Application of Nash Equilibrium: Taking the Game Between Enterprises as an Example
Why this work is in the frame
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Bibliographic record
Abstract
The game theory of microeconomics is one of the important analysis and decision-making tools for managing enterprises. Von Neumann discusses the zero-sum game of two people, while Nash discusses a broader range of games. This paper aims to explore the important concept of Nash Equilibrium in game theory. With the wide application of Nash Equilibrium in different fields such as economics, politics, psychology and machine learning, it is becoming increasingly important to understand and apply Nash Equilibrium. The author first introduces the concept and mathematical definition of Nash Equilibrium, and then takes the "Prisoner's Dilemma" game as an example to elaborate its application methods and significance in detail. Subsequently, the author discusses the limitations of Nash Equilibrium, including the inability to guarantee the maximum profit, and proposes corresponding solutions. Finally, the author explores the applications of Nash Equilibrium in different fields, as well as the prospects in machine learning and artificial intelligence fields.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it