Hausladen, Carina Ines ORCID: 0000-0003-4397-2339 (2020). Behavioral Economics - Enhanced: Machine Learning and Decision Making. PhD thesis, Universität zu Köln.

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In this thesis, I investigate decision-making in the fields of behavioral economics, experimental economics, and law and economics. The research questions I ask are: Can we nudge people towards being more honest? Can we use language to find out who lies? Which factors influence a judge’s decision, and how do people cooperate? Specifically, I investigate contributions in a public goods game, (dis-)honest decision-making in a die-in-the-cup and tax compliance game. Furthermore, I investigate the bounds of rational decision-making in the context of the law. To answer the posed questions, I apply – alongside traditional econometrics – machine learning methods: I use natural language classification to predict decisions based on text data. Furthermore, I use time-series clustering to reduce complexity and thereby enable theory building and interpretation.

Item Type: Thesis (PhD thesis)
CreatorsEmailORCIDORCID Put Code
Hausladen, Carina
AuthorSchubert, MarcelUNSPECIFIED
AuthorEngel, ChristophUNSPECIFIED
AuthorFochmann, MartinUNSPECIFIED
AuthorAsh, ElliottUNSPECIFIED
AuthorMohr, PeterUNSPECIFIED
AuthorNikolaychuk, OlexandrUNSPECIFIED
URN: urn:nbn:de:hbz:38-122314
Date: 5 October 2020
Language: English
Faculty: Faculty of Management, Economy and Social Sciences
Divisions: Ehemalige Fakultäten, Institute, Seminare > Faculty of Management, Economy and Social Sciences
Subjects: Data processing Computer science
Social sciences
Uncontrolled Keywords:
Behavioral EconomicsEnglish
Experimental EconomicsEnglish
Machine LearningEnglish
Natural Language ProcessingEnglish
Empirical Legal StudiesEnglish
Date of oral exam: 5 October 2020
NameAcademic Title
Fochmann, MartinProf. Dr.
Engel, ChristophProf. Dr.
Proksch, Sven-OliverProf. Dr.
Funders: Center for Social and Economic Behavior (C-SEB), University of Cologne, Max-Planck Institute for Research on Collective Goods, Bonn
Projects: Color Me Honest! Time Pressure and (Dis-)Honest Behaviour, Predicting (Dis-)Honesty: Leveraging Text Classification for Behavioral Experimental Research, Charting the Type Space: The Case of Linear Public Good Games, Text Classification of Ideological Direction in Judicial Opinions
Refereed: Yes


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