Complex mathematical model for soft processes in information fusion

Data has become increasingly comprehensive and now include descriptions of the sources from which the data originates. This allows for the Information Fusion (IF) process to bring together data from different sources to develop numerous pieces of information and attach an uncertainty value to the information. The IF process is important for a Decision Maker (DM) because it reduces the amount of data a DM would have to look at by condensing it into pieces of information that can then be processed by the DM to determine a Course-of-Action (COA). In many instances the DM is still overwhelmed with information after the IF process and the inclusion of uncertainty values allows for a further reduction by removing unreliable information. In particular, the proposed methodology introduces a new mathematical model that works in conjunction with a human fuser (soft process) to modify uncertainty values. Our math model focuses on combining uncertainty values using Shannon entropy to determine the data points that the human fuser should update. The integration of the math model with soft processes can potentially increase the reliable knowledge available to a DM, thus allowing DM's to determine COAs that are more accurate.