To me, the difference you describe, between the (new) 538 and the Silver models, is not so much a difference between Bayesian and frequentist modeling as it is a difference between causal and non-causal modeling. The poll result in Iowa ripples through the 538 model because it is being modeled as an effect of the views of certain kinds of voters. Because of that, it functions as an indirect observation of those views, and because those kinds of voters live in other states too, a shift in our estimate of those voters views creates a shift in our expectations about how the vote will turn out in those other states, which in turn affects the estimates of the candidates' win probabilities. The important thing is, it's the causal assumptions in the model, not the Bayesian framework, that creates this behavior.
Now, to be fair, most causal models are Bayesian because the idea of reasoning about latent variables from their observable effects fits more naturally into a Bayesian framework than it does into a frequentist framework. The converse, however, is not true. Making a model Bayesian doesn't automatically make it causal, and a non-causal Bayesian model would perform just as badly in this instance as a non-causal frequentist model.
75% of that Iowa poll was conducted before the debate - deducing that 538 (using that poll) reacted quicker to the debate is incorrect.
To me, the difference you describe, between the (new) 538 and the Silver models, is not so much a difference between Bayesian and frequentist modeling as it is a difference between causal and non-causal modeling. The poll result in Iowa ripples through the 538 model because it is being modeled as an effect of the views of certain kinds of voters. Because of that, it functions as an indirect observation of those views, and because those kinds of voters live in other states too, a shift in our estimate of those voters views creates a shift in our expectations about how the vote will turn out in those other states, which in turn affects the estimates of the candidates' win probabilities. The important thing is, it's the causal assumptions in the model, not the Bayesian framework, that creates this behavior.
Now, to be fair, most causal models are Bayesian because the idea of reasoning about latent variables from their observable effects fits more naturally into a Bayesian framework than it does into a frequentist framework. The converse, however, is not true. Making a model Bayesian doesn't automatically make it causal, and a non-causal Bayesian model would perform just as badly in this instance as a non-causal frequentist model.