eprintid: 34685
rev_number: 34
eprint_status: archive
userid: 8069
dir: disk0/00/03/46/85
datestamp: 2024-04-26 09:49:18
lastmod: 2024-04-26 11:11:03
status_changed: 2024-04-26 09:49:18
type: doctoralThesis
metadata_visibility: show
creators_name: Ferraz, Vinicius
title: Understanding and Modeling Economic Behavior: Experimental Insights and Computational Perspectives
subjects: ddc-004
subjects: ddc-330
subjects: ddc-600
divisions: i-100200
divisions: i-100400
divisions: i-100500
divisions: i-100600
divisions: i-100700
divisions: i-110300
divisions: i-110400
divisions: i-180500
divisions: i-180600
divisions: i-181000
adv_faculty: af-18
keywords: Behavioral Economics, Computational Economics, Experimental Economics, Machine Learning
cterms_swd: Wirtschaft
cterms_swd: Wirtschaftstheorie
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.
date: 2024
id_scheme: DOI
id_number: 10.11588/heidok.00034685
ppn_swb: 1887173560
own_urn: urn:nbn:de:bsz:16-heidok-346854
date_accepted: 2024-03-19
advisor: HASH(0x558eaa73a768)
language: eng
bibsort: FERRAZVINIUNDERSTAND
full_text_status: public
place_of_pub: Heidelberg
citation:   Ferraz, Vinicius  (2024) Understanding and Modeling Economic Behavior: Experimental Insights and Computational Perspectives.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/34685/7/phd_dissertation_print_FINAL_Ferraz.pdf