Requirements for writing a Master's thesis at our Chair
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Interest in our research topics (everything related to decision science, cognitive science, cognitive psychology, or behavioral research methods)
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Successful completion of the modules "Empirical research methods in management and economics" or "Consumer Behavior Research Methods" (grade of 2.3 or better)
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Successful attendance of at least one further module offered by the Chair (for a list of our teaching portfolio see here)
First steps to writing a thesis with us
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Coordinate the topic of your thesis with your potential supervisor. Reach out via email (attaching your CV and most recent transcripts) and give us a short outline of your proposed thesis topic.
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You can either reach out with your own novel and innovative research ideas, or choose from our currently open thesis topics listed below.
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In general, we are also open to supervising Master's thesis that are written in cooperation with a company, providing they are related to our research topics or require the application of thorough research methods.
Traditional research on risky choice has often applied description-based paradigms, where participants are given the option to choose between to lotteries with known outcomes and corresponding probabilities. However, in everyday life, we do not always have summary statistics about the choice options available, but often have to rely on our previous experience instead. This is reflected for example in the experience-based feedback paradigm (Hertwig & Erev, 2009), where individuals initially have no information about the choice options but have to learn about their payoff structures through repeated choices. Whereas cumulative prospect theory (CPT; Tversky & Kahneman, 1992) represents one of the most established models of risky choice, we have recently found initial evidence that estimated CPT parameters may systematically change over the time course of repeated choices in the feedback paradigm. The focus of this thesis project is a) to search the literature and gather available datasets based on the feedback paradigm, to then b) model participant's choice behavior with CPT in every choice round over the time course across the collected datasets.
Requirements: Experience in data analysis using R. Of advantage: Previous experience with computational modeling
Supervisor: Nuno Busch (nuno.busch(at)tum.de)
Recommended Literature:
- Hertwig, R., & Erev, I. (2009). The description–experience gap in risky choice. Trends in Cognitive Sciences, 13(12), 517–523. https://doi.org/10.1016/j.tics.2009.09.004
- Barron, G., & Erev, I. (2003). Small feedback‐based decisions and their limited correspondence to description‐based decisions. Journal of Behavioral Decision Making, 16(3), 215–233. https://doi.org/10.1002/bdm.443
- Hof, L. & Busch, N. (2025). Bayesian Cognitive Modeling Tutorial. Github. https://github.com/linushof/TutorialsForTeaching/blob/main/BayesianCognitiveModeling/bayesian_cognitive_modeling.md
Loss aversion is one of the most prominent concepts in the study of risky decision making, describing the widely observed phenomenon that people tend to assign more subjective value to losses than to equally sized gains. Recent research has demonstrated that loss aversion may however have boundary conditons. For example, the absolute magnitude of outcome values seems to affect the degree of loss aversion, leading to debates if established formalizations of prospect theory models should be adapted. The focus of this thesis project is to implement a series of functional forms of loss aversion's magnitude dependence to systematically compare their model fit in a Bayesian hierarchical modeling approach.
