Methoden vergleichen

	Simulationsgestütztes Ex-post-facto-Design	Agentenbasiertes Modellieren (ABM)
Fachgebiet	Forschungsdesign	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	Ex post facto: 1964; simulation-assisted hybrid: 1990s–2000s	1970s–1990s (formalized as a field)
Urheber	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)
Typ	Non-experimental observational design with computational augmentation	Computational simulation method
Wegweisende Quelle	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Aliasnamen	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
Verwandt	4	5
Zusammenfassung	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.