Modelling Stochastic Decision-Making Processes

  • Credits: 10
  • Ending: Examination
  • Range: 16sP
  • Semester: summer
  • Faculty of Economic Informatics

Teachers

Included in study programs

Data Science in Economics

Data Science in Economics

Teaching results

The course focuses on stochastic modeling and optimization methods for decision support and covers recent research contributions in several fields of operations management and economics.
The topics of the course will be introduced using state-of-the-art overview articles and then be highlighted by the study of recent research papers in the respective field. The objective is to give both, an overview of research fields, typical research methodology, and to inspire own work in the field.

Indicative content

• Uncertainty Modeling: Probability Theory, Stochastic Processes, Fuzzy Set Theory, Bayes Updating.
• Stochastic Dynamic Programming and Approximate Dynamic Programming.
• Markov Chains and Markov Decision Processes.
The course is focused on stochastic modeling and optimization methods to support decision-making. The aim is to provide a brief overview of scientific knowledge in several areas of stochastic modeling processes and based on the acquired knowledge, to construct a mathematical model applicable in economic practice. Software tools such as R, Python, GAMS, Simul8, and Eviews are used to solve problems. The aim is also to provide an overview of research areas and typical research methodology and to inspire Ph.D. students for their own work in their field.
• Stochastic Programming: Chance Constrained Programming, Two-Stage Models with Recourse, Sample Average Approximation, Sampling Strategies, Data-Driven Optimization-Machine Learning Interface.
• Fuzzy Optimization and Decision Making
• Simulation modelling.
• Applications: Queuing Theory, Queuing Networks, Inventory Theory, Operations Management

Support literature

1. Stewart, W. J. (2009). Probability, Markov Chains, Queues, and Simulation. Princeton university press.
2. Tijms, H.C. (2003). A First Course in Stochastic Models. Wiley.
3. King, A.J., Wallace, S.W. (2012). Modeling with Stochastic Programming. Springer.
4. Powell, W. (2011). Approximate Dynamic Programming. Wiley.
5. Privault, N. (2013). Understanding Markov Chains. Examples and Applications, Springer
6. Kleijnen, J.P.C. (2008). Design and Analysis of Simulation Experiments. Springer.

Requirements to complete the course

40 % assignments; 60 % final paper

Student workload

Total study load (in hours): 10 credits x 26 hours = 260 hours
Distribution of study load
260 hours
16 hours participation in consultations
44 hours preparation for consultations
100 hours of project processing
100 hours exam preparation

Language whose command is required to complete the course

Slovak, English

Date of approval: 10.02.2023

Date of the latest change: 16.05.2022