论文标题
基于内存的减少建模和基于数据的意见扩展估计
Memory-based reduced modelling and data-based estimation of opinion spreading
论文作者
论文摘要
我们根据基于代理的模型调查了意见动态，并有兴趣预测共享意见的整个代理商的百分比的演变。由于这些意见百分比可以看作是对完整系统状态的汇总观察，因此每个代理人的个人意见，我们在莫里西格投射形式主义的框架中对此进行了看法。更具体地说，我们展示了如何估算一个非线性自回归模型（NAR），并使用来自时间序列百分比给出的数据的内存，并讨论其针对代理交互网络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不同网络拓扑的示例的预测质量。
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.
