Simulating human decision making for testing soft and hard/soft fusion algorithms

Current methods for evaluating the effects of human opinions in data fusion systems are often dependent on human testing (which is logistically hard and difficult to arrange for repeated tests of the same population). The alternative is to use hypothetical examples, which tend to be simplistic. To facilitate studies of data fusion architectures which integrate ¿soft¿ human-generated decisions, we have used a simulator of subjective beliefs. The simulator is based on the two-stage dynamic signal detection model of Pleskac and Busemeyer (2010). We use this scheme to simulate human opinions and combine them using belief fusion methods, including Bayes' Rule; Dempster's Rule of Combination (DRC); Yager's rule; the Proportional Conflict Redistribution Rule #5 (PCR5) from Dezert-Smarandache theory; and the consensus operator from subjective logic. In our simulations, the DRC and Bayes rule exhibited performance that was on par with, and in some cases better than PCR5 and the consensus operator (when used in conjunction with a measure of source reliability). In all simulated cases, Yager's rule exhibited inferior performance.