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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.