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| Deterministische agentenbasierte Modellierung× | Agentenbasiertes Modellieren (ABM)× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1996 | 1970s–1990s (formalized as a field) |
| Urheber≠ | Epstein, J. M. & Axtell, R. | Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s) |
| Typ≠ | Computational simulation — deterministic rule-based agents | Computational simulation method |
| Wegweisende Quelle≠ | Epstein, J. M., & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. MIT Press. ISBN: 9780262550253 | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. DOI ↗ |
| Aliasnamen | D-ABM, Deterministic ABM, Rule-Based Agent Simulation, Fixed-Rule Agent-Based Model | ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | Deterministic Agent-Based Modeling (D-ABM) is a computational simulation approach in which autonomous agents follow fully specified, non-random behavioral rules within a structured environment. Every run with identical initial conditions produces identical outcomes, making the model fully reproducible and transparent for analysis of emergent system behavior without stochastic noise. | 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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