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| Diskrete Wahl Simulation× | Monte-Carlo-Simulation× | |
|---|---|---|
| Fachgebiet≠ | Simulation | Entscheidungsfindung |
| Familie≠ | Process / pipeline | MCDM |
| Entstehungsjahr≠ | 1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s | 1949 |
| Urheber≠ | Daniel McFadden (random utility theory); Kenneth Train (simulation methods) | Metropolis, N., Ulam, S. |
| Typ≠ | Discrete choice modelling with Monte Carlo simulation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Wegweisende Quelle≠ | Train, K.E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliasnamen≠ | stated preference simulation, SP simulation, revealed preference modelling, Ayrık Seçim Simülasyonu (Stated Preference / SP Simulation) | — |
| Verwandt≠ | 5 | 0 |
| Zusammenfassung≠ | Discrete choice simulation is a behavioural modelling method — grounded in random utility theory formalised by Daniel McFadden in the 1970s and extended to simulation-based estimation by Kenneth Train — that estimates how individuals choose among mutually exclusive alternatives and then uses those estimated preference parameters to forecast how choice shares would shift under hypothetical policy or market scenarios. It is the dominant quantitative tool in transport demand analysis, health economics, environmental valuation, and marketing research. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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