Directly to content
  1. Publishing |
  2. Search |
  3. Browse |
  4. Recent items rss |
  5. Open Access |
  6. Jur. Issues |
  7. DeutschClear Cookie - decide language by browser settings

Understanding and Modeling Economic Behavior: Experimental Insights and Computational Perspectives

Ferraz, Vinicius

[thumbnail of phd_dissertation_print_FINAL_Ferraz.pdf]
Preview
PDF, English
Download (21MB) | Terms of use

Citation of documents: Please do not cite the URL that is displayed in your browser location input, instead use the DOI, URN or the persistent URL below, as we can guarantee their long-time accessibility.

Abstract

In the multidisciplinary field of economic behavior, traditional theories often struggle to capture the inherently complex nature of human decision-making processes. Building upon well-established theoretical foundations, this research proposes a comprehensive exploration of human economic behavior. It leverages the strengths of behavioral economics, experimental methodologies, and advanced computational techniques, integrating these into comprehensive analytical models. Through five interconnected papers, the core objective is to investigate the psychological complexities of individual choices. Rigorous experimental designs and robust methodologies reveal detailed insights into actions and decisions. The introduced studies cover adaptive and evolutionary learning, equilibria in asymmetric games, as well as fairness and loss aversion in strategic interactions. They also investigate the correlation between dark personality traits and dishonesty, explore the phenomenon of algorithm aversion, and examine the dynamics of motivated sampling of information. These topics collectively provide a broad perspective on human decision-making in economic contexts, with the findings offering deep insights into diverse real-world inspired scenarios. To achieve this, the research utilizes advanced computational techniques such as Genetic Algorithms, Agent-Based Models, Reinforcement Learning, Machine Learning, and Causal Inference. From understanding the psychological mechanisms underlying decision-making to examining well-established behavioral traits like loss aversion and dark personality traits, this dissertation paints a comprehensive picture. It adeptly bridges the gap between theoretical constructs and real-world implications, presenting a fresh perspective on the dynamic nature of economic behavior in contemporary society.

Document type: Dissertation
Supervisor: Schwieren, Prof. Dr. Christiane
Place of Publication: Heidelberg
Date of thesis defense: 19 March 2024
Date Deposited: 26 Apr 2024 09:49
Date: 2024
Faculties / Institutes: The Faculty of Behavioural and Cultural Studies > Institute of Psychology
The Faculty of Behavioural and Cultural Studies > Institut für Bildungswissenschaft
The Faculty of Behavioural and Cultural Studies > Institut für Sport und Sportwissenschaft
The Faculty of Behavioural and Cultural Studies > Institut für Gerontologie
The Faculty of Behavioural and Cultural Studies > Institute of Ethnology
The Faculty of Mathematics and Computer Science > Department of Computer Science
The Faculty of Mathematics and Computer Science > Institut für Mathematik
The Faculty of Economics and Social Studies > Institute of Political Science
The Faculty of Economics and Social Studies > Institute of Sociology
The Faculty of Economics and Social Studies > Alfred-Weber-Institut for Economics
DDC-classification: 004 Data processing Computer science
330 Economics
600 Technology (Applied sciences)
Controlled Keywords: Wirtschaft, Wirtschaftstheorie
Uncontrolled Keywords: Behavioral Economics, Computational Economics, Experimental Economics, Machine Learning
About | FAQ | Contact | Imprint |
OA-LogoDINI certificate 2013Logo der Open-Archives-Initiative