Behavioral Economics and Decision-Making

Teachers

Included in study programs

Teaching results

After studying this module, students should be able to:
Knowledge:
- understand the basic principles of behavioral economics and is able to identify the basic behavioral factors that influence the economic decisions of individuals
- understand the processes of economic decision-making ant the effect of behavioral factors on this process
Competences:
- develop the ability to influence decisions and manage (as a future manager) other entities (co-workers, subordinates, clients, etc.) in terms of identifying ways to encourage better decisions and better results
- apply tools to improve their managerial decision-making in the context of the heuristics and biases
Skills:
- analyze, critically interpret and compare the results of prior research in order to apply them to managerial decisions
- express their opinions based on empirical data and theoretical models

Indicative content

The course focuses on the issue of behavioral economics and the process of decision-making. The aim of including individual issues within the course is to approach the psychological factors that influence economic decisions together with the definition of the extent of their influence. The course has two aspects: first, it provides students with an insight into the results of previous research and, second, it provides students with practical advice on applying these findings to topics in management.

Support literature

RAYNARD, R. Economic Psychology. Wiley, 2017.
CARTWRIGHT, E. Behavioral economics. Routledge, 2018.
ARIELY, D., & WERTENBROCH, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13(3), 219-224.
FEHR, Ernst; GÄCHTER, Simon. Fairness and retaliation: The economics of reciprocity. Journal of economic perspectives, 2000, 14.3: 159-181.
KAHNEMAN, Daniel; TVERSKY, Amos. Prospect theory: An analysis of decision under risk. In: Handbook of the fundamentals of financial decision making: Part I. 2013. p. 99-127.
KŐSZEGI, Botond; RABIN, Matthew. A model of reference-dependent preferences. The Quarterly Journal of Economics, 2006, 121.4: 1133-1165.
LAIBSON, David. Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics, 1997, 112.2: 443-478.
STROTZ, Robert Henry. Myopia and inconsistency in dynamic utility maximization. The Review of Economic Studies, 1955, 23.3: 165-180.
SZYSZKA, A. 2013. Behavioral finance and capital markets: How psychology influences investors and corporations. Springer.
THALER, Richard H.; BENARTZI, Shlomo. Save more tomorrow™: Using behavioral economics to increase employee saving. Journal of political Economy, 2004, 112.S1: S164-S187.
BROKEŠOVÁ, Z. - DECK, C. - PÉLIOVÁ, J.. Comparing a risky choice in the field and across lab procedures. Journal of Economic Psychology, August 2017, vol. 61, pp. 203-212.
VUČETIĆ, M.- BROKEŠOVÁ, Z. - HUDEC, M. - PASTORÁKOVÁ, E. Financial Literacy and Psychological Disaster Preparedness: Applicability of Approach Based on Fuzzy Functional Dependencies. Information Processing & Management, vol. 59, no. 2 (2022), pp. 1-12 online.
BROKEŠOVÁ, Zuzana - CUPAK, Andrej - LEPINTEUR, Anthony - RIZOV, Marian. Real Assets and Subjective Well-Being: Using a Novel Measure for Relative Effects. In: Social Indicators Research. Amsterdam : Springer Gabler. ISSN 1573-0921. 180, 1567–1591.

Syllabus

1. Introduction to Behavioral Economics and Decision-Making 2. Theory of Economic Decision Making, 3. Decision-Making under Risk and Uncertainty, Prospect theory 4. Research Methods in Behavioral Economics, Experimental Economics 5. Choices over Time and Procrastination 6. Social Motivation: Image, Reciprocity and Inequity 7. Information Processing and Mistakes in Cognition 8. Naivite and Heuristics in Strategic Thinking 9. Social Norms and Culture 10. Market Design 11. Behavioral Finance, Investment Decisions, Psychology of the Money 12. Nudges and Policy, Behavioral Marketing and Advertising 13. Happiness and Welfare

Requirements to complete the course

20 % presentation and discussion of a research study, 20 % semester team project, 60 % written exam
To pass the exam, student must score at least 51% of the total points available for the exam.

Date of approval: 27.02.2025

Date of the latest change: 16.01.2026