  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.
