Objectives and competences
A student at this subject
1. develops the capacity to participate in the decision-making process
2. gains in-depth knowledge of decision-making systems and decision-support models
3. gains the ability to independently organize the decision-making process, with particular emphasis on group decision-making
Content (Syllabus outline)
1. Decision making process as a socio-technical process
2. Decision support systems and decision models
3. Multi-attribute decision making
4. Qualitative and quantitative modelling
5. Decision support methods and techniques
6. Using AI methods in the decision making
7. Data analytics
8. Deciding under risk / unknown conditions
9. Human in the decision-making process
10. Group decision making
11. Evaluating decisions
Learning and teaching methods
• Lectures
• Case studies
• Exercises
• Developing a decision support model for organizing a chosen decision support process
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
• to rationally link the elements of the decision process
• to argue the role of modern tools to support decision-making processes with an emphasis on group decision-making
• to develop a decision-making model to support a decision process
• to analyse the results of decision models
• to apply a decision model in a given process
• to choose suitable contemporary methods and techniques
Intended learning outcomes - transferable/key skills and other attributes
- ability to organize a decision making process as a cybernetic process
- ability to develop models for decision support by using modern methods and techniques
Readings
1. Bohanec M (2012). Odločanje in modeli. 1. Ponatis. Ljubljana: DMFA.
2. Bavec C, Kovačič A, Krisper M, Rajkovič V, Vintar M (2018). Slovenija na poti digitalne preobrazbe. Ljubljana: Založba UL FRI.
3. Howard RA, Abbas AE (2016). Foundations of decision analysis. Boston, MA: Pearson Education Limited.
4. Ragsdale C (2018). Spreadsheet modeling and decision analysis: a practical introduction to business analytics (8. izd.). Boston, MA: Cengage Learning.
Prerequisits
Prerequisites for enrolling:
knowledge of statistics and probability
basic knowledge of mathematics
basic knowledge of ICT
Exam admission requirement:
positive evaluation of a seminar work and its defence
Additional information on implementation and assessment Reflexive diary or written exam 100 %
