The timing of elections is flexible in many countries. We study this optimization, by first creating a Bayesian learning model of a mean-reverting political support process. We then explore optimal electoral timing, modeling it as a renewable American option with interacting waiting and stopping...

This paper produces a comprehensive theory of the value of Bayesian information and its static demand. Our key insight is to assume 'natural units' corresponding to the sample size of conditionally i.i.d. signals - focusing on the smooth nearby model of the precision of an observation of a...