Decisions under risk : an economic analysis

This dissertation explores decision-making under risk through a comprehensive economic analysis. It aims to deepen our understanding of how individuals make choices when faced with uncertainty and risk, integrating various theoretical models and empirical findings. The thesis begins by surveying established models in the literature from economics, psychology and biology, highlighting their strengths and limitations. Subsequently, possible challenges and avenues forward are presented. To highlight the limitations of a constant risk preference assumption, an experiment was carefully designed for the second paper. This study compares these models in an online experiment, emulating real-world investment decisions. The findings reveal significant deviations from traditional theories, suggesting the need for more flexible and encompassing frameworks. A change in individual decision-making behavior is observed between different rounds of the experiment, not only indicating a variable risk preference, but an adaptive decision-making process altogether. To explore this on a theoretical level, this dissertation subsequently delves into the evolutionary dynamics of risk preferences, examining how different behavioral models survive and thrive in competitive environments. The results indicate that models which allow for adaptive risk preferences have an evolutionary advantage over those that do not. This questions the current empiric approach in search for a single risk preference parameter to explain and predict individual behavior. An observed change in the decision making process could be modelled with the assumption of bounded rationality, offering a more accurate depiction of human behavior. Overall, this dissertation contributes to the field of behavioral economics by providing a nuanced understanding of decision-making under risk, surveying a wide array of models, criteria to evaluate them, and offering practical implications for policy-making and risk management. The work underscores the importance of integrating empirical evidence with theoretical advancements to better comprehend and predict behavior in uncertain environments.