在转换模型中消除错误
Disappearing errors in a conversion model
摘要：
同样的基本微分方程模型已被改编为人口的成员之间的不同状态的时间依赖的转换。转换模型已被应用于不同的背景下，如流行病感染，巴斯模型的创新扩散，和舆论的ideodynamic模型。例如，该模型的思想动力学版本预测了公众舆论的变化，以回应追溯到不确定过去的有说服力的信息。所有的信息都是有误差的，本章讨论信息测量中的误差如何随着时间的推移而消失，从而使预测的意见值逐渐不受过去测量误差的影响。预测的不确定性进行了讨论，使用正式统计，敏感性分析和自助方差计算。本章介绍了对丰田汽车制造商的意见时间序列的思想动力学预测，这些预测是根据两年半来每天的Twitter分数计算出来的。在这段时间里，丰田突然受到了一系列坏消息的冲击，该模型可以准确地预测随之而来的对丰田的好感度下降和对丰田的负面评价上升。
英文摘要：
The same basic differential equation model has been adapted for time-dependent conversions of members of a population among different states. The conversion model has been applied in different contexts such as epidemiological infections, the Bass model for the diffusion of innovations, and the ideodynamic model for public opinion. For example, the ideodynamic version of the model predicts changes in public opinions in response to persuasive messages extending back to an indefinite past. All messages are measured with error, and this chapter discusses how errors in message measurements disappear with time so that predicted opinion values gradually become unaffected by past measurement errors. Prediction uncertainty is discussed using formal statistics, sensitivity analysis and bootstrap variance calculations. This chapter presents ideodynamic predictions for opinion time series about the Toyota car manufacturer calculated from daily Twitter scores over two and half years. During this time, there was a sudden onslaught of bad news for Toyota, and the model could accurately predict the accompanying drop in favorable Toyota opinion and rise in unfavorable opinion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
