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Systematic Learning and Pedagogy Through a Modular, Progressive-Based Simulation Modeling Framework for Complex Systems
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DOI：10.1002/sys.70047.png)
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Abstract
En 中文
Smart Transportation Systems (SmTS) will exceed complex systems with their classification as complex, sociotechnical, and AI-based systems. Possessing a toolkit that helps systemically understand, analyze, and assess these systems will be advantageous in the early stages of the systems engineering (SE) lifecycle, and Agent Based Models (ABMs) could provide this alleviation. This paper proposes how lessons learned from a novice ABM developer can help create a novel framework that uncovers complex system behaviors and bolsters simulation modeling learning mechanisms for engineers. A knowledge base of StarLogo and NetLogo was developed using SmTS as a system of interest (SoI). A comparative assessment of StarLogo and NetLogo was performed, creating an expandable toolkit called the Simulation Modeling Pipeline Framework (SMPF). The SmTS was then utilized as a case study for applying the SMPF as a learning tool for simulation model prototyping and development. Results showed StarLogo and NetLogo are exceptional ABMs for novice ABM developers, however each comes with their strengths and weaknesses. StarLogo programming language is conducive for rapid code generation but lacks lower modeling fidelity. NetLogo consummates StarLogo's weaknesses, however its proprietary programming language is cumbersome for beginners. These complementary characteristics helped form the SMPF by connecting model abstractions of disparate ABMs, facilitating systematic-based learning, prototyping, and development of simulation models. By using two ABMs; a toolkit for simulation modeling of emerging and/or nonexistent complex systems can be developed and utilized in learning ABM development, while cultivating a deeper comprehension of system mechanisms at various levels for a SoI.
Keywords:
agent-based modeling and simulation
complex systems
smart transportation systems
sociotechnical systems
systematic learning
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