Requirements: Experience in data analysis using R. Of advantage: Previous experience with computational modeling
Supervisor: Nuno Busch (nuno.busch(at)tum.de)
Recommended Literature:
- Mukherjee, S., Khan, O., & Srinivasan, N. (2025). The role of magnitude in loss aversion. Decision. https://doi.org/10.1037/dec0000256
- Harinck, F., Van Dijk, E., Van Beest, I., & Mersmann, P. (2007). When Gains Loom Larger Than Losses: Reversed Loss Aversion for Small Amounts of Money. Psychological Science, 18(12), 1099–1105. https://doi.org/10.1111/j.1467-9280.2007.02031.x
- Ariely, D., Huber, J., & Wertenbroch, K. (2005). When Do Losses Loom Larger than Gains? Journal of Marketing Research, 42(2), 134–138. https://doi.org/10.1509/jmkr.42.2.134.62283
- Mukherjee, S., Sahay, A., Pammi, V. S. C., & Srinivasan, N. (2017). Is loss-aversion magnitude-dependent? Measuring prospective affective judgments regarding gains and losses. Judgment and Decision Making, 12(1), 81–89. https://doi.org/10.1017/S1930297500005258
- Novemsky, N., & Kahneman, D. (2005). The Boundaries of Loss Aversion. Journal of Marketing Research, 42(2), 119–128. https://doi.org/10.1509/jmkr.42.2.119.62292
- Zeif, D., & Yechiam, E. (2022). Loss aversion (simply) does not materialize for smaller losses. Judgment and Decision Making, 17(5), 1015–1042. https://doi.org/10.1017/S193029750000930X
- Hof, L. & Busch, N. (2025). Bayesian Cognitive Modeling Tutorial. Github. https://github.com/linushof/TutorialsForTeaching/blob/main/BayesianCognitiveModeling/bayesian_cognitive_modeling.md
| Complete | Own Proposal | Supervisor | Title | Student | Study Program |
|---|---|---|---|---|---|
| Nuno | Validating webcam-based eyetracking in online studies of risky choice | A. B. Azimi | MMT | ||
| Nuno | The Role of Outcome Magnitudes for Inducing Loss Aversion | S. Yıldız | MMT | ||
| x | Nuno | Loss Aversion in Cumulative Prospect Theory: A Bayesian Comparison of Description, Sampling, and Feedback Conditions | R. Eberle | MCS | |
| Nuno | Feedback-Driven Dynamics of Loss Aversion: A Comparison of Described and Experienced Outcomes | E. Schauer | MMT | ||
| Sebastian | Effects of self-confidence on choice confidence in risky choice | Merve Önal | MMT | ||
| Sebastian | Confidence and Risk: A literature review on gambling on performance in perceptual and knowledge taks | Mikhail Shchelkunov | MMT | ||
| X | X | Sebastian | Einfluss auf Kaufentscheidungen: menschliche versus virtuelle Influencer (Bachelor Thesis) | Stefanie Winkelmeier | BBWL |
| Sebastian | Modeling Changes of Mind in Perceptual Decision-Making: An Extension of the dynWEV Framework with Empirical Validation | Aylin Bayram | MMT | ||
| Sebastian | Investigating Choice History Effects in Perceptual Confidence Judgments | Şeyma Çakır | MMT | ||
| X | Sebastian | Using neural networks for Bayesian inference in the cognitive modelling of confidence judgments | Ardelan Ciplak | MMT | |
| Sebastian | Modelling the effect of independent discriminability evidence on confidence judgments (Bachelor Thesis) | Lisa Dieneiger | BMT | ||
| X | Sebastian | Computational modelling of the magnitude sensitivity effect on confidence in food choices | Lea Hohenstein | MCS | |
| X | Thorsten | Media literacy by design: How visual prompts increase the ability to detect distorted news coverage | Ann-Christin Gah | ||
| X | Thorsten | Unveiling the curtain of dynamic decision making: A mouse cursor analysis of the Balloon Analogue Risk Task | Christoph Pirker | ||
| X | Thorsten | Loss aversion in risky choice for younger and older adults | Nur Melis Ballıkaya | ||
| X | Thorsten | Metric knowledge in real life: Is it one-dimensional? | Cansu Ünlü | ||
| X | Thorsten | Era-based fine-tuning in AI: Assessing gender bias shifts across time | Bilal Imamoglu | ||
| X | Thorsten | Risk in bits: Understanding cryptocurrency investors’ risk behavior through a prospect theory lens | Lucas Pirker | ||
| X | Thorsten | Understanding user responses to XAI: A comparative analysis of explanation methods for spurious model detection | Ezgi Beceren | ||
| X | Thorsten | Improving intuitive estimates of carbon emissions with a seeding procedure | Anna Dreier |
Study Programs:
MMT: Master in Management and Technology
MCS: Master in Consumer Science
BMT: Bachelor in Management and Technology
BBWL: Bachelor in Technologie- und Managementorientierte Betriebswirtschaftslehre