. Francis Ysidro Edgeworth's Mathematical Psychics demonstrated the notion of competitive equilibria in a two-person (as well as two-type) economy; Emile Borel, in “Algèbre et calcul des probabilités,” Comptes Rendus Académie des Sciences , vol. 184, 1927, provided the first insight into mixed strategies that randomization may support a stable outcome. While many other contributors hold a place in the history of game theory, it is widely accepted that modern analysis began with John von Neumann and Oskar Morgenstern's book, Theory of Games and Economic Behavior . Then building on von Neumann and Morgenstern's results John Nash developed the modern framework for methodological analysis. Depending on the nature of the different approaches, there are different possible applications of game theory. If the information is strictly limited to local information, the non-cooperative game might be the only choice for each individual to play. However, such a game might have a very low-efficiency outcome. To overcome this problem, pricing or referee approaches have been proposed. If the users care about long-term benefits, the repeated game can be employed to enforce cooperation by the threat of future punishment from others.
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 | 0.001 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".