NA_DeMa Decision Making

University of Finance and Administration
Summer 2026
Extent and Intensity
2/0/0. 3 credit(s). Recommended Type of Completion: z (credit). Other types of completion: zk (examination).
Teacher(s)
Mirela Moldoveanu, M.Sc., Ph.D. (seminar tutor)
Guaranteed by
Mirela Moldoveanu, M.Sc., Ph.D.
Department of Social Sciences – Departments – University of Finance and Administration
Contact Person: Mgr. Petra Dovhunová
Supplier department: Department of Social Sciences – Departments – University of Finance and Administration
Timetable of Seminar Groups
NA_DeMa/pECNCNPH: each odd Thursday 15:45–16:29 E404, each odd Thursday 16:30–17:15 E404, each odd Thursday 17:30–18:14 E404, each odd Thursday 18:15–19:00 E404, M. Moldoveanu
Prerequisites
There are no pre-requisites for this course.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
Decision-making is a fundamental cognitive and strategic process that influences success across various domains, including business, politics, personal life, and policy-making. This course explores the psychology, neuroscience, economics, and strategic frameworks behind decision-making. Students will learn how to recognise biases, analyse risks, apply rational and behavioural approaches, and leverage decision-making models to improve outcomes in professional and personal contexts.
Learning outcomes
By the end of this course, students will be able to: 1. Understand the psychological and economic foundations of decision-making. 2. Identify cognitive biases and heuristics that influence choices. 3. Evaluate decision-making frameworks and tools used in business, policy, and personal contexts. 4. Apply decision analysis techniques to real-world problems. 5. Develop critical thinking skills for ethical and strategic decision-making. 6. Utilise data-driven and intuitive approaches for making complex decisions.
Syllabus
Week Lecture Title 1 Introduction to Decision-Making: Theories and Approaches 2 Rational vs. Behavioural Decision-Making 3 Cognitive Biases and Heuristics 4 Decision-Making Under Uncertainty and Risk 5 The Role of Emotions in Decision-Making 6 Group Decision-Making and Social Influences 7 Data-Driven Decision-Making and Predictive Analytics 8 Ethical and Moral Decision-Making 9 Decision-Making in Business and Management 10 Decision-Making in Crisis Situations 11 AI, Automation, and Decision-Making in the Digital Age 12 Integrative Approaches: Improving Personal and Professional Decision-Making Recommended literature: Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124–1131. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press. Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99–118. Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2004). Risk as Analysis and Risk as Feelings: Some Thoughts About Affect, Reason, Risk, and Rationality. Risk Analysis, 24(2), 311–322. Ariely, D. (2008). Predictably Irrational: The Hidden Forces That Shape Our Decisions. HarperCollins.
Teaching methods
Each lecture will be structured with an interactive and applied learning approach, combining theoretical foundations with real-world applications. The structure of the lectures will typically follow this format: Concept Introduction – A short lecture segment explaining key theories and concepts. Case Study Analysis – Examination of real-world decision-making scenarios (business, personal, political). Interactive Discussion – Group discussions, debates, and simulations. Hands-on Exercises – Application of decision models and problem-solving exercises. Reflection and Integration – Students will engage in self-assessments and strategy planning for improving decision-making. Pedagogical methods will include problem-based learning (PBL), case study discussions, simulations, and experiential learning exercises. Group work and collaborative decision tasks will allow students to experience real-world complexities in decision-making. Digital tools and AI-based decision analysis platforms will be introduced in later lectures to explore technology’s role in decision processes.
Assessment methods
The students will earn the course credit based on their active performance in class and the quality of their seminar assignments. Additional assignments will be required from ISP students.
Language of instruction
English
Further Comments
The course can also be completed outside the examination period.
The course is also listed under the following terms Summer 2027.
  • Enrolment Statistics (Summer 2026, recent)
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