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This chapter explores the crucial role of modeling methods in MBST for understanding complex real-world problems viewed as systems. Modeling improves our mental nodels by capturing how the purpose, structure, and behavior of systems are interconnected and influence each other. The modeling methods discussed in more detail are architectural(MBSE and DSM), system dynamics, agent based, stochastic, optimization, and AI/ML. Here, we emphasize the importance of integrating systems thinking principles into these models to understand difficult to predict issues and improve our decision-making processes. We include practical examples and case studies, such as wildfire UAV fleet deployment and urban water and energy management, to give the reader a feel of how the various modeling methods help. We close the chapter and the book by discussing the potential benefits and challenges of using AI/ML (circa fall 2024) in systems thinking, advocating for its use as a supplemental tool to enhance the understanding and analysis of complex real-world problems viewed as systems.

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Models for MBST Framework

  • 章节

  • 发表日期：

    2025年

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  • Kamran Eftekhari Shahroudi,
  • Steven Conrad,
  • Jill Speece,
  • Kirk Reinholtz,
  • Martin “Trae” Span,
  • Sarwat Chappell,
  • Quentin Saulter,
  • Golam M Bokhtier

摘要

This chapter explores the crucial role of modeling methods in MBST for understanding complex real-world problems viewed as systems. Modeling improves our mental nodels by capturing how the purpose, structure, and behavior of systems are interconnected and influence each other. The modeling methods discussed in more detail are architectural(MBSE and DSM), system dynamics, agent based, stochastic, optimization, and AI/ML. Here, we emphasize the importance of integrating systems thinking principles into these models to understand difficult to predict issues and improve our decision-making processes. We include practical examples and case studies, such as wildfire UAV fleet deployment and urban water and energy management, to give the reader a feel of how the various modeling methods help. We close the chapter and the book by discussing the potential benefits and challenges of using AI/ML (circa fall 2024) in systems thinking, advocating for its use as a supplemental tool to enhance the understanding and analysis of complex real-world problems viewed as systems.