Evidence Propagation and Consensus Formation in Noisy Environments

We study the effectiveness of consensus formation in multi-agent systems where belief updating is an iterative two-part process, consisting of both belief updating based on direct evidence and also belief combination between agents, within the context of a best-ofn problem. Agents' beliefs are represented within Dempster-Shafer theory by mass functions and we investigate the macro-level properties of four well-known belief combination operators: Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging operator. Simulation experiments are conducted for different evidence rates and noise levels. Broadly, Dubois & Prade's operator results in better convergence to the best state, and is more robust to noisy evidence.