方法对比
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| 模拟辅助的事后设计× | 基于主体的建模(ABM)× | |
|---|---|---|
| 领域≠ | 研究设计 | 仿真 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | Ex post facto: 1964; simulation-assisted hybrid: 1990s–2000s | 1970s–1990s (formalized as a field) |
| 提出者≠ | Kerlinger, F. N. (ex post facto basis); simulation integration drawn from computational social science (Axelrod, Epstein, 1990s) | Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s) |
| 类型≠ | Non-experimental observational design with computational augmentation | Computational simulation method |
| 开创性文献≠ | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. DOI ↗ |
| 别名 | simulation-enhanced causal-comparative design, ex post facto with simulation, retrospective simulation design, SAEPF design | ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling |
| 相关≠ | 4 | 5 |
| 摘要≠ | Simulation-assisted ex post facto design is a non-experimental observational approach in which the researcher examines already-occurred events or conditions using existing records and then supplements the empirical analysis with computational simulation to approximate counterfactual scenarios that cannot be observed in reality. The design retains the retrospective, naturalistic character of classic ex post facto research while leveraging agent-based, Monte Carlo, or system-dynamics simulation to address the inherent confound limitations of purely archival work. | Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone. |
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