Advanced Behavioral Economics

  • Ending: Examination
  • Range: 32sP
  • Semester: winter
  • Year: 2
  • Faculty of Economics and Finance

Teachers

Included in study programs

Faculty of Economics and Finance

Economics

Teaching results

Teaching results:
Knowledge
The course will provide students with an advanced insights on selected topics and research methods
of behavioral economics. Students will acquire understanding and analytical insights in the
deviations of individuals from rationality and cognitive distortions.
Abilities and skills
Students will be able applying knowledge gained for addressing economic problems both in the
public and private sectors. After having completed the course, students will be able to identify and
evaluate systematic deviations in behavior of various economic agents and propose alternative
solutions to reduce them and assess the impact of these interventions.
Graduates from the course will also be able to analyze collected data and based on this analysis
address questions that are relevant for behavioral sciences.
Students will also be able to synthesize various approaches, theories and conclusions from empirical
studies in behavioral economics.
In addition to building up economic knowledge, the course also develops analytical skills and
academic presentation skills of students (the later in the field of written and verbal academic
communication).

Indicative content

Indicative content:
1. Behavioral economics – scope and methodology
(experiments, causal inference, cognitive limits)
2. Prospect theory and decision–making under risk
(loss aversion, weighting, reference points)
3. Social preferences
(fairness, reciprocity, altruism)
4. Intertemporal choice, time preference and self-control
(present bias, commitment, procrastination)
5. Heuristics and behavioral biases
(anchoring, availability, overconfidence, belief errors)
6. Behavioral nudges
(defaults, framing, salience, simplification)
7. Models of limited rationality
(limited attention, rational inattention models)
8. Behavioral game theory
(level-k, reciprocity, bounded reasoning)
9. Behavioral Public Policy & RCTs at scale
(large-scale trials, evidence-based interventions)
10. Behavioral Industrial Organization
(salience, shrouding, price complexity)
11. AI & Behavioral Economics
(algorithmic nudging, personalization, digital behavior patterns)

Support literature

Camerer, C. F., Loewenstein, G., & Rabin, M. (Eds.). (2003). Advances in Behavioral Economics. Princeton, NJ: Princeton University Press.
ISBN: 9780691090399.
Dhami, S. (2016). The Foundations of Behavioral Economic Analysis. Oxford: Oxford University Press.
ISBN: 9780198715528.
Kahneman, D., & Tversky, A. (1979). Teória vyhliadok: analýza rozhodovania v podmienkach rizika. Econometrica, 47(2), 263–291.
https://doi.org/10.2307/1914185
Fehr, E., & Schmidt, K. M. (1999). Teória spravodlivosti, konkurencie a spolupráce. The Quarterly Journal of Economics, 114(3), 817–868.
https://doi.org/10.1162/003355399556151
Laibson, D. (1997). Zlaté vajcia a hyperbolické diskontovanie. The Quarterly Journal of Economics, 112(2), 443–478.
https://doi.org/10.1162/003355397555253
Tversky, A., & Kahneman, D. (1974). Rozsudok v podmienkach neistoty: Heuristika a predsudky. Science, 185(4157), 1124–1131.
https://doi.org/10.1126/science.185.4157.1124
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Zlepšovanie rozhodnutí o zdraví, bohatstve a šťastí. New Haven, CT: Yale University Press.
ISBN: 9780300122237.
Gabaix, X. (2014). Model obmedzenej racionality založený na riedkosti. The Quarterly Journal of Economics, 129(4), 1661–1710.
https://doi.org/10.1093/qje/qju023
Camerer, C. F. (2003). Teória behaviorálnych hier: Experimenty v strategickej interakcii. Princeton, NJ: Princeton University Press.
ISBN: 9780691090399.
DellaVigna, S., & Linos, E. (2022). RCTs at Scale: Evidence from Two Million Voters. The Quarterly Journal of Economics, 137(4), 2439–2503.
https://doi.org/10.1093/qje/qjac034

Requirements to complete the course

Requirements to complete the course:
40% - preparation of research paper (literature review and original research)
10% - active contribution to discussions in class
50% - written exam

Student workload

Time allocation: 130 hours
32 hours– contact hours
36 hours – preparation of the research paper
20 hours – preparation for class
42 hours – preparation for the final exam

Language whose command is required to complete the course

english

Date of approval: 28.10.2025

Date of the latest change: 03.12.2025