Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Programación Dinámica Basada en Agentes× | Programación Dinámica Estocástica× | |
|---|---|---|
| Campo | Simulación | Simulación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1957 (DP); 1990s onward (ABM integration) | 1957 |
| Autor original≠ | Bellman, R. (DP foundation); Tesfatsion, L. et al. (ABM-DP integration) | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Tipo≠ | Hybrid simulation-optimization | Sequential optimization under uncertainty |
| Fuente seminal | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780691079516 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Alias | ABDP, Agent-based DP, Multi-agent dynamic programming, ABM-DP | SDP, Markov Decision Process, MDP, Stochastic DP |
| Relacionados≠ | 5 | 6 |
| Resumen≠ | Agent-based dynamic programming (ABDP) embeds Bellman's dynamic programming framework within individual agents of an agent-based model, enabling each agent to solve sequential, multi-stage decision problems using backward induction or value-function iteration. The result is a population of optimizing agents whose interactions generate emergent system-level behavior. | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. |
| ScholarGateConjunto de datos ↗ |
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