Risk, Entropy, and Decision-Making Under Uncertainty

Standard models for analyzing programmatic risk do not align with real-life behavior of humans when choosing between alternatives, resulting in inconsistencies between mental models formed by these decision-makers and the application of formal risk analyses. This misalignment devalues risk assessment when making discrete choices, a problem also observed in the financial risk community. Contributing to this problem is the lack of a global fungible unit of programmatic risk which would allow for modeling and pattern analysis across different systems. To understand the present body of knowledge, this literature review examines three distinct fields of study, programmatic risk management, entropy (information theory), and decision-making under uncertainty (DMUU), to ascertain if there might be a superior method of managing risk consistent with human decision-making processes, similar to that shown in Prospect Theory. While many of the works reviewed pertain to one or two of these three fields, merely a small number of primary references elucidate the connections between risk, entropy, and DMUU. The conclusion of this literature review is that basic relationships connecting these fields have been introduced, but no theory has yet been fully developed, providing opportunity for more rigorous, in-depth research.