Multi-agent diffusion of decision experiences

Diffusion geometry offers a fresh perspective on multi-scale information analysis, which is critical to multiagent systems that need to process massive data sets. A recent study has shown that when the «diffusion distance» concept is applied to human decision experiences, its performance on solution synthesis can be significantly better than using Euclidean distance. However, as a data set expands over time, it can quickly exceed the processing capacity of a single agent. In this paper, we proposed a multi-agent diffusion approach where a massive data set is split into several subsets and each diffusion agent only needs to work with one subset in diffusion computation. We conducted experiments with different splitting strategies applied to a set of decision experiences. The result indicates that the multi-agent diffusion approach is beneficial, and it is even possible to benefit from using a larger group of diffusion agents if their subsets have common experiences and pairly-shared experiences. Our study also shows that system performance could be affected significantly by the splitting granularity (size of each splitting unit). This study paves the road for applying the multi-agent diffusion approach to massive data analysis.