We study the design of optimal incentives in a two-player dynamic contest. Two players continuously spend costly effort to attain a score lead, which is also affected by noise. The first player to reach a predetermined score difference (finish line) wins a prize. We focus on the choice of the...

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, modelling it as a renewable American option with interacting waiting and stopping...

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, modelling it as a renewable American option with interacting waiting and stopping...

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...

In the social learning model of Banerjee [1] and Bikhchandani, Hirshleifer and Welch [2] individuals take actions sequentially after observing the history of actions taken by the predecessors and an informative private signal. If the state of the world is changing stochastically over time during...

An unresolved problem in Bayesian decision theory is how to value and price information. This paper resolves both problems assuming inexpensive information. Building on Large Deviation Theory, we produce a generically complete asymptotic order on samples of i.i.d. signals in finite-state,...