The theoretical alignment of Bayesian statistics and cumulative prospect theory

The focus of this research is combining Bayesian statistics, in particular Bayesian data analysis, with utility theory. From utility theory we select the work from Kahneman and Tversky, i.e., Cumulative Prospect Theory. Computer science and software engineering research today focuses on traditional frequentist approaches from statistics, to point at improvements a certain technique, approach, method, has compared to a baseline. We believe that this approach is used too often to represent a dualism, i.e, ending up in a binary decision: yes/no, 0/1, or pass/fail. We argue that reality is rarely this simple. Instead, by combining Bayesian data analysis with cumulative prospect theory, we believe we can oer realistic scenarios to decision makers, which allows them to receive a better understanding of where the borders between yes/no are and how they affect their processes, organizations, etc. To this end, many times they might not select a `0' or `1', but rather pick `0.5'.