With the recent US presidential election, there seems to be a plethora of election "modelers" (e.g. 538). Given their "accuracy" of predicting the election outcome, I have come up with a couple of criticisms of what I perceive to be deficiencies in election modeling.
To have a valid computational model (e.g. in the physical sciences), one must do extensive validation. This means comparing your model to known physical cases with known outcomes. Without validation, computational models are irrelevant because there is no measure of either their accuracy or precision. Since demographics change and elections are held only once, this seems particularly challenging to election modeling.
The election is held once with a binary outcome. There is no way to measure the outcome of the election more than once. It is unclear how one can attach a "chance of winning" to a single measurement of the state of the system and interpreting what relevance that even has.
QUESTION : Given the lack of validation, how is modeling elections (like 538 does) a valid approach? Likewise given that elections are held only once, how is the percent "chance of winning" supposed to be interpreted?