  We investigate opinion dynamics based on an agent-based model, and are
interested in predicting the evolution of the percentages of the entire agent
population that share an opinion. Since these opinion percentages can be seen
as an aggregated observation of the full system state, the individual opinions
of each agent, we view this in the framework of the Mori-Zwanzig projection
formalism. More specifically, we show how to estimate a nonlinear
autoregressive model (NAR) with memory from data given by a time series of
opinion percentages, and discuss its prediction capacities for various specific
topologies of the agent interaction network. We demonstrate that the inclusion
of memory terms significantly improves the prediction quality on examples with
different network topologies.
