Abstract
Understanding and managing complex systems has become one of the biggest challenges for research, policy and industry. Modeling and simulation of complex systems promises to enable us to understand how a human nervous system and brain not just maintain the activities of a metabolism, but enable the production of intelligent behavior, how huge ecosystems adapt to changes, or what actually influences climatic changes. Also man-made systems are getting more complex and difficult, or even impossible, to grasp. Therefore we need methods and tools that can help us in, for example, estimating how different infrastructure investments will affect the transport system and understanding the behavior of large Internet-based systems in different situations. This type of system is becoming the focus of research and sustainable management as there are now techniques, tools and the computational resources available. This chapter discusses modeling and simulation of such complex systems. We will start by discussing what characterizes complex systems.
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Abbreviations
- 2-D:
-
two-dimensional
- ABS:
-
agent-based simulation
- ACT-R:
-
Adaptive Control of Thought-Rational
- BDI:
-
belief, desire, intention
- BOID:
-
beliefs, obligations, inentions and desires
- CORMAS:
-
common-pool resources and multi-agent systems
- DEVS:
-
Discrete Event System Specification
- GIS:
-
geographical information system
- MABS:
-
multi-agent-based simulation
- MASON:
-
multi-agent simulator of neighborhoods
- PSI:
-
principles of synthetic intelligence
- SeSAm:
-
Shell for Simulated Agent Systems
- UML:
-
unified modeling language
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Davidsson, P., Klügl, F., Verhagen, H. (2017). Simulation of Complex Systems. In: Magnani, L., Bertolotti, T. (eds) Springer Handbook of Model-Based Science. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-30526-4_35
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DOI: https://doi.org/10.1007/978-3-319-30526-4_35
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