Simulating the Principal‐Agent Relationship between Enterprise Owners and Professional Managers Using Evolutionary Game Theory and System Dynamics
Bibliographic record
Abstract
The separation of ownership and management is a common operation mode in modern enterprises, which establishes the principal‐agent relationship between modern enterprise owners and professional managers. Due to the information asymmetry and interest conflicts between the principal and agent, the principal‐agent problem will occur and affect the efficiency of enterprise operations. Therefore, it is necessary to propose measures to improve the principal‐agent relationship. This paper analyzed the principal‐agent problem between enterprise owners and professional managers based on system dynamics, evolutionary game, and principal‐agent theory and built a principal‐agent evolutionary game model to analyze the rule of strategic choices and predict the equilibrium outcomes of different scenarios. In addition, the influence of different factors on strategic choices was simulated by the system dynamics model. The results depicted that the basic benefits and costs of cooperation are the key factors of the strategic choices, and the gap between the expected payoffs of different strategies also affects the probability of choosing those cooperative strategies. Proper supervision, standardization of the managerial labor market, and establishment of long‐term incentives are crucial to cooperation between enterprise owners and professional managers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".