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.

我们调查基于代理人的模型的意见动态，并有兴趣预测共享意见的整个代理人所占百分比的演变。由于这些意见百分比可以看作是对整个系统状态，每个代理的个人意见的汇总观察，因此我们在Mori-Zwanzig投影形式主义的框架内对此进行观察。更具体地说，我们展示了如何根据意见百分率的时间序列给出的数据来估计具有记忆的非线性自回归模型（NAR），并讨论了其对代理交互网络的各种特定拓扑的预测能力。我们证明，包含内存项可以显着提高具有不同网络拓扑的示例的预测质量。