Objectives and competences
- Depict the role of modeling and simulation in today's science at the complex systems analysis
- Teach the students the methods and techniques of modeling and simulation in the sense of hybrid models
- Determination of complex simulation models application in the form of dynamical simulators
- Present the field of model application, effectiveness and facets at the support of business decisions
- Study of the different methods of validation and verification of complex models and interpretation of results
- Study of nonlinear systems in the field of organizational sciences
Content (Syllabus outline)
- Complex system simulation paradigm
- Classification of mathematical and simulation models
- Application fields of continuous, discrete event and agent-based models
- Qualitative and quantitative modeling of continuous systems: directed graphs, system dynamics, nonlinear chaotic systems, sensitivity analysis
- Discrete event simulation
- Random generators and statistical distributions
- Modeling with event graphs
- Process oriented models
- Modeling with Petri nets
- Agent based models
- Modeling of Cyber-physical systems
- Hybrid models
- Validation of models
- Experimental design and result analysis
Learning and teaching methods
- lectures
- seminar
- tutorial
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
• Ability to define the specific problem as the simulation model by the application of hybrid simulation techniques
• Analysis of data and design of experiments
• Knowledge and understanding of simulation tools and their application
• Understanding of different simulation techniques and areas of application
• Mastering of holistic development of systems for decision support based on the hybrid simulation techniques
Intended learning outcomes - transferable/key skills and other attributes
- Foster interdisciplinary collaboration between academia, research and industry experts at the problem solving with the methods of simulation
- Mastering of holistic development of systems for decision support based on the hybrid simulation techniques
Readings
- Strogatz S. (2014) Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Studies in Nonlinearity), Westview Press.
- Sayama H (2015) Introduction to the Modeling and Analysis of Complex Systems, Open SUNY Textbooks
- Miller J.H. (2016) A Crude Look at the Whole: The Science of Complex Systems in Business, Life, and Society, Basic Books
- Mobus G.E. Kalton M.C. (2015) Principles of Systems Science (Understanding Complex Systems), Springer
- Gros, C. (2009) Complex and Adaptive Dynamical Systems: A primer. Springer, New York.
- Severance, F. L. (2001) System Modeling and Simulation: An Introduction, John Wiley & Sons, Chichester.
- Zeigler, P. (2000) Theory of modeling and Simulation, Academic Press.
Additional information on implementation and assessment exercises within lectures and seminar 40%
seminar work 60%